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Dick van Dijk

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. van Dijk, D.J.C. & Terasvirta, T. & Franses, Ph.H.B.F., 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Research Papers EI 2000-23/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Mentioned in:

    1. Guest Contribution: "An assessment of the US jobless recovery through a non-linear Okun’s law"
      by Menzie Chinn in Econbrowser on 2012-12-18 04:05:06

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Dick van Dijk & Dennis Fok & Philip Hans Franses, 2005. "A multi-level panel STAR model for US manufacturing sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 811-827.

    Mentioned in:

    1. A multi-level panel STAR model for US manufacturing sectors (Journal of Applied Econometrics 2005) in ReplicationWiki ()
  2. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.

    Mentioned in:

    1. Testing for ARCH in the presence of additive outliers (Journal of Applied Econometrics 1999) in ReplicationWiki ()

Working papers

  1. Sander Barendse & Erik Kole & Dick van Dijk, 2019. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Tinbergen Institute Discussion Papers 19-058/III, Tinbergen Institute.

    Cited by:

    1. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    2. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

  2. Anne Opschoor & André Lucas & Istvan Barra & Dick van Dijk, 2019. "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings," Tinbergen Institute Discussion Papers 19-013/IV, Tinbergen Institute, revised 23 Oct 2019.

    Cited by:

    1. Peter Reinhard Hansen & Chen Tong, 2024. "Convolution-t Distributions," Papers 2404.00864, arXiv.org.
    2. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
    3. Alanya-Beltran Willy, 2023. "Modelling volatility dependence with score copula models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(5), pages 649-668, December.
    4. Lin Deng & Michael Stanley Smith & Worapree Maneesoonthorn, 2023. "Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns," Papers 2308.05564, arXiv.org, revised Mar 2024.
    5. Chen Tong & Peter Reinhard Hansen, 2023. "Characterizing Correlation Matrices that Admit a Clustered Factor Representation," Papers 2308.05895, arXiv.org.

  3. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2015. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Tinbergen Institute Discussion Papers 15-140/III, Tinbergen Institute, revised 19 Apr 2017.

    Cited by:

    1. Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges, 2018. "Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors?," Working Papers 18-4, HEC Montreal, Canada Research Chair in Risk Management, revised 25 Jun 2021.
    2. Fijorek, Kamil & Jurkowska, Aleksandra & Jonek-Kowalska, Izabela, 2021. "Financial contagion between the financial and the mining industries – Empirical evidence based on the symmetric and asymmetric CoVaR approach," Resources Policy, Elsevier, vol. 70(C).
    3. Sander Barendse & Erik Kole & Dick van Dijk, 2023. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 528-568.
    4. Sander Barendse & Andrew J. Patton, 2020. "Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter," Economics Series Working Papers 909, University of Oxford, Department of Economics.
    5. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
    6. Conrad, Christian & Stuermer, Karin, 2017. "On the economic determinants of optimal stock-bond portfolios: international evidence," Working Papers 0636, University of Heidelberg, Department of Economics.
    7. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

  4. Sait R. Ozturk & Michel van der Wel & Dick van Dijk, 2015. "Why do Pit-Hours outlive the Pit?," Tinbergen Institute Discussion Papers 15-082/III, Tinbergen Institute.

    Cited by:

    1. Gousgounis, Eleni & Onur, Esen, 2018. "The effect of pit closure on futures trading," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 69-90.

  5. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.

    Cited by:

    1. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    2. P Gorgi & P R Hansen & P Janus & S J Koopman, 2019. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 1-32.
    3. Andre Lucas & Anne Opschoor & Julia Schaumburg, 2016. "Accounting for Missing Values in Score-Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers 16-067/IV, Tinbergen Institute.

  6. Dick van Dijk & Robin L. Lumsdaine & Michel van der Wel, 2014. "Market Set-Up in Advance of Federal Reserve Policy Decisions," NBER Working Papers 19814, National Bureau of Economic Research, Inc.

    Cited by:

    1. Dario Caldara & Edward Herbst, 2019. "Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 157-192, January.
    2. Tim Bollerslev & Jia Li & Yuan Xue, 2016. "Volume, Volatility and Public News Announcements," CREATES Research Papers 2016-19, Department of Economics and Business Economics, Aarhus University.

  7. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    3. Marchetti, Isabella & Rego, Erik Eduardo, 2022. "The impact of hourly pricing for renewable generation projects in Brazil," Renewable Energy, Elsevier, vol. 189(C), pages 601-617.
    4. Marie Bessec & Julien Fouquau, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," Post-Print hal-01644930, HAL.
    5. Sinha, Pankaj & Mathur, Kritika, 2016. "Empirical Analysis of Developments in the Day Ahead Electricity Markets in India," MPRA Paper 72969, University Library of Munich, Germany.
    6. Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, vol. 9(12), pages 1-19, December.
    7. Jun Maekawa & Koji Shimada, 2019. "A Speculative Trading Model for the Electricity Market: Based on Japan Electric Power Exchange," Energies, MDPI, vol. 12(15), pages 1-15, July.
    8. Pavel Atănăsoae, 2018. "The Operating Strategies of Small-Scale Combined Heat and Power Plants in Liberalized Power Markets," Energies, MDPI, vol. 11(11), pages 1-16, November.
    9. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    10. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    11. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    12. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    13. Zorana Božić & Dušan Dobromirov & Jovana Arsić & Mladen Radišić & Beata Ślusarczyk, 2020. "Power Exchange Prices: Comparison of Volatility in European Markets," Energies, MDPI, vol. 13(21), pages 1-15, October.
    14. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    15. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    16. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    17. Tomasz Rokicki & Piotr Bórawski & Barbara Gradziuk & Piotr Gradziuk & Aldona Mrówczyńska-Kamińska & Joanna Kozak & Danuta Jolanta Guzal-Dec & Kamil Wojtczuk, 2021. "Differentiation and Changes of Household Electricity Prices in EU Countries," Energies, MDPI, vol. 14(21), pages 1-21, October.
    18. Jakub Nowotarski & Bidong Liu & Rafal Weron & Tao Hong, 2015. "Improving short term load forecast accuracy via combining sister forecasts," HSC Research Reports HSC/15/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    19. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    20. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    21. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
    22. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    23. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
    24. Niko Hauzenberger & Michael Pfarrhofer & Luca Rossini, 2020. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," Papers 2011.04577, arXiv.org, revised Apr 2023.
    25. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    26. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
    27. Ergemen, Yunus Emre & Haldrup, Niels & Rodríguez-Caballero, Carlos Vladimir, 2016. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," Energy Economics, Elsevier, vol. 60(C), pages 79-96.
    28. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    29. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    30. Halužan, Marko & Verbič, Miroslav & Zorić, Jelena, 2020. "Performance of alternative electricity price forecasting methods: Findings from the Greek and Hungarian power exchanges," Applied Energy, Elsevier, vol. 277(C).
    31. Sayar Karmakar & Marek Chudý & Wei Biao Wu, 2022. "Long‐term prediction intervals with many covariates," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 587-609, July.
    32. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
    33. Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020. "Long-term prediction intervals with many covariates," Papers 2012.08223, arXiv.org, revised Sep 2021.
    34. Jakub Nowotarski & Eran Raviv & Stefan Trueck & Rafal Weron, 2013. "An empirical comparison of alternate schemes for combining electricity spot price forecasts," HSC Research Reports HSC/13/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    35. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.
    36. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
    37. Alonso Fernández, Andrés Modesto & Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    38. Ernstsen, Rune Ramsdal & Boomsma, Trine Krogh & Tegnér, Martin & Skajaa, Anders, 2017. "Hedging local volume risk using forward markets: Nordic case," Energy Economics, Elsevier, vol. 68(C), pages 490-514.
    39. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," CQE Working Papers 6117, Center for Quantitative Economics (CQE), University of Muenster.
    40. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    41. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, vol. 9(8), pages 1-21, July.
    42. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    43. Michail I. Seitaridis & Nikolaos S. Thomaidis & Pandelis N. Biskas, 2021. "Fundamental Responsiveness in European Electricity Prices," Energies, MDPI, vol. 14(22), pages 1-14, November.
    44. Hendrik Butemann & Katja Schimmelpfeng, 2020. "Long-term electricity production planning of a flexible biogas plant considering wear and tear," Journal of Business Economics, Springer, vol. 90(9), pages 1289-1313, November.
    45. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    46. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    47. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    48. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    49. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
    50. Miguel Pinhão & Miguel Fonseca & Ricardo Covas, 2022. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve," Mathematics, MDPI, vol. 10(12), pages 1-20, June.
    51. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    52. Hryshchuk, Antanina & Lessmann, Stefan, 2018. "Deregulated day-ahead electricity markets in Southeast Europe: Price forecasting and comparative structural analysis," IRTG 1792 Discussion Papers 2018-009, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    53. Jakub Nowotarski & Rafal Weron, 2014. "Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices," HSC Research Reports HSC/14/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    54. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    55. Krystyna Gomółka & Piotr Kasprzak, 2022. "Household Ability of Expenditures on Electricity and Energy Resources in the Countries That Joined the EU after 2004," Energies, MDPI, vol. 15(9), pages 1-21, April.
    56. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    57. Luigi Grossi & Fany Nan, 2018. "The influence of renewables on electricity price forecasting: a robust approach," Working Papers 2018/10, Institut d'Economia de Barcelona (IEB).
    58. Sirin, Selahattin Murat & Yilmaz, Berna N., 2020. "Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect," Energy Policy, Elsevier, vol. 144(C).
    59. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    60. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    61. Antonio Bello & Derek Bunn & Javier Reneses & Antonio Muñoz, 2016. "Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices," Energies, MDPI, vol. 9(11), pages 1-15, November.
    62. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting," Applied Energy, Elsevier, vol. 198(C), pages 203-222.
    63. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    64. Mohammad Nure Alam, 2021. "Accessing the Effect of Renewables on the Wholesale Power Market," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 341-360.

  8. Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2013. "Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression," CREATES Research Papers 2013-16, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Post-Print hal-02055155, HAL.
    2. Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies," CEIS Research Paper 255, Tor Vergata University, CEIS, revised 08 Nov 2012.
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    4. A. Frenkel’ A. & N. Volkova N. & A. Surkov A. & E. Romanyuk I. & А. Френкель А. & Н. Волкова Н. & А. Сурков А. & Э. Романюк И., 2018. "Использование Методов Гребневой Регрессии При Объединении Прогнозов // The Application Of Ridge Regression Methods When Combining Forecasts," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(4), pages 6-17.
    5. Oslandsbotn, Andreas & Kereta, Željko & Naumova, Valeriya & Freund, Yoav & Cloninger, Alexander, 2022. "StreaMRAK a streaming multi-resolution adaptive kernel algorithm," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    6. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    7. Wei, Yu & Liang, Chao & Li, Yan & Zhang, Xunhui & Wei, Guiwu, 2020. "Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models," Finance Research Letters, Elsevier, vol. 35(C).
    8. Peter Exterkate, 2012. "Model Selection in Kernel Ridge Regression," CREATES Research Papers 2012-10, Department of Economics and Business Economics, Aarhus University.
    9. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    10. Tian Han & Ying Wang & Xiao Wang & Kang Chen & Huaiwu Peng & Zhenxin Gao & Lanxin Cui & Wentong Sun & Qinke Peng, 2023. "Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    11. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    12. Daiki Maki & Yasushi Ota, 2019. "Robust tests for ARCH in the presence of the misspecified conditional mean: A comparison of nonparametric approches," Papers 1907.12752, arXiv.org, revised Sep 2019.
    13. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    14. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
    15. Milan Fičura, 2019. "Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks," FFA Working Papers 1.001, Prague University of Economics and Business, revised 24 Nov 2019.
    16. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
    17. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.
    18. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    19. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    20. Peter Exterkate, 2011. "Modelling Issues in Kernel Ridge Regression," Tinbergen Institute Discussion Papers 11-138/4, Tinbergen Institute.
    21. Wojciech Victor Fulmyk, 2023. "Nonlinear Granger Causality using Kernel Ridge Regression," Papers 2309.05107, arXiv.org.

  9. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2013. "Predicting Covariance Matrices with Financial Conditions Indexes," Tinbergen Institute Discussion Papers 13-113/III, Tinbergen Institute.

    Cited by:

    1. Manamani SAHOO, 2017. "Financial conditions index (FCI), inflation and growth: Some evidence," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(612), A), pages 147-172, Autumn.

  10. Kole, H.J.W.G. & van Dijk, D.J.C., 2013. "How to Identify and Forecast Bull and Bear Markets?," ERIM Report Series Research in Management ERS-2013-016-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Felix Haase & Matthias Neuenkirch, 2020. "Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US," Research Papers in Economics 2020-01, University of Trier, Department of Economics.
    2. Linh Nguyen & Vilém Novák & Soheyla Mirshahi, 2020. "Trend‐cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 111-124, July.
    3. Kirby, Chris, 2023. "A closer look at the regime-switching evidence of bull and bear markets," Finance Research Letters, Elsevier, vol. 52(C).
    4. Liu, Jia & Maheu, John M & Song, Yong, 2023. "Identification and Forecasting of Bull and Bear Markets using Multivariate Returns," MPRA Paper 119515, University Library of Munich, Germany.
    5. Nicholas Apergis & Vasilios Plakandaras & Ioannis Pragidis, 2022. "Industry momentum and reversals in stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3093-3138, July.
    6. Asif, Raheel & Frömmel, Michael & Mende, Alexander, 2022. "The crisis alpha of managed futures: Myth or reality?," International Review of Financial Analysis, Elsevier, vol. 80(C).
    7. Fernando Henrique De Paula E Silva Mendes & Guilherme Valle Mour, 2014. "Evidências De Bull E Bear Market No Índice Bovespa: Uma Aplicação De Modelos De Regime Markoviano E Duration Dependence," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 138, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    8. Paulo M.M. Rodrigues & João Cruz, 2018. "Structural Changes in the Duration of Bull Markets and Business Cycle Dynamics," Working Papers w201814, Banco de Portugal, Economics and Research Department.
    9. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    10. Mahadeo, Scott M.R. & Heinlein, Reinhold & Legrenzi, Gabriella D., 2019. "Energy contagion analysis: A new perspective with application to a small petroleum economy," Energy Economics, Elsevier, vol. 80(C), pages 890-903.
    11. Bejaoui, Azza & Karaa, Adel, 2016. "Revisiting the bull and bear markets notions in the Tunisian stock market: New evidence from multi-state duration-dependence Markov-switching models," Economic Modelling, Elsevier, vol. 59(C), pages 529-545.
    12. Stephen Thiele, 2020. "Modeling the conditional distribution of financial returns with asymmetric tails," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 46-60, January.
    13. Zegadło, Piotr, 2022. "Identifying bull and bear market regimes with a robust rule-based method," Research in International Business and Finance, Elsevier, vol. 60(C).
    14. Mendes, Fernando Henrique de Paula e Silva & Caldeira, João Frois & Moura, Guilherme Valle, 2018. "Evidence of Bull and Bear Markets in the Bovespa index: An application of Markovian regime-switching Models with Duration Dependence," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(1), May.
    15. Mahadeo, Scott M.R. & Heinlein, Reinhold & Legrenzi, Gabriella D., 2022. "Contagion testing in frontier markets under alternative stressful S&P 500 market scenarios," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    16. Damir Tokic & Dave Jackson, 2023. "When a correction turns into a bear market: What explains the depth of the stock market drawdown? A discretionary global macro approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(3), pages 184-197, May.
    17. Goodarzi, Milad & Meinerding, Christoph, 2023. "Asset allocation with recursive parameter updating and macroeconomic regime identifiers," Discussion Papers 06/2023, Deutsche Bundesbank.
    18. Borjigin, Sumuya & Yang, Yating & Yang, Xiaoguang & Sun, Leilei, 2018. "Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 107-115.
    19. Hanna, Alan J., 2018. "A top-down approach to identifying bull and bear market states," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 93-110.

  11. Anne Opschoor & Michel van der Wel & Dick van Dijk & Nick Taylor, 2012. "On the Effects of Private Information on Volatility," CREATES Research Papers 2012-08, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Heejoon Han & Dennis Kristensen, 2012. "Asymptotic Theory for the QMLE in GARCH-X Models with Stationary and Non-Stationary Covariates," CREATES Research Papers 2012-25, Department of Economics and Business Economics, Aarhus University.

  12. Bannouh, K. & Martens, M.P.E. & van Dijk, D.J.C., 2012. "Forecasting Volatility with the Realized Range in the Presence of Noise and Non-Trading," ERIM Report Series Research in Management ERS-2012-018-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Liu, Qiang & Liu, Yiqi & Liu, Zhi & Wang, Li, 2018. "Estimation of spot volatility with superposed noisy data," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 62-79.
    2. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    3. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    4. Kearney, Fearghal & Murphy, Finbarr & Cummins, Mark, 2015. "An analysis of implied volatility jump dynamics: Novel functional data representation in crude oil markets," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 199-216.
    5. Chia-Lin Chang & David E Allen & Michael McAleer, 2013. "Recent Developments in Financial Economics and Econometrics:An Overview," KIER Working Papers 842, Kyoto University, Institute of Economic Research.
    6. Vortelinos, Dimitrios I. & Lakshmi, Geeta, 2015. "Market risk of BRIC Eurobonds in the financial crisis period," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 295-310.
    7. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.

  13. Martin L. Scholtus & Dick van Dijk & Bart Frijns, 2012. "Speed, Algorithmic Trading, and Market Quality around Macroeconomic News Announcements," Tinbergen Institute Discussion Papers 12-121/III, Tinbergen Institute.

    Cited by:

    1. López, Raquel & Esparcia, Carlos, 2021. "Analysis of the performance of volatility-based trading strategies on scheduled news announcement days: An international equity market perspective," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 32-54.
    2. Hatch, Brian C. & Johnson, Shane A. & Wang, Qin Emma & Zhang, Jun, 2021. "Algorithmic trading and firm value," Journal of Banking & Finance, Elsevier, vol. 125(C).
    3. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    4. Arumugam, Devika & Prasanna, P. Krishna & Marathe, Rahul R., 2023. "Do algorithmic traders exploit volatility?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    5. Kurov, Alexander & Sancetta, Alessio & Strasser, Georg & Wolfe, Marketa Halova, 2019. "Price Drift Before U.S. Macroeconomic News: Private Information about Public Announcements?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(1), pages 449-479, February.
    6. Mila Getmansky & Ravi Jagannathan & Loriana Pelizzon & Ernst Schaumburg & Darya Yuferova, 2017. "Stock Price Crashes: Role of Slow-Moving Capital," NBER Working Papers 24098, National Bureau of Economic Research, Inc.
    7. Banerjee, Ameet Kumar & Pradhan, H.K., 2022. "Intraday analysis of macroeconomic news surprises, and asymmetries in Indian benchmark bond," Finance Research Letters, Elsevier, vol. 45(C).
    8. Rosa, Carlo, 2016. "Walking on thin ice: Market quality around FOMC announcements," Economics Letters, Elsevier, vol. 138(C), pages 5-8.
    9. Gu, Chen & Chen, Denghui & Stan, Raluca & Shen, Aizhong, 2022. "It is not just What you say, but How you say it: Why tonality matters in central bank communication," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 216-231.
    10. Yamada, Masahiro & Ito, Takatoshi, 2022. "Price discovery and liquidity recovery: Forex market reactions to macro announcements," Journal of International Money and Finance, Elsevier, vol. 120(C).
    11. George Jiang & Ingrid Lo & Giorgio Valente, 2014. "High-Frequency Trading around Macroeconomic News Announcements: Evidence from the U.S. Treasury Market," Staff Working Papers 14-56, Bank of Canada.
    12. Masahiro Yamada & Takatoshi Ito, 2020. "Price Discovery and Liquidity Recovery: Forex Market Reactions to Macro Announcements," NBER Working Papers 27036, National Bureau of Economic Research, Inc.
    13. Frino, Alex & Mollica, Vito & Webb, Robert I. & Zhang, Shunquan, 2017. "The impact of latency sensitive trading on high frequency arbitrage opportunities," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 91-102.
    14. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    15. Kurov, Alexander & Stan, Raluca, 2018. "Monetary policy uncertainty and the market reaction to macroeconomic news," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 127-142.
    16. Xu, Yanyan & Huang, Dengshi & Ma, Feng & Qiao, Gaoxiu, 2019. "The heterogeneous impact of liquidity on volatility in Chinese stock index futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 73-85.
    17. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    18. Aitken, Michael & Cumming, Douglas & Zhan, Feng, 2013. "High frequency trading and end-of-day price dislocation," CFS Working Paper Series 2013/16, Center for Financial Studies (CFS).
    19. Cartea, Álvaro & Payne, Richard & Penalva, José & Tapia, Mikel, 2019. "Ultra-fast activity and intraday market quality," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 157-181.
    20. Alex Frino & Ognjen Kovacevic & Vito Mollica & Robert I. Webb, 2022. "Connectivity costs and price efficiency: An event study," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(2), pages 296-309, February.
    21. Daniel Martin Katz & Michael J Bommarito II & Tyler Soellinger & James Ming Chen, 2015. "Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making," Papers 1508.05751, arXiv.org, revised May 2017.
    22. Lyudmila A. Glik & Oleg L. Kritski, 2014. "Detecting informed activities in European-style option tradings," Papers 1403.3294, arXiv.org.
    23. Tim A. Herberger & Matthias Horn & Andreas Oehler, 2020. "Are intraday reversal and momentum trading strategies feasible? An analysis for German blue chip stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 179-197, June.
    24. Lyudmila A. Glik & Oleg L. Kritski, 2014. "Finding informed traders in futures and their inderlying assets in intraday trading," Papers 1402.6583, arXiv.org.
    25. Haugom, Erik & Ray, Rina, 2017. "Heterogeneous traders, liquidity, and volatility in crude oil futures market," Journal of Commodity Markets, Elsevier, vol. 5(C), pages 36-49.
    26. Indriawan, Ivan & Jiao, Feng & Tse, Yiuman, 2021. "The FOMC announcement returns on long-term US and German bond futures," Journal of Banking & Finance, Elsevier, vol. 123(C).
    27. Kurov, Alexander & Sancetta, Alessio & Wolfe, Marketa Halova, 2022. "Drift Begone! Release policies and preannouncement informed trading," Journal of International Money and Finance, Elsevier, vol. 128(C).
    28. Keiichi Goshima & Yusuke Kumano, 2018. "Monetary Policy Announcement and Algorithmic News Trading in the Foreign Exchange Market," IMES Discussion Paper Series 18-E-13, Institute for Monetary and Economic Studies, Bank of Japan.
    29. Qixuan Luo & Shijia Song & Handong Li, 2023. "Research on the Effects of Liquidation Strategies in the Multi-asset Artificial Market," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1721-1750, December.
    30. Khairul Zharif Zaharudin & Martin R. Young & Wei‐Huei Hsu, 2022. "High‐frequency trading: Definition, implications, and controversies," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 75-107, February.
    31. Frijns, Bart & Indriawan, Ivan & Tourani-Rad, Alireza, 2015. "Macroeconomic news announcements and price discovery: Evidence from Canadian–U.S. cross-listed firms," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 35-48.
    32. Jurich, Stephen N. & Mishra, Ajay Kumar & Parikh, Bhavik, 2020. "Indecisive algos: Do limit order revisions increase market load?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    33. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    34. Fernandez-Perez, Adrian & Frijns, Bart & Tourani-Rad, Alireza, 2017. "When no news is good news – The decrease in investor fear after the FOMC announcement," Journal of Empirical Finance, Elsevier, vol. 41(C), pages 187-199.
    35. López, Raquel, 2018. "The behaviour of energy-related volatility indices around scheduled news announcements: Implications for variance swap investments," Energy Economics, Elsevier, vol. 72(C), pages 356-364.
    36. Kaplanski, Guy & Levy, Haim, 2015. "Trading breaks and asymmetric information: The option markets," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 390-404.
    37. Serbera, Jean-Philippe & Paumard, Pascal, 2016. "The fall of high-frequency trading: A survey of competition and profits," Research in International Business and Finance, Elsevier, vol. 36(C), pages 271-287.
    38. Chang, Ya-Ting & Gau, Yin-Feng & Hsu, Chih-Chiang, 2017. "Liquidity Commonality in Foreign Exchange Markets During the Global Financial Crisis and the Sovereign Debt Crisis: Effects of Macroeconomic and Quantitative Easing Announcements," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 172-192.
    39. Bertone, Stephen & Paeglis, Imants & Ravi, Rahul, 2015. "(How) has the market become more efficient?," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 72-86.
    40. Alex Frino & Michael Garcia & Zeyang Zhou, 2020. "Impact of algorithmic trading on speed of adjustment to new information: Evidence from interest rate derivatives," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 749-760, May.
    41. Ikizlerli, Deniz & Holmes, Phil & Anderson, Keith, 2019. "The response of different investor types to macroeconomic news," Journal of Multinational Financial Management, Elsevier, vol. 50(C), pages 13-28.
    42. Kam F. Chan & Philip Gray, 2018. "Volatility jumps and macroeconomic news announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(8), pages 881-897, August.

  14. Cem Cakmakli & Richard Paap & Dick van Dijk, 2012. "Measuring and Predicting Heterogeneous Recessions," Koç University-TUSIAD Economic Research Forum Working Papers 1206, Koc University-TUSIAD Economic Research Forum.

    Cited by:

    1. Pierre Guérin & Danilo Leiva-Leon, 2015. "Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data," Staff Working Papers 15-24, Bank of Canada.
    2. Candelon, Bertrand & Metiu, Norbert & Straetmans, Stefan, 2013. "Disentangling economic recessions and depressions," Discussion Papers 43/2013, Deutsche Bundesbank.
    3. Cem Çakmakli & Hamza Dem I˙rcani & Sumru Altug, 2021. "Modelling of Economic and Financial Conditions for Real‐Time Prediction of Recessions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 663-685, June.

  15. Dick van Dijk & Siem Jan Koopman & Michel van der Wel & Jonathan H. Wright, 2012. "Forecasting Interest Rates with Shifting Endpoints," Tinbergen Institute Discussion Papers 12-076/4, Tinbergen Institute.

    Cited by:

    1. Giacomini, Raffaella & Ragusa, Giuseppe & Altavilla, Carlo, 2013. "Anchoring the Yield Curve Using Survey Expectations," CEPR Discussion Papers 9738, C.E.P.R. Discussion Papers.
    2. Alberto Caruso & Laura Coroneo, 2023. "Does Real‐Time Macroeconomic Information Help to Predict Interest Rates?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2027-2059, December.
    3. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    4. Christoph Berninger & Almond Stöcker & David Rügamer, 2022. "A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 181-200, January.
    5. Speck, Christian, 2023. "Pricing the Bund term structure with linear regressions – without an observable short rate," Discussion Papers 08/2023, Deutsche Bundesbank.
    6. Joseph P. Byrne & Shuo Cao. & Dimitris Korobilis., 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," Working Papers 2015_08, Business School - Economics, University of Glasgow.
    7. Geiger, Felix & Schupp, Fabian, 2018. "With a little help from my friends: Survey-based derivation of euro area short rate expectations at the effective lower bound," Discussion Papers 27/2018, Deutsche Bundesbank.
    8. Hitesh Doshi & Kris Jacobs & Rui Liu, 2021. "Information in the Term Structure: A Forecasting Perspective," Management Science, INFORMS, vol. 67(8), pages 5255-5277, August.
    9. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    10. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    11. Doshi, Hitesh & Jacobs, Kris & Liu, Rui, 2018. "Macroeconomic determinants of the term structure: Long-run and short-run dynamics," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 99-122.
    12. Jiazi Chen & Zhiwu Hong & Linlin Niu, 2022. "Forecasting Interest Rates with Shifting Endpoints: The Role of the Demographic Age Structure," Working Papers 2022-06-25, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    13. Malik, Sheheryar & Meldrum, Andrew, 2016. "Evaluating the robustness of UK term structure decompositions using linear regression methods," Journal of Banking & Finance, Elsevier, vol. 67(C), pages 85-102.
    14. Fausto Vieira & Fernando Chague & Marcelo Fernandes, 2016. "Forecasting the Brazilian Yield Curve Using Forward-Looking Variables," Working Papers 799, Queen Mary University of London, School of Economics and Finance.
    15. Mr. Ralph Chami & Mr. Thomas F. Cosimano & Jun Ma & Ms. Celine Rochon, 2017. "What’s Different about Bank Holding Companies?," IMF Working Papers 2017/026, International Monetary Fund.
    16. Gaus, Eric & Sinha, Arunima, 2018. "What does the yield curve imply about investor expectations?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 248-265.
    17. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    18. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
    19. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    20. Daniel R. Kowal & Antonio Canale, 2021. "Semiparametric Functional Factor Models with Bayesian Rank Selection," Papers 2108.02151, arXiv.org, revised May 2022.
    21. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    22. Bruno Feunou & Jean-Sébastien Fontaine, 2021. "Debt-Secular Economic Changes and Bond Yields," Staff Working Papers 21-14, Bank of Canada.
    23. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    24. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  16. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    2. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2013. "Risks of large portfolios," MPRA Paper 44206, University Library of Munich, Germany.
    3. Bollerslev, Tim & Meddahi, Nour & Nyawa, Serge, 2019. "High-dimensional multivariate realized volatility estimation," Journal of Econometrics, Elsevier, vol. 212(1), pages 116-136.

  17. Martin Scholtus & Dick van Dijk, 2012. "High-Frequency Technical Trading: The Importance of Speed," Tinbergen Institute Discussion Papers 12-018/4, Tinbergen Institute.

    Cited by:

    1. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.
    2. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Mid-price prediction based on machine learning methods with technical and quantitative indicators," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-39, June.
    3. Giancarlo Salirrosas Mart'inez, 2016. "Biased Roulette Wheel: A Quantitative Trading Strategy Approach," Papers 1609.09601, arXiv.org.
    4. Martin L. Scholtus & Dick van Dijk & Bart Frijns, 2012. "Speed, Algorithmic Trading, and Market Quality around Macroeconomic News Announcements," Tinbergen Institute Discussion Papers 12-121/III, Tinbergen Institute.

  18. Sjoerd van den Hauwe & Richard Paap & Dick J.C. van Dijk, 2011. "An Alternative Bayesian Approach to Structural Breaks in Time Series Models," Tinbergen Institute Discussion Papers 11-023/4, Tinbergen Institute.

    Cited by:

    1. Jiawen Xu & Pierre Perron, 2017. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series WP2018-014, Boston University - Department of Economics, revised Nov 2018.

  19. Cem Cakmakli & Richard Paap & Dick J.C. van Dijk, 2011. "Modeling and Estimation of Synchronization in Multistate Markov-Switching Models," Tinbergen Institute Discussion Papers 11-002/4, Tinbergen Institute.

    Cited by:

    1. Danilo Leiva-Leon, 2014. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," Staff Working Papers 14-38, Bank of Canada.
    2. Ritika Jaiswal & Rashmi Uchil, 2018. "An Analysis of Gold Futures as an Alternative Asset: Evidence from India," International Journal of Economics and Financial Issues, Econjournals, vol. 8(6), pages 144-150.
    3. Vassilios Babalos & Mehmet Balcilar & Rangan Gupta & Nikolaos Philippas, 2014. "Revisiting Herding Behavior in REITs: A RegimeSwitching Approach," Working Papers 15-15, Eastern Mediterranean University, Department of Economics.
    4. Balcilar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2013. "Investor herds and regime-switching: Evidence from Gulf Arab stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 295-321.
    5. Çakmaklı, Cem & Paap, Richard & van Dijk, Dick, 2013. "Measuring and predicting heterogeneous recessions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2195-2216.
    6. Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
    7. Mehmet Balcilar & Riza Demirer & Shawkat Hammoudeh & Ahmed Khalifa, 2013. "Do Global Shocks Drive Investor Herds in Oil-Rich Frontier Markets?," Working Papers 819, Economic Research Forum, revised Dec 2013.
    8. Nalan Baştürk & Cem Çakmakli & S. Pinar Ceyhan & Herman K. Van Dijk, 2014. "Posterior‐Predictive Evidence On Us Inflation Using Extended New Keynesian Phillips Curve Models With Non‐Filtered Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1164-1182, November.
    9. Carlo Di Giorgio, 2016. "Business Cycle Synchronization of CEECs with the Euro Area: A Regime Switching Approach," Journal of Common Market Studies, Wiley Blackwell, vol. 54(2), pages 284-300, March.
    10. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2015. "Regional and global spillovers and diversification opportunities in the GCC equity sectors," Emerging Markets Review, Elsevier, vol. 24(C), pages 160-187.
    11. Nalan Basturk & Cem Cakmakli & Pinar Ceyhan & Herman K. van Dijk, 2013. "Posterior-Predictive Evidence on US Inflation using Extended Phillips Curve Models with non-filtered Data," Koç University-TUSIAD Economic Research Forum Working Papers 1321, Koc University-TUSIAD Economic Research Forum.
    12. Nalan Basturk & Cem Cakmakli & Pinar Ceyhan & Herman K. van Dijk, 2013. "Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series," Tinbergen Institute Discussion Papers 13-011/III, Tinbergen Institute.

  20. Sjoerd van den Hauwe & Dick van Dijk & Richard Paap, 2011. "Bayesian Forecasting of Federal Funds Target Rate Decisions," Tinbergen Institute Discussion Papers 11-093/4, Tinbergen Institute.

    Cited by:

    1. Seibert, Armin & Sirchenko, Andrei & Müller, Gernot, 2021. "A model for policy interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
    2. Dick van Dijk & Robin L. Lumsdaine & Michel van der Wel, 2014. "Market Set-Up in Advance of Federal Reserve Policy Decisions," NBER Working Papers 19814, National Bureau of Economic Research, Inc.
    3. Andrei Sirchenko, 2019. "A regime-switching model for the federal funds rate target," UvA-Econometrics Working Papers 19-01, Universiteit van Amsterdam, Dept. of Econometrics.
    4. Kim, Hyerim & Kang, Kyu Ho, 2022. "The Bank of Korea watch," Journal of International Money and Finance, Elsevier, vol. 126(C).

  21. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Post-Print hal-00834423, HAL.

    Cited by:

    1. Grigory Franguridi, 2014. "Higher order conditional moment dynamics and forecasting value-at-risk (in Russian)," Quantile, Quantile, issue 12, pages 69-82, February.
    2. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
    3. Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
    4. Luisa Bisaglia & Matteo Grigoletto, 2018. "A new time-varying model for forecasting long-memory series," Papers 1812.07295, arXiv.org.
    5. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    6. David T. Frazier & Gael M. Martin & Ruben Loaiza-Maya, 2022. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Monash Econometrics and Business Statistics Working Papers 1/22, Monash University, Department of Econometrics and Business Statistics.
    7. Chris McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," Reserve Bank of New Zealand Discussion Paper Series DP2016/10, Reserve Bank of New Zealand.
    8. Jensen, Mark J. & Maheu, John M., 2013. "Bayesian semiparametric multivariate GARCH modeling," Journal of Econometrics, Elsevier, vol. 176(1), pages 3-17.
    9. Benjamin Beckers & Helmut Herwartz & Moritz Seidel, 2017. "Risk forecasting in (T)GARCH models with uncorrelated dependent innovations," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 121-137, January.
    10. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    11. Mahsa Gorji & Rasoul Sajjad, 2017. "Improving Value-at-Risk Estimation from the Normal EGARCH Model," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 11(1), March.
    12. Cees Diks & Valentyn Panchenko & Oleg Sokolinskiy, & Dick van Dijk, 2013. "Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support," Tinbergen Institute Discussion Papers 13-061/III, Tinbergen Institute.
    13. Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
    14. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers 1416, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2017.
    15. Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
    16. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    17. Coe, Patrick J & Vahey, Shaun P., 2014. "Probablistic Prediction of the US Great Recession with Historical Expert," EMF Research Papers 06, Economic Modelling and Forecasting Group.
    18. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman & Herman van Dijk, 2019. "Partially Censored Posterior for Robust and Efficient Risk Evaluation," Tinbergen Institute Discussion Papers 19-057/III, Tinbergen Institute.
    19. Fawcett, Nicholas & Kapetanios, George & Mitchell, James & Price, Simon, 2014. "Generalised density forecast combinations," Bank of England working papers 492, Bank of England.
    20. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    21. Mengheng Li & Siem Jan (S.J.) Koopman, 2018. "Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction," Tinbergen Institute Discussion Papers 18-027/III, Tinbergen Institute.
    22. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    23. Diks, Cees & Panchenko, Valentyn & Sokolinskiy, Oleg & van Dijk, Dick, 2014. "Comparing the accuracy of multivariate density forecasts in selected regions of the copula support," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 79-94.
    24. Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021. "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 56-73.
    25. Kim, Dongwhan & Kang, Kyu Ho, 2021. "Conditional value-at-risk forecasts of an optimal foreign currency portfolio," International Journal of Forecasting, Elsevier, vol. 37(2), pages 838-861.
    26. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    27. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    28. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    29. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    30. Carol Alexander & Michael Coulon & Yang Han & Xiaochun Meng, 2021. "Evaluating the Discrimination Ability of Proper Multivariate Scoring Rules," Papers 2101.12693, arXiv.org.
    31. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.
    32. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    33. Lukasz Gatarek & Lennart Hoogerheide & Koen Hooning & Herman K. van Dijk, 2013. "Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation," Tinbergen Institute Discussion Papers 13-060/III, Tinbergen Institute, revised 06 Mar 2014.
    34. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    35. Jonas R. Brehmer & Tilmann Gneiting, 2020. "Properization: constructing proper scoring rules via Bayes acts," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 659-673, June.
    36. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.
    37. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    38. Xiaochun Meng & James W. Taylor & Souhaib Ben Taieb & Siran Li, 2020. "Scores for Multivariate Distributions and Level Sets," Papers 2002.09578, arXiv.org, revised Jun 2023.
    39. Hambuckers, Julien & Heuchenne, Cedric, 2017. "A robust statistical approach to select adequate error distributions for financial returns," LIDAM Reprints ISBA 2017031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    40. Rossi, Barbara & Sekhposyan, Tatevik, 2014. "Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set," International Journal of Forecasting, Elsevier, vol. 30(3), pages 662-682.
    41. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    42. James Mitchell & Martin Weale, 2021. "Censored Density Forecasts: Production and Evaluation," Working Papers 21-12R, Federal Reserve Bank of Cleveland, revised 16 Aug 2022.
    43. Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023. "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers 12/23, Monash University, Department of Econometrics and Business Statistics.
    44. Emilio Zanetti Chini, 2019. "Strategic judgment: its game-theoretic foundations,its econometric elicitation," Working Papers in Public Economics 190, University of Rome La Sapienza, Department of Economics and Law.
    45. Elena Andreou & Andros Kourtellos, 2018. "Scoring rules for simple forecasting models: The case of Cyprus GDP and its sectors," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(1), pages 59-73, June.
    46. Lee, Cheol Woo & Kang, Kyu Ho, 2023. "Estimating and testing skewness in a stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 445-467.
    47. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
    48. Zdeněk Zmeškal & Dana Dluhošová & Karolina Lisztwanová & Antonín Pončík & Iveta Ratmanová, 2023. "Distribution Prediction of Decomposed Relative EVA Measure with Levy-Driven Mean-Reversion Processes: The Case of an Automotive Sector of a Small Open Economy," Forecasting, MDPI, vol. 5(2), pages 1-19, May.
    49. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    50. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    51. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.
    52. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks," Papers 2304.06950, arXiv.org.
    53. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2014. "Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities," Tinbergen Institute Discussion Papers 14-090/III, Tinbergen Institute.
    54. Tobias Fissler & Hajo Holzmann, 2022. "Measurability of functionals and of ideal point forecasts," Papers 2203.08635, arXiv.org.
    55. Delatola, E.-I. & Griffin, J.E., 2013. "A Bayesian semiparametric model for volatility with a leverage effect," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 97-110.
    56. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    57. Diego Chicana & Rafael Nivin, 2021. "Evaluating Growth-at-Risk as a tool for monitoring macro-financial risks in the Peruvian economy," IHEID Working Papers 07-2021, Economics Section, The Graduate Institute of International Studies.
    58. McAdam, Peter & Warne, Anders, 2018. "Euro area real-time density forecasting with financial or labor market frictions," Working Paper Series 2140, European Central Bank.
    59. Kuangyu Wen & Wenbin Wu & Ximing Wu, 2023. "Electricity demand forecasting and risk management using Gaussian process model with error propagation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 957-969, July.
    60. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    61. McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.

  22. Oleg Sokolinskiy & Dick van Dijk, 2011. "Forecasting Volatility with Copula-Based Time Series Models," Tinbergen Institute Discussion Papers 11-125/4, Tinbergen Institute.

    Cited by:

    1. Simard Clarence & Rémillard Bruno, 2015. "Forecasting time series with multivariate copulas," Dependence Modeling, De Gruyter, vol. 3(1), pages 1-24, May.
    2. Jorge V. Pérez-Rodríguez, 2020. "Another look at the implied and realised volatility relation: a copula-based approach," Risk Management, Palgrave Macmillan, vol. 22(1), pages 38-64, March.
    3. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    4. J. A. Carrillo & M. Nieto & J. F. Velez & D. Velez, 2021. "A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions," Forecasting, MDPI, vol. 3(2), pages 1-22, May.
    5. Wenjing Wang & Minjing Tao, 2020. "Forecasting Realized Volatility Matrix With Copula-Based Models," Papers 2002.08849, arXiv.org.
    6. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    7. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
    8. Czado, Claudia & Ivanov, Eugen & Okhrin, Yarema, 2019. "Modelling temporal dependence of realized variances with vines," Econometrics and Statistics, Elsevier, vol. 12(C), pages 198-216.

  23. Michiel de Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," Working Paper 2010/01, Norges Bank.

    Cited by:

    1. Dick Dijk & Siem Jan Koopman & Michel Wel & Jonathan H. Wright, 2014. "Forecasting interest rates with shifting endpoints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 693-712, August.
    2. Elizondo Rocío, 2023. "The Three Intelligible Factors of the Yield Curve in Mexico," Working Papers 2023-13, Banco de México.
    3. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
    4. Andrea Fronzetti Colladon & Stefano Grassi & Francesco Ravazzolo & Francesco Violante, 2021. "Forecasting financial markets with semantic network analysis in the COVID—19 crisis," Working Papers 2021-06, Center for Research in Economics and Statistics.
    5. Andrea Monticini & Francesco Ravazzolo, 2014. "Forecasting the intraday market price of money," DISCE - Working Papers del Dipartimento di Economia e Finanza def010, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    6. Kučera, Adam, 2020. "Identification of triggers of U.S. yield curve movements," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Fausto Vieira & Fernando Chague & Marcelo Fernandes, 2016. "Forecasting the Brazilian Yield Curve Using Forward-Looking Variables," Working Papers 799, Queen Mary University of London, School of Economics and Finance.
    8. Adam Kucera & Evzen Kocenda & Ales Marsal, 2022. "Yield Curve Dynamics and Fiscal Policy Shocks," Working Papers IES 2022/04, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2022.
    9. Elizondo Rocío, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
    10. Wellmann, Dennis & Trück, Stefan, 2018. "Factors of the term structure of sovereign yield spreads," Journal of International Money and Finance, Elsevier, vol. 81(C), pages 56-75.
    11. Fabricio Tourrucôo & João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2016. "Forecasting The Yield Curve With The Arbitrage-Free Dynamic Nelson-Siegel Model: Brazilian Evidence," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    12. Christian Kascha & Carsten Trenkler, 2011. "Cointegrated VARMA models and forecasting US interest rates," ECON - Working Papers 033, Department of Economics - University of Zurich.
    13. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    14. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    15. Gordon H. Dash & Nina Kajiji & Domenic Vonella, 2018. "The role of supervised learning in the decision process to fair trade US municipal debt," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(1), pages 139-168, June.
    16. Rui Chen & Jiri Svec & Maurice Peat, 2016. "Forecasting the Government Bond Term Structure in Australia," Australian Economic Papers, Wiley Blackwell, vol. 55(2), pages 99-111, June.
    17. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    18. Stuart, Rebecca, 2018. "A quarterly Phillips curve for Switzerland using interpolated data, 1963–2016," Economic Modelling, Elsevier, vol. 70(C), pages 78-86.
    19. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  24. Exterkate, P. & van Dijk, D.J.C. & Heij, C. & Groenen, P.J.F., 2010. "Forecasting the Yield Curve in a Data-Rich Environment using the Factor-Augmented Nelson-Siegel Model," Econometric Institute Research Papers EI 2010-06, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    2. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    3. Dick Dijk & Siem Jan Koopman & Michel Wel & Jonathan H. Wright, 2014. "Forecasting interest rates with shifting endpoints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 693-712, August.
    4. Adam Traczyk, 2013. "Financial integration and the term structure of interest rates," Empirical Economics, Springer, vol. 45(3), pages 1267-1305, December.
    5. Michele Manna & Emmanuela Bernardini & Mauro Bufano & Davide Dottori, 2013. "Modelling public debt strategies," Questioni di Economia e Finanza (Occasional Papers) 199, Bank of Italy, Economic Research and International Relations Area.
    6. Fausto Vieira & Fernando Chague, Marcelo Fernandes, 2016. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Working Papers, Department of Economics 2016_31, University of São Paulo (FEA-USP).
    7. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    8. Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & Muhammed Hasan Yilmaz, 2020. "Do Local and Global Factors Impact the Emerging Markets’s Sovereign Yield Curves? Evidence from a Data-Rich Environment," Working Papers 2004, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    9. Scott A. Brave & R. Andrew Butters & David Kelley, 2019. "A New “Big Data” Index of U.S. Economic Activity," Economic Perspectives, Federal Reserve Bank of Chicago, issue 1, pages 1-30.
    10. Fausto Vieira & Fernando Chague & Marcelo Fernandes, 2016. "Forecasting the Brazilian Yield Curve Using Forward-Looking Variables," Working Papers 799, Queen Mary University of London, School of Economics and Finance.
    11. Siem Jan Koopman & Michel van der Wel, 2011. "Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model," Tinbergen Institute Discussion Papers 11-063/4, Tinbergen Institute.
    12. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
    13. Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & Muhammed Hasan Yilmaz, 2018. "The Interaction between Yield Curve and Macroeconomic Factors," CBT Research Notes in Economics 1802, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    14. Caio Almeida & Axel Simonsen & José Valentim Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
    15. Gerhart, Christoph & Lütkebohmert, Eva, 2020. "Empirical analysis and forecasting of multiple yield curves," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 59-78.
    16. Massimo Guidolin & Daniel L. Thornton, 2010. "Predictions of short-term rates and the expectations hypothesis," Working Papers 2010-013, Federal Reserve Bank of St. Louis.
    17. Lorenčič Eva, 2016. "Testing the Performance of Cubic Splines and Nelson-Siegel Model for Estimating the Zero-coupon Yield Curve," Naše gospodarstvo/Our economy, Sciendo, vol. 62(2), pages 42-50, June.
    18. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    19. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    20. Anthony H. Tu & Cathy Yi-Hsuan Chen, 2016. "What Derives the Bond Portfolio Value-at-Risk: Information Roles of Macroeconomic and Financial Stress Factors," SFB 649 Discussion Papers SFB649DP2016-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    21. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    22. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.

  25. Basturk, N. & Paap, R. & van Dijk, D.J.C., 2010. "Financial Development and Convergence Clubs," Econometric Institute Research Papers EI 2010-52, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2021. "Bayes estimates of multimodal density features using DNA and Economic Data," Tinbergen Institute Discussion Papers 21-017/III, Tinbergen Institute.

  26. Schauten, M.B.J. & van Dijk, D.J.C., 2010. "Corporate Governance and the Cost of Debt of Large European Firms," ERIM Report Series Research in Management ERS-2010-025-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Ghouma, Hatem & Ben-Nasr, Hamdi & Yan, Ruiqian, 2018. "Corporate governance and cost of debt financing: Empirical evidence from Canada," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 138-148.
    2. Carlo Bellavite Pellegrini & Raul Caruso & Rocco Cifone, 2019. "The Impact of ESG Scores on both Firm Profitability and Value in the Automotive Sector (2002-2016)," Working Papers 1004, European Centre of Peace Science, Integration and Cooperation (CESPIC), Catholic University 'Our Lady of Good Counsel'.

  27. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.

    Cited by:

    1. Thieu, Le Quyen, 2016. "Variance targeting estimation of the BEKK-X model," MPRA Paper 75572, University Library of Munich, Germany.
    2. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    3. Frédérique Bec & Annabelle de Gaye, 2019. "Le modèle autorégressif autorégressif à seuil avec effet rebond : Une application aux rendements boursiers français et américains ," Working Papers hal-02014663, HAL.
    4. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A Comprehensive Look at Financial Volatility Prediction by Economic Variables," BIS Working Papers 374, Bank for International Settlements.
    5. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Guidolin, Massimo & Ravazzolo, Francesco & Tortora, Andrea Donato, 2013. "Alternative econometric implementations of multi-factor models of the U.S. financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(2), pages 87-111.
    7. Frédérique BEC & Songlin ZENG, 2013. "Do Stock Returns Rebound After Bear Markets? An Empirical Analysis From Five OECD Countries," THEMA Working Papers 2013-21, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    8. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.

  28. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2009. "Cointegration in a historical perspective," Econometric Institute Research Papers EI 2009-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
    2. Fragiskos Archontakis & Rocco Mosconi, 2021. "Søren Johansen and Katarina Juselius: A Bibliometric Analysis of Citations through Multivariate Bass Models," Econometrics, MDPI, vol. 9(3), pages 1-28, August.

  29. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2009. "Structural Breaks in the International Transmission of Inflation," Centre for Growth and Business Cycle Research Discussion Paper Series 119, Economics, The University of Manchester.

    Cited by:

    1. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2009. "Changes in International Business Cycle Affiliations," Economics Discussion Paper Series 0924, Economics, The University of Manchester.
    2. Peter Dolton & Li Lin, 2011. "From Grants to Loans and Fees: The Demand for Post-Compulsory Education in England and Wales from 1955 to 2008," CEE Discussion Papers 0127, Centre for the Economics of Education, LSE.

  30. Markwat, T.D. & Kole, H.J.W.G. & van Dijk, D.J.C., 2009. "Time Variation in Asset Return Dependence: Strength or Structure?," ERIM Report Series Research in Management ERS-2009-052-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Małgorzata Doman & Ryszard Doman, 2013. "Dynamic linkages between stock markets: the effects of crises and globalization," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 12(2), pages 87-112, August.
    2. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    3. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
    4. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    5. Fermanian, Jean-David & Wegkamp, Marten H., 2012. "Time-dependent copulas," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 19-29.
    6. Hans Manner & Olga Reznikova, 2012. "A Survey on Time-Varying Copulas: Specification, Simulations, and Application," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 654-687, November.
    7. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.

  31. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.

    Cited by:

    1. Ardia, David & Lennart, Hoogerheide & Nienke, Corré, 2011. "Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?," MPRA Paper 28259, University Library of Munich, Germany.
    2. Anne Sofie Jore & James Mitchell & Shaun Vahey, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," Reserve Bank of New Zealand Discussion Paper Series DP2008/18, Reserve Bank of New Zealand.
    3. Amisano, Gianni & Geweke, John, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series 969, European Central Bank.
    4. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.

  32. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2008. "Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation," Centre for Growth and Business Cycle Research Discussion Paper Series 109, Economics, The University of Manchester.

    Cited by:

    1. Elena Andreou & Alessandra Pelloni & Marianne Sensier, 2008. "Is Volatility Good for Growth? Evidence from the G7," Centre for Growth and Business Cycle Research Discussion Paper Series 97, Economics, The University of Manchester.
    2. Erdenebat Bataa & Marwan Izzeldin & Denise Osborn, 2015. "Changes in the global oil market," Working Papers 75761696, Lancaster University Management School, Economics Department.
    3. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2009. "Changes in International Business Cycle Affiliations," Economics Discussion Paper Series 0924, Economics, The University of Manchester.
    4. Hailemariam, Abebe & Smyth, Russell, 2019. "What drives volatility in natural gas prices?," Energy Economics, Elsevier, vol. 80(C), pages 731-742.
    5. Bataa, Erdenebat & Wohar, Mark & Vivian, Andrew, 2015. "Changes in the relationship between short-term interest rate, inflation and growth: Evidence from the UK, 1820-2014," MPRA Paper 72422, University Library of Munich, Germany.
    6. Erdenebat Bataa, 2019. "Growth and Inflation Regimes in Greater Tumen Initiative Area," The Northeast Asian Economic Review, ERINA - Economic Research Institute for Northeast Asia, vol. 7(1), pages 15-29, November.
    7. Altansukh, Gantungalag & Becker, Ralf & Bratsiotis, George J. & Osborn, Denise R., 2017. "What is the Globalisation of Inflation?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74, pages 1-27.
    8. Steffen Henzel & Elisabeth Wieland, 2013. "Synchronization and Changes in International Inflation Uncertainty," CESifo Working Paper Series 4194, CESifo.
    9. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap Procedures for Detecting Multiple Persistence Shifts in Heteroskedastic Time Series," Boston University - Department of Economics - Working Papers Series WP2020-009, Boston University - Department of Economics.
    10. Scott W. Hegerty, 2020. "Structural breaks and regional inflation convergence for five new Euro members," Economic Change and Restructuring, Springer, vol. 53(2), pages 219-239, May.
    11. Vasilios Plakandaras & Rangan Gupta & Mehmet Balcilar & Qiang Ji, 2021. "Evolving United States Stock Market Volatility: The Role of Conventional and Unconventional Monetary Policies," Working Papers 202113, University of Pretoria, Department of Economics.
    12. James Yetman, 2017. "The evolution of inflation expectations in Canada and the US," Canadian Journal of Economics, Canadian Economics Association, vol. 50(3), pages 711-737, August.
    13. Cheolbeom Park & Erdenebat Bataa, 2017. "Is the Recent Low Oil Price Attributable to the Shale Revolution?," Discussion Paper Series 1704, Institute of Economic Research, Korea University.
    14. Luetkepohl Helmut & Xu Fang, 2011. "Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-23, February.
    15. Jesper Roine & Daniel Waldenström, 2011. "Common Trends and Shocks to Top Incomes: A Structural Breaks Approach," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 832-846, August.
    16. Gantungalag Altansukh & Ralf Becker & George Bratsiotis & Denise R. Osborn, 2018. "Structural Breaks in International Inflation Linkages for OECD Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 240, Economics, The University of Manchester.
    17. Bataa, Erdenebat, 2012. "The Composite Leading Indicator of Mongolia," MPRA Paper 72415, University Library of Munich, Germany.
    18. Bataa, Erdenebat, 2012. "Macroeconomic risks of Mongolia and ways to mitigate them," MPRA Paper 72386, University Library of Munich, Germany, revised 25 Jun 2013.
    19. OECD & Elena Rusticelli, 2014. "Rescuing the Phillips curve: Making use of long-term unemployment in the measurement of the NAIRU," OECD Journal: Economic Studies, OECD Publishing, vol. 2014(1), pages 109-127.
    20. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2009. "Structural Breaks in the International Transmission of Inflation," Centre for Growth and Business Cycle Research Discussion Paper Series 119, Economics, The University of Manchester.

  33. Nalan Basturk & Richard Paap & Dick van Dijk, 2008. "Structural Differences in Economic Growth," Tinbergen Institute Discussion Papers 08-085/4, Tinbergen Institute.

    Cited by:

    1. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    2. Michelle Gilmartin & Dimitris Korobilis, 2012. "On Regional Unemployment: An Empirical Examination of the Determinants of Geographical Differentials in the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 59(2), pages 179-195, May.
    3. Morier, Bruno & Teles, Vladimir Kühl, 2016. "A Time-Varying Markov-Switching Model For Economic Growth," Macroeconomic Dynamics, Cambridge University Press, vol. 20(6), pages 1550-1580, September.

  34. Schauten, M.B.J. & van Dijk, D.J.C. & van der Waal, J-P., 2008. "Corporate Governance and the Value of Excess Cash Holdings of Large European Firms," ERIM Report Series Research in Management ERS-2008-027-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Chowdhury, Rajib & Doukas, John A. & Park, Jong Chool, 2021. "Stakeholder orientation and the value of cash holdings: Evidence from a natural experiment," Journal of Corporate Finance, Elsevier, vol. 69(C).
    2. Mohamed Arouri & Guillaume Pijourlet, 2017. "CSR Performance and the Value of Cash Holdings: International Evidence," Journal of Business Ethics, Springer, vol. 140(2), pages 263-284, January.
    3. Mosab I. Tabash & Adel Ahmed & Linda Nalini Daniel & Yasmeen Elsantil, 2023. "Impact of Board Ownership Structure on Firm Value and Excessive Cash Holdings: Evidence from Pakistan," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(3), pages 109-134, September.
    4. Mun, Seongjae & Han, Seung Hun & Seo, Dongwook, 2020. "The impact of CEO educational background on corporate cash holdings and value of excess cash," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    5. Alomran, Abdulaziz Ahmed & Alsubaiei, Bader Jawid, 2022. "Oil price uncertainty and corporate cash holdings: Global evidence," International Review of Financial Analysis, Elsevier, vol. 81(C).
    6. Pascal Nguyen & Nahid Rahman, 2020. "Institutional ownership, cross‐shareholdings and corporate cash reserves in Japan," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1175-1207, April.
    7. Godfred A. Bokpin & Zangina Isshaq & Francis Aboagye‐Otchere, 2011. "Ownership structure, corporate governance and corporate liquidity policy," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 3(3), pages 262-279, August.
    8. Benjamin Carl Anderson, 2022. "Internal control weaknesses, agency costs, and the value of cash holdings," Economics Bulletin, AccessEcon, vol. 42(2), pages 852-866.
    9. Tosun, Onur Kemel & El Kalak, Izidin & Hudson, Robert, 2022. "How female directors help firms to attain optimal cash holdings," International Review of Financial Analysis, Elsevier, vol. 80(C).
    10. Liu, Benjamin & Johl, Shireenjit & Lasantha, Ruwan, 2023. "ESG scores and cash holdings: The role of disciplinary trading," Finance Research Letters, Elsevier, vol. 55(PA).

  35. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Discussion Papers 2008-23, School of Economics, The University of New South Wales.

    Cited by:

    1. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    2. Koziol, Philipp & Schell, Carmen & Eckhardt, Meik, 2015. "Credit risk stress testing and copulas: Is the Gaussian copula better than its reputation?," Discussion Papers 46/2015, Deutsche Bundesbank.
    3. Jean-David Fermanian, 2012. "An overview of the goodness-of-fit test problem for copulas," Papers 1211.4416, arXiv.org.
    4. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    5. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
    6. Cees Diks & Valentyn Panchenko & Oleg Sokolinskiy, & Dick van Dijk, 2013. "Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support," Tinbergen Institute Discussion Papers 13-061/III, Tinbergen Institute.
    7. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    8. Marc Gronwald & Janina Ketterer & Stefan Trück, 2011. "The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis," CESifo Working Paper Series 3418, CESifo.
    9. Liu, Xiaochun, 2015. "Modeling time-varying skewness via decomposition for out-of-sample forecast," International Journal of Forecasting, Elsevier, vol. 31(2), pages 296-311.
    10. Diks, Cees & Panchenko, Valentyn & Sokolinskiy, Oleg & van Dijk, Dick, 2014. "Comparing the accuracy of multivariate density forecasts in selected regions of the copula support," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 79-94.
    11. Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2019. "Parametric Inference on the Mean of Functional Data Applied to Lifetime Income Curves," Working papers 2019rwp-153, Yonsei University, Yonsei Economics Research Institute.
    12. Krenar Avdulaj & Jozef Barunik, 2013. "Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data," Papers 1307.5981, arXiv.org, revised Feb 2015.
    13. Hayette Gatfaoui, 2010. "Investigating the Dependence Structure between Credit Default Swap Spreads and the U.S. Financial Market," Post-Print hal-00565525, HAL.
    14. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2020. "Modelling Returns in US Housing Prices – You’re the One for Me, Fat Tails," Working Papers 2020:13, Örebro University, School of Business.
    15. Dr. Gregor Bäurle & Elizabeth Steiner & Dr. Gabriel Züllig, 2018. "Forecasting the production side of GDP," Working Papers 2018-16, Swiss National Bank.
    16. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    17. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
    18. Abdallah Ben Saida & Jean-luc Prigent, 2018. "On the robustness of portfolio allocation under copula misspecification," Annals of Operations Research, Springer, vol. 262(2), pages 631-652, March.
    19. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    20. Gang-Jin Wang & Chi Xie & Peng Zhang & Feng Han & Shou Chen, 2014. "Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-11, May.
    21. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    22. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.
    23. Ahmet Akca & Ethem Çanakoğlu, 2021. "Adaptive stochastic risk estimation of firm operating profit," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(3), pages 463-504, September.
    24. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    25. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 90-110.
    26. Fawad, Muhammad & Yan, Ting & Chen, Lu & Huang, Kangdi & Singh, Vijay P., 2019. "Multiparameter probability distributions for at-site frequency analysis of annual maximum wind speed with L-Moments for parameter estimation," Energy, Elsevier, vol. 181(C), pages 724-737.
    27. Pelster, Matthias & Vilsmeier, Johannes, 2016. "The determinants of CDS spreads: Evidence from the model space," Discussion Papers 43/2016, Deutsche Bundesbank.
    28. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.

  36. Markwat, T.D. & Kole, H.J.W.G. & van Dijk, D.J.C., 2008. "Contagion as Domino Effect in Global Stock Markets," ERIM Report Series Research in Management ERS-2008-071-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Dirk G. Baur & Thomas K. McDermott, "undated". "Is gold a safe haven? International evidence," The Institute for International Integration Studies Discussion Paper Series iiisdp310, IIIS.
    2. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2020. "Market-crash forecasting based on the dynamics of the alpha-stable distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Li, Chong, 2017. "Log-periodic view on critical dates of the Chinese stock market bubbles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 305-311.
    4. Paolo Emilio Mistrulli, 2007. "Assessing financial contagion in the interbank market: Maximum entropy versus observed interbank lending patterns," Temi di discussione (Economic working papers) 641, Bank of Italy, Economic Research and International Relations Area.
    5. Agudelo, Diego A. & Múnera, Daimer J., 2023. "Who are the vectors of contagion? Evidence from emerging markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
    6. Charlotte Christiansen, 2013. "Classifying Returns as Extreme: European Stock and Bond Markets," CREATES Research Papers 2013-37, Department of Economics and Business Economics, Aarhus University.
    7. Dragan Tevdovski & Viktor Stojkoski, 2021. "What is Behind Extreme Negative Returns co-movement in the South Eastern European Stock Markets?," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 68(1), pages 43-61, March.
    8. Khalfaoui, Rabeh & Tiwari, Aviral Kumar & Kablan, Sandrine & Hammoudeh, Shawkat, 2021. "Interdependence and lead-lag relationships between the oil price and metal markets: Fresh insights from the wavelet and quantile coherency approaches," Energy Economics, Elsevier, vol. 101(C).
    9. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    10. Corbet, Shaen & Efthymiou, Marina & Lucey, Brian & O'Connell, John F., 2021. "When lightning strikes twice: The tragedy-induced demise and attempted corporate resuscitation of Malaysia airlines," Annals of Tourism Research, Elsevier, vol. 87(C).
    11. Esaka, Taro, 2010. "De facto exchange rate regimes and currency crises: Are pegged regimes with capital account liberalization really more prone to speculative attacks?," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1109-1128, June.
    12. Mensi, Walid & Hammoudeh, Shawkat & Rehman, Mobeen Ur & Al-Maadid, Alanoud Ali S. & Hoon Kang, Sang, 2020. "Dynamic risk spillovers and portfolio risk management between precious metals and global foreign exchange markets," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    13. Dirk G. Baur, 2010. "Financial Contagion and the Real Economy," CAMA Working Papers 2010-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Don Bredin & Stuart Hyde, 2010. "Investigating Sources of Unanticipated Exposure in Industry Stock Returns," Working Papers 201001, Geary Institute, University College Dublin.
    15. Fischer, Justina A.V., 2012. "Globalization and Political Trust," MPRA Paper 36692, University Library of Munich, Germany.
    16. Takashi Miyazaki & Shigeyuki Hamori, 2016. "The determinants of a simultaneous crash in gold and stock markets: An ordered logit approach," Discussion Papers 1603, Graduate School of Economics, Kobe University.
    17. Reboredo, Juan C., 2012. "Modelling oil price and exchange rate co-movements," Journal of Policy Modeling, Elsevier, vol. 34(3), pages 419-440.
    18. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    19. René Garcia & Georges Tsafack, 2009. "Dependence Structure and Extreme Comovements in International Equity and Bond Markets," CIRANO Working Papers 2009s-21, CIRANO.
    20. Rafał SIEDLECKI & Daniel PAPLA, 2016. "Conditional Correlation Coefficient As A Tool For Analysis Of Contagion In Financial Markets And Real Economy Indexes Based On The Synthetic Ratio," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 287-299.
    21. Trabelsi, Nader, 2017. "Asymmetric tail dependence between oil price shocks and sectors of Saudi Arabia System," The Journal of Economic Asymmetries, Elsevier, vol. 16(C), pages 26-41.
    22. Ioana Moldovan & Claudia Medrega, 2011. "Correlation of International Stock Markets Before and During the Subprime Crisis," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 14(40), pages 173-193, June.
    23. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.
    24. Chiang, Thomas C. & Zheng, Dazhi, 2010. "An empirical analysis of herd behavior in global stock markets," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1911-1921, August.
    25. Maria Kasch & Massimiliano Caporin, 2008. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," "Marco Fanno" Working Papers 0065, Dipartimento di Scienze Economiche "Marco Fanno".
    26. Dragan Tevdovski, 2014. "Extreme negative coexceedances in South Eastern European stock markets," CREATES Research Papers 2014-18, Department of Economics and Business Economics, Aarhus University.
    27. Sayantan Banerjee & Kousik Guhathakurta, 2019. "Change-point Analysis in Financial Networks," Papers 1911.05952, arXiv.org.
    28. Hummaira Jabeen, 2022. "Monetary Policy Shock Transmission in Emerging Markets," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 8(4), pages 379-390, December.
    29. Apostolos Thomadakis, 2012. "Measuring Financial Contagion with Extreme Coexceedances," School of Economics Discussion Papers 1112, School of Economics, University of Surrey.
    30. Kang, Sanghoon & Hernandez, Jose Arreola & Sadorsky, Perry & McIver, Ronald, 2021. "Frequency spillovers, connectedness, and the hedging effectiveness of oil and gold for US sector ETFs," Energy Economics, Elsevier, vol. 99(C).
    31. Dimic, Nebojsa & Piljak, Vanja & Swinkels, Laurens & Vulanovic, Milos, 2021. "The structure and degree of dependence in government bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    32. Sensoy, Ahmet & Hacihasanoglu, Erk & Nguyen, Duc Khuong, 2015. "Dynamic convergence of commodity futures: Not all types of commodities are alike," Resources Policy, Elsevier, vol. 44(C), pages 150-160.
    33. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2013. "On the short- and long-run efficiency of energy and precious metal markets," Energy Economics, Elsevier, vol. 40(C), pages 832-844.
    34. Dungey, Mardi & Milunovich, George & Thorp, Susan, 2010. "Unobservable shocks as carriers of contagion," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 1008-1021, May.
    35. Yousaf, Imran & Beljid, Makram & Chaibi, Anis & Ajlouni, Ahmed AL, 2022. "Do volatility spillover and hedging among GCC stock markets and global factors vary from normal to turbulent periods? Evidence from the global financial crisis and Covid-19 pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    36. Pedro Raffy Vartanian, 2020. "Volatility transmission between commodities and Ibovespa in the period 2000–2016: Is there a possibility of diversification?," International Economics and Economic Policy, Springer, vol. 17(2), pages 483-501, May.
    37. Liu, Hsiang-Hsi & Wang, Teng-Kun & Li, Weny, 2019. "Dynamical Volatility and Correlation among US Stock and Treasury Bond Cash and Futures Markets in Presence of Financial Crisis: A Copula Approach," Research in International Business and Finance, Elsevier, vol. 48(C), pages 381-396.
    38. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Sensoy, Ahmet & Kang, Sang Hoon, 2019. "Energy, precious metals, and GCC stock markets: Is there any risk spillover?," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 45-70.
    39. Szymon Okoń, 2012. "New Approach to Remuneration Policy for Investment Firms: a Polish Capital Market Perspective," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 6(1), March.
    40. Jian, Zhihong & Li, Xupei, 2021. "Skewness-based market integration: A systemic risk measure across international equity markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    41. Sensoy, Ahmet, 2013. "Dynamic relationship between precious metals," Resources Policy, Elsevier, vol. 38(4), pages 504-511.
    42. Mink, Mark, 2015. "Measuring stock market contagion: Local or common currency returns?," Emerging Markets Review, Elsevier, vol. 22(C), pages 18-24.
    43. Sulejmani Artan & Tevdovski Dragan, 2022. "How the Contagion is Transmitted to the Macedonian Stock Market? an Analysis of Co-Exceedances," South East European Journal of Economics and Business, Sciendo, vol. 17(1), pages 1-13, June.
    44. Mansor, Ibrahim H., 2011. "Financial Market Risk and Gold Investment in an Emerging Market: The Case of Malaysia," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 79-89, December.
    45. Berger, Dave & Pukthuanthong, Kuntara, 2016. "Fragility, stress, and market returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 152-163.
    46. Štefan Lyócsa & Roman Horváth, 2018. "Stock Market Contagion: a New Approach," Open Economies Review, Springer, vol. 29(3), pages 547-577, July.
    47. Daniel Traian PELE & Alexandra Ioana CONDA & Raul Cristian BAG & Miruna MAZURENCU-MARINESCU-PELE & Vasile Alecsandru STRAT, 2023. "Financial Risk Meter for The Romanian Stock Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-24, March.
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    51. Konermann, Patrick & Meinerding, Christoph & Sedova, Olga, 2013. "Asset allocation in markets with contagion: The interplay between volatilities, jump intensities, and correlations," Review of Financial Economics, Elsevier, vol. 22(1), pages 36-46.
    52. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2012. "International diversification: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 871-885.
    53. EnDer Su, 2021. "Testing stock market contagion properties between large and small stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 147-202, July.
    54. Fuchun Li & Hui Zhu, 2014. "Testing for financial contagion based on a nonparametric measure of the cross‐market correlation," Review of Financial Economics, John Wiley & Sons, vol. 23(3), pages 141-147, September.
    55. Fischer, Justina A.V., 2012. "The choice of domestic policies in a globalized economy," MPRA Paper 36990, University Library of Munich, Germany.
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    57. Abubakar Jamaladeen & David E. Omoregie & Samuel F. Onipede & Nafiu A. Bashir, 2022. "A regime-switching skew-normal model of contagion in some selected stock markets," SN Business & Economics, Springer, vol. 2(12), pages 1-20, December.
    58. Erniel B. Barrios & Paolo Victor T. Redondo, 2021. "Nonparametric Test for Volatility in Clustered Multiple Time Series," Papers 2104.14412, arXiv.org, revised Feb 2023.
    59. Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah & Sjö, Bo, 2016. "On the time scale behavior of equity-commodity links: Implications for portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 30-46.
    60. Yoon, Seong-Min & Al Mamun, Md & Uddin, Gazi Salah & Kang, Sang Hoon, 2019. "Network connectedness and net spillover between financial and commodity markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 801-818.
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    62. Li, Fuchun & Zhu, Hui, 2014. "Testing for financial contagion based on a nonparametric measure of the cross-market correlation," Review of Financial Economics, Elsevier, vol. 23(3), pages 141-147.
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    64. Xing, Dun-Zhong & Li, Hai-Feng & Li, Jiang-Cheng & Long, Chao, 2021. "Forecasting price of financial market crash via a new nonlinear potential GARCH model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    65. V. Rasskazov E. & В. Рассказов Е., 2016. "Измерение Финансового Заражения На Примере Моделирования Риска Банковского Дефолта // The Methodology For Measuring Financial Contagion: The Case Study Of Bank Default Risk Simulation," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 20(3), pages 54-61.
    66. Sandoval Paucar, Giovanny, 2018. "Efectos de desbordamiento sobre los mercados financieros de Colombia. Identificación a través de la heterocedasticidad [Spillovers effects on financial markets of Colombia. Identification through h," MPRA Paper 90422, University Library of Munich, Germany.
    67. Lei Wu & Qingbin Meng & Kuan Xu, 2015. "'Slow-burn' spillover and 'fast and furious' contagion: a study of international stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 933-958, June.
    68. Chouliaras, Andreas & Grammatikos, Theoharry, 2013. "News Flow, Web Attention and Extreme Returns in the European Financial Crisis," MPRA Paper 51335, University Library of Munich, Germany.
    69. Wan, Li & Han, Liyan & Xu, Yang & Matousek, Roman, 2021. "Dynamic linkage between the Chinese and global stock markets: A normal mixture approach," Emerging Markets Review, Elsevier, vol. 49(C).
    70. Omar, Ayman M.A. & Wisniewski, Tomasz Piotr & Nolte, Sandra, 2017. "Diversifying away the risk of war and cross-border political crisis," Energy Economics, Elsevier, vol. 64(C), pages 494-510.
    71. Koch, Nicolas, 2014. "Tail events: A new approach to understanding extreme energy commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 195-205.
    72. Martín-Barragán, Belén & Ramos, Sofía B. & Veiga, Helena, 2013. "Correlations between oil and stock markets : a wavelet-based approach," DES - Working Papers. Statistics and Econometrics. WS ws130504, Universidad Carlos III de Madrid. Departamento de Estadística.
    73. Anika Sedyaning Wikanti, 2011. "Contagion effects of us financial crisis on Indonesia," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 3(2), pages 125-137, April.
    74. Ioana MOLDOVAN, 2011. "Stock Markets Correlation: before and during the Crisis Analysis," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(8(561)), pages 111-122, August.
    75. Jian, Zhihong & Lu, Haisong & Zhu, Zhican & Xu, Huiling, 2023. "Frequency heterogeneity of tail connectedness: Evidence from global stock markets," Economic Modelling, Elsevier, vol. 125(C).
    76. Buch, Claudia M. & Neugebauer, Katja, 2011. "Bank-specific shocks and the real economy," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 2179-2187, August.
    77. Suleman, Muhammad Tahir & McIver, Ron & Kang, Sang Hoon, 2021. "Asymmetric volatility connectedness between Islamic stock and commodity markets," Global Finance Journal, Elsevier, vol. 49(C).
    78. Kumar, Pawan & Singh, Vipul Kumar, 2022. "Does crude oil fire the emerging markets currencies contagion spillover? A systemic perspective," Energy Economics, Elsevier, vol. 116(C).
    79. Faggini, Marisa & Bruno, Bruna & Parziale, Anna, 2019. "Crises in economic complex networks: Black Swans or Dragon Kings?," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 105-115.
    80. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2011. "International diversification: A copula approach," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 403-417, February.
    81. Kateryna Anatoliyevna Kopyl & John Byong-Tek Lee, 2016. "How safe are the safe haven assets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 30(4), pages 453-482, November.
    82. Cerqueti, Roy & Costantini, Mauro, 2011. "Testing for rational bubbles in the presence of structural breaks: Evidence from nonstationary panels," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2598-2605, October.
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    84. Berger, Dave & Pukthuanthong, Kuntara, 2012. "Market fragility and international market crashes," Journal of Financial Economics, Elsevier, vol. 105(3), pages 565-580.
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    92. Ellul, Reuben, 2015. "Analysing correlation between the MSE index and global stock markets," MPRA Paper 72464, University Library of Munich, Germany.
    93. Guidolin, Massimo & Pedio, Manuela, 2017. "Identifying and measuring the contagion channels at work in the European financial crises," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 117-134.
    94. Fischer, Justina A.V., 2012. "The choice of domestic policies in a globalized economy: Extended Version," MPRA Paper 37816, University Library of Munich, Germany.
    95. Bali, Turan G. & Cakici, Nusret, 2010. "World market risk, country-specific risk and expected returns in international stock markets," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1152-1165, June.
    96. Kang, Sang Hoon & Uddin, Gazi Salah & Troster, Victor & Yoon, Seong-Min, 2019. "Directional spillover effects between ASEAN and world stock markets," Journal of Multinational Financial Management, Elsevier, vol. 52.
    97. Panait, Iulian & Slavescu, Ecaterina Oana, 2011. "Volatility and causality study of the daily returns on the Bucharest Stock Exchange during 2007-2011," MPRA Paper 41786, University Library of Munich, Germany.

  37. Chulia-Soler, H. & Martens, M.P.E. & van Dijk, D.J.C., 2007. "The Effects of Federal Funds Target Rate Changes on S&P100 Stock Returns, Volatilities, and Correlations," ERIM Report Series Research in Management ERS-2007-066-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Kishor, N. Kundan & Marfatia, Hardik A., 2013. "The time-varying response of foreign stock markets to U.S. monetary policy surprises: Evidence from the Federal funds futures market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 1-24.
    2. Marfatia, Hardik A., 2014. "Impact of uncertainty on high frequency response of the U.S. stock markets to the Fed's policy surprises," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(3), pages 382-392.

  38. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Ravazzolo, F., 2007. "Evaluating real-time forecasts in real-time," Econometric Institute Research Papers EI 2007-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Clements, Michael P. & Galvão, Ana Beatriz, 2009. "First Announcements and Real Economic Activity," The Warwick Economics Research Paper Series (TWERPS) 885, University of Warwick, Department of Economics.

  39. de Zwart, G.J. & Markwat, T.D. & Swinkels, L.A.P. & van Dijk, D.J.C., 2007. "The Economic Value of Fundamental and Technical Information in Emerging Currency Markets," ERIM Report Series Research in Management ERS-2007-096-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Yamani, Ehab, 2019. "Diversification role of currency momentum for carry trade: Evidence from financial crises," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 1-19.
    2. Stephen Gilmore & Fumio Hayashi, 2008. "Emerging Market Currency Excess Returns," NBER Working Papers 14528, National Bureau of Economic Research, Inc.
    3. Evzen Kocenda & Vit Bubak & Filip Zikes, 2011. "Volatility Transmission in Emerging European Foreign Exchange Markets," William Davidson Institute Working Papers Series wp1020, William Davidson Institute at the University of Michigan.
    4. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," CEPR Discussion Papers 7008, C.E.P.R. Discussion Papers.
    5. Dowling, Michael & Cummins, Mark & Lucey, Brian M., 2016. "Psychological barriers in oil futures markets," Energy Economics, Elsevier, vol. 53(C), pages 293-304.
    6. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    7. Kuang, P. & Schröder, M. & Wang, Q., 2014. "Illusory profitability of technical analysis in emerging foreign exchange markets," International Journal of Forecasting, Elsevier, vol. 30(2), pages 192-205.
    8. Tajaddini, Reza & Crack, Timothy Falcon, 2012. "Do momentum-based trading strategies work in emerging currency markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 521-537.
    9. Huadong Chang & Guozhi An, 2019. "Will History Repeat Itself? Empirical Research on A-Share Candlesticks in China Based on Matching Method," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(5), pages 1-8.
    10. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    11. Paul De Grauwe & Agnieszka Markiewicz, 2006. "Learning to Forecast the Exchange Rate: Two Competing Approaches," Computing in Economics and Finance 2006 367, Society for Computational Economics.
    12. Melvin, Michael & Prins, John & Shand, Duncan, 2013. "Forecasting Exchange Rates: an Investor Perspective," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 721-750, Elsevier.
    13. Yamani, Ehab, 2021. "Can technical trading beat the foreign exchange market in times of crisis?," Global Finance Journal, Elsevier, vol. 48(C).
    14. Christopher J. Neely & Paul A. Weller, 2011. "Lessons from the evolution of foreign exchange trading strategies," Working Papers 2011-021, Federal Reserve Bank of St. Louis.
    15. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Forecasting the CNY-CNH pricing differential: The role of investor attention," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 232-247.
    16. Carl Chiarella & Xue-Zhong He & Remco C.J. Zwinkels, 2014. "Heterogeneous Expectations in Asset Pricing: Empirical Evidence from the S&P500," Research Paper Series 344, Quantitative Finance Research Centre, University of Technology, Sydney.
    17. Matthew Famiglietti & Yuliya Ivanova & Christopher J. Neely & Paul A. Weller, 2014. "Can risk explain the profitability of technical trading in currency markets?," Working Papers 2014-033, Federal Reserve Bank of St. Louis, revised 12 Jun 2020.
    18. Jamali, Ibrahim & Yamani, Ehab, 2019. "Out-of-sample exchange rate predictability in emerging markets: Fundamentals versus technical analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 241-263.
    19. Atilgan, Yigit & Demirtas, K. Ozgur & Simsek, Koray D., 2016. "Derivative markets in emerging economies: A survey," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 88-102.
    20. Meifen Chu, 2020. "Wavelet Analysis of the Euro and its Co-Movement with Four Exchange Rates," Eurasian Journal of Social Sciences, Eurasian Publications, vol. 8(3), pages 123-133.
    21. Doskov, Nikolay & Swinkels, Laurens, 2015. "Empirical evidence on the currency carry trade, 1900–2012," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 370-389.
    22. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    23. Jiadong Tong & Zijun Wang & Jian Yang, 2016. "Information Flow Between Forward and Spot Markets: Evidence From the Chinese Renminbi," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(7), pages 695-718, July.
    24. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    25. Conover, C. Mitchell & Jensen, Gerald R. & Johnson, Robert R. & Szakmary, Andrew C., 2017. "Emerging markets: Is the trend still your friend?," Global Finance Journal, Elsevier, vol. 32(C), pages 128-148.
    26. de Jong, Eelke & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2010. "Heterogeneity of agents and exchange rate dynamics: Evidence from the EMS," Journal of International Money and Finance, Elsevier, vol. 29(8), pages 1652-1669, December.
    27. Doukas, John A. & Zhang, Hao, 2013. "The performance of NDF carry trades," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 172-190.

  40. van Dijk, A. & Franses, Ph.H.B.F. & Paap, R. & van Dijk, D.J.C., 2007. "Modeling regional house prices," Econometric Institute Research Papers EI 2007-55, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. S Holly & M Hashem Pesaran & T Yamagata, "undated". "Spatial and Temporal Diffusion of House Prices in the UK," Discussion Papers 09/32, Department of Economics, University of York.
    2. Ling Zhang & He Wang & Yan Song & Haizhen Wen, 2019. "Spatial Spillover of House Prices: An Empirical Study of the Yangtze Delta Urban Agglomeration in China," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    3. Dominik Blatt & Kausik Chaudhuri & Hans Manner, 2021. "Spillover in the UK Housing Market," Graz Economics Papers 2021-13, University of Graz, Department of Economics.
    4. James D. Hamilton & Michael T. Owyang, 2011. "The Propagation of Regional Recessions," NBER Working Papers 16657, National Bureau of Economic Research, Inc.
    5. Katharina Pijnenburg, 2013. "The Spatial Dimension of US House Price Developments," Discussion Papers of DIW Berlin 1270, DIW Berlin, German Institute for Economic Research.
    6. Roel Helgers & Erik Buyst, 2016. "Spatial and Temporal Diffusion of Housing Prices in the Presence of a Linguistic Border: Evidence from Belgium," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(1), pages 92-122, March.
    7. Ceren Ozgen & Thomas de Graff, 2013. "Sorting out the impact of cultural diversity on innovative firms. An empirical analysis of Dutch micro-data," Norface Discussion Paper Series 2013012, Norface Research Programme on Migration, Department of Economics, University College London.
    8. Andrea Cipollini & Fabio Parla, 2018. "Housing Market Shocks in Italy: a GVAR approach," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0069, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    9. Katharina Pijnenburg, 2017. "The spatial dimension of US house prices," Urban Studies, Urban Studies Journal Limited, vol. 54(2), pages 466-481, February.
    10. Katharina Pijnenburg, 2014. "The Spatial Dimension of US House Price Developments," ERSA conference papers ersa14p127, European Regional Science Association.
    11. Maureen Lankhuizen & Thomas De Graaff & Henri De Groot, 2012. "Product Heterogeneity, Intangible Barriers & Distance Decay: The effect of multiple dimensions of distance on trade across different product categories," ERSA conference papers ersa12p151, European Regional Science Association.
    12. Sergei S. Shibaev, 2016. "Recession Propagation In Small Regional Economies: Spatial Spillovers And Endogenous Clustering," Working Paper 1369, Economics Department, Queen's University.
    13. Bing Zhu & Dorinth van Dijk & Colin Lizieri, 2021. "Price diffusion across international private commercial real estate markets," Working Papers 732, DNB.
    14. Maureen B.M. Lankhuizen & Thomas de Graaff & Henri L.F. de Groot, 2012. "Product Heterogeneity, Intangible Barriers and Distance Decay: The Effect of Multiple Dimensions of Distance on Trade across Different Product Categories," Tinbergen Institute Discussion Papers 12-065/3, Tinbergen Institute.

  41. Stracca, Livio & Musso, Alberto & van Dijk, Dick, 2007. "Instability and nonlinearity in the euro area Phillips curve," Working Paper Series 811, European Central Bank.

    Cited by:

    1. Claudia Fontanari, & Antonella Palumbo & Chiara Salvatori, 2019. "Potential Output in Theory and Practice: A Revision and Update of Okun`s Original Method," Working Papers Series 93, Institute for New Economic Thinking.
    2. Bogdan DIMA & Ştefana Maria DIMA & Flavia BARNA, 2019. "Inflation Contagion Effects in the Baltic Countries: A Time-varying Coefficients VAR with Stochastic Volatility Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 72-87, March.
    3. Cristian Constantin Popescu & Laura Diaconu (Maxim), 2022. "Inflation – Unemployment Dilemma. A Cross-Country Analysis," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 69(3), pages 377-392, June.
    4. Sin-Yu Ho & Bernard Njindan Iyke, 2019. "Unemployment And Inflation: Evidence Of A Nonlinear Phillips Curve In The Eurozone," Journal of Developing Areas, Tennessee State University, College of Business, vol. 53(4), pages 151-163, Fall.
    5. Sarah M. Lein & Eva Köberl, 2009. "Capacity Utilisation, Constraints and Price Adjustments under the Microscope," Working Papers 2009-06, Swiss National Bank.
    6. Barrera-Chaupis, Carlos, 2014. "La relación entre los ciclos discretos en la inflación y el crecimiento: Perú 1993-2012 [The relationship between inflation's and growth's discrete cycles: Peru 1993-2012]," MPRA Paper 60959, University Library of Munich, Germany.
    7. Gross, Marco, 2009. "Nonparametric Hybrid Phillips Curves Based on Subjective Expectations: Estimates for the Euro Area," Working Paper Series 1119, European Central Bank.
    8. Chletsos, Michael & Drosou, Vasiliki & Roupakias, Stelios, 2016. "Can Phillips curve explain the recent behavior of inflation? Further evidence from USA and Canada," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 20-28.
    9. Weider Loureto Alves & Roberto Tatiwa Ferreira, 2023. "Phillips curve and the exchange rate pass-through: a time–frequency approach," Empirical Economics, Springer, vol. 64(5), pages 2165-2181, May.
    10. Areosa, Waldyr Dutra & McAleer, Michael & Medeiros, Marcelo C., 2011. "Moment-based estimation of smooth transition regression models with endogenous variables," Journal of Econometrics, Elsevier, vol. 165(1), pages 100-111.
    11. Seong Yeon Chang & Pierre Perron, 2013. "A Comparison of Alternative Methods to Construct Confidence Intervals for the Estimate of a Break Date in Linear Regression Models," Boston University - Department of Economics - Working Papers Series wp2015-010, Boston University - Department of Economics, revised 11 Oct 2015.
    12. Fröhling, Annette & Lommatzsch, Kirsten, 2011. "Output sensitivity of inflation in the euro area: Indirect evidence from disaggregated consumer prices," Discussion Paper Series 1: Economic Studies 2011,25, Deutsche Bundesbank.
    13. Doyle, Matthew & Falk, Barry L., 2006. "Do Asymmetric Central Bank Preferences Help Explain Observed Inflation Outcomes?," Staff General Research Papers Archive 12501, Iowa State University, Department of Economics.
    14. Karol Szafranek, 2016. "Linking excessive disinflation and output movements in an emerging, small open economy A hybrid New Keynesian Phillips Curve perspective," NBP Working Papers 239, Narodowy Bank Polski.
    15. Renaud St-Cyr, 2018. "Non-linéarité de la courbe de Phillips : un survol de la littérature," Staff Analytical Notes 2018-3, Bank of Canada.
    16. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    17. OKIMOTO Tatsuyoshi, 2018. "Trend Inflation and Monetary Policy Regimes in Japan," Discussion papers 18024, Research Institute of Economy, Trade and Industry (RIETI).
    18. Dieppe, Alistair & Ortega, Eva & D'Agostino, Antonello & Karlsson, Tohmas & Benkovskis, Konstantins & Caivano, Michele & Hurtado, Samuel & Várnai, Tímea, 2011. "Assessing the sensitivity of inflation to economic activity," Working Paper Series 1357, European Central Bank.
    19. Burhan Biçer & Almila Burgac Cil, 2023. "Symmetric and Asymmetric Dynamics of Output Gap and Inflation Relation for Turkish Economy," Prague Economic Papers, Prague University of Economics and Business, vol. 2023(5), pages 520-549.
    20. Wieland, Volker & Wolters, Maik, 2014. "Is there a threat of self-reinforcing deflation in the Euro area? A view through the lens of the Phillips curve," IMFS Working Paper Series 81, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    21. Mónica Correa-López & Matías Pacce & Kathi Schlepper, 2019. "Exploring trend inFLation dynamics in Euro Area countries," Working Papers 1909, Banco de España.
    22. Pierre Perron & Yohei Yamamoto, 2008. "On the Usefulness or Lack Thereof of Optimality Criteria for Structural Change Tests," Boston University - Department of Economics - Working Papers Series wp2008-006, Boston University - Department of Economics.
    23. Axel A Weber & Rafael Gerke & Andreas Worms, 2009. "Has the monetary transmission process in the euro area changed? Evidence vased on VAR estimates," BIS Working Papers 276, Bank for International Settlements.
    24. S. Béreau & V. Faubert & K. Schmidt, 2018. "Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors," Working papers 663, Banque de France.
    25. Donayre, Luiggi, 2022. "On the behavior of Okun's law across business cycles," Economic Modelling, Elsevier, vol. 112(C).
    26. Engelbert Stockhammer & Dimitris Sotiropoulos, 2012. "Rebalancing the Euro area: The costs of internal devaluation," Working Papers PKWP1206, Post Keynesian Economics Society (PKES).
    27. Wolfgang Pollan, 2012. "Incomes Policies, Expectations and the NAIRU," WIFO Working Papers 433, WIFO.
    28. Antonia Lopez Villavicencio & Valérie Mignon, 2014. "Nonlinearity of the inflation-output trade-off and time-varying price rigidity," Post-Print hal-01386097, HAL.
    29. Andrejs Bessonovs & Olegs Tkacevs, 2016. "Relationship Between Inflation and Economic Activity and Its Variation Over Time in Latvia," Working Papers 2016/03, Latvijas Banka.
    30. Sfichi Elena Daniela & Bratiloveanu Alina, 2017. "Correlations between Labor Employment and Economic Growth," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 242-247, June.
    31. Semmler, Willi & Gross, Marco, 2017. "Mind the output gap: the disconnect of growth and inflation during recessions and convex Phillips curves in the euro area," Working Paper Series 2004, European Central Bank.
    32. Ferreira, Diego & Palma, Andreza Aparecida, 2015. "Forecasting Inflation with the Phillips Curve: A Dynamic Model Averaging Approach for Brazil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 69(4), December.
    33. Donayre, Luiggi & Panovska, Irina, 2016. "Nonlinearities in the U.S. wage Phillips curve," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 19-43.
    34. Diego Moccero & Shingo Watanabe & Boris Cournède, 2011. "What Drives Inflation in the Major OECD Economies?," OECD Economics Department Working Papers 854, OECD Publishing.
    35. Sznajderska, Anna, 2014. "Asymmetric effects in the Polish monetary policy rule," Economic Modelling, Elsevier, vol. 36(C), pages 547-556.
    36. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2019. "Domestic and global determinants of inflation: evidence from expectile regression," Temi di discussione (Economic working papers) 1225, Bank of Italy, Economic Research and International Relations Area.
    37. Hasanov, Mübariz & Araç, Aysen & Telatar, Funda, 2010. "Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey," Economic Modelling, Elsevier, vol. 27(5), pages 1103-1115, September.
    38. Robert Anderton & Alessandro Galesi & Marco Lombardi & Filippo di Mauro, 2010. "Key Elements of Global Inflation," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    39. Kanellopoulos, Nikolaos C. & Koutroulis, Aristotelis G., 2016. "Non-linearities in euro area inflation persistence," Economic Modelling, Elsevier, vol. 59(C), pages 116-123.
    40. Robert Anderton & Paul Hiebert, 2009. "The Impact of Globalisation on the Euro Area Macroeconomy," Discussion Papers 09/14, University of Nottingham, GEP.
    41. Bofinger, Peter & Schnabel, Isabel & Feld, Lars P. & Schmidt, Christoph M. & Wieland, Volker, 2014. "Mehr Vertrauen in Marktprozesse. Jahresgutachten 2014/15 [More confidence in market processes. Annual Report 2014/15]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201415.
    42. Donayre, Luiggi & Panovska, Irina, 2018. "U.S. wage growth and nonlinearities: The roles of inflation and unemployment," Economic Modelling, Elsevier, vol. 68(C), pages 273-292.
    43. Andrew Levin, 2014. "Comment on “Chronic Deflation in Japan”," Asian Economic Policy Review, Japan Center for Economic Research, vol. 9(1), pages 42-43, January.
    44. William Gatt, 2016. "Time variation, asymmetry and threshold effect in Malta's Phillips curve," CBM Working Papers WP/02/2016, Central Bank of Malta.
    45. Aristidou, Chrystalleni, 2018. "The meta-Phillips Curve: Modelling U.S. inflation in the presence of regime change," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 367-379.
    46. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Forecasting Performance of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7763, C.E.P.R. Discussion Papers.
    47. Dino Martellato, 2008. "Economic Integration and Macroeconomic Convergence in the Euro Area," Working Papers 2008_34, Department of Economics, University of Venice "Ca' Foscari".
    48. Mamdouh Abdelmoula M. ABDELSALAM, 2017. "Improving Phillips Curve’s Inflation Forecasts under Misspecification," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 54-76, September.
    49. Owen Grech & Noel Rapa, 2016. "STREAM: A structural macro-econometric model of the Maltese economy," CBM Working Papers WP/01/2016, Central Bank of Malta.
    50. Malikane, Christopher, 2014. "Traditional Inflation Dynamics," MPRA Paper 61427, University Library of Munich, Germany.
    51. Marcellino, Massimiliano & Musso, Alberto, 2010. "Real time estimates of the euro area output gap: reliability and forecasting performance," Working Paper Series 1157, European Central Bank.

  42. Paap, R. & Segers, R. & van Dijk, D.J.C., 2007. "Do leading indicators lead peaks more than troughs?," Econometric Institute Research Papers EI 2007-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Makram El-Shagi & Gregor von Schweinitz, 2016. "Qual VAR revisited: Good forecast, bad story," Journal of Applied Economics, Universidad del CEMA, vol. 19, pages 293-322, November.
    2. Çakmaklı, Cem & Paap, Richard & van Dijk, Dick, 2013. "Measuring and predicting heterogeneous recessions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2195-2216.
    3. Liu, Jia & Maheu, John M & Song, Yong, 2023. "Identification and Forecasting of Bull and Bear Markets using Multivariate Returns," MPRA Paper 119515, University Library of Munich, Germany.
    4. Henri Nyberg, 2010. "Dynamic probit models and financial variables in recession forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 215-230.
    5. Pérez-Quirós, Gabriel & Poncela, Pilar & Camacho, Máximo, 2012. "Extracting nonlinear signals from several economic indicators," CEPR Discussion Papers 8865, C.E.P.R. Discussion Papers.
    6. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    7. Ruben Hernandez-Murillo & Michael T. Owyang & Margarita Rubio, 2013. "Clustered housing cycles," Working Papers 2013-021, Federal Reserve Bank of St. Louis.
    8. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    9. Catherine Doz & Anna Petronevich, 2017. "On the consistency of the two-step estimates of the MS-DFM: a Monte Carlo study," PSE Working Papers halshs-01592863, HAL.
    10. Bram van Os & Dick van Dijk, 2020. "Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model," Tinbergen Institute Discussion Papers 20-057/VI, Tinbergen Institute, revised 14 Dec 2020.
    11. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
    12. Dovern, Jonas & Ziegler, Christina, 2008. "Predicting growth rates and recessions: assessing US leading indicators under real-time conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy (IfW Kiel).
    13. Cem Çakmakli & Hamza Dem I˙rcani & Sumru Altug, 2021. "Modelling of Economic and Financial Conditions for Real‐Time Prediction of Recessions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 663-685, June.
    14. Sergey Smirnov, 2011. "Those Unpredictable Recessions," HSE Working papers WP BRP 02/EC/2011, National Research University Higher School of Economics.
    15. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    16. Camacho, Maximo, 2013. "Mixed-frequency VAR models with Markov-switching dynamics," Economics Letters, Elsevier, vol. 121(3), pages 369-373.

  43. Fidrmuc, J.P. & Roosenboom, P.G.J. & van Dijk, D.J.C., 2007. "When Do Managers Seek Private Equity Backing in Public-to-Private Transactions?," ERIM Report Series Research in Management ERS-2007-028-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

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    1. Mao, Yaping & Renneboog, Luc, 2015. "Do managers manipulate earnings prior to management buyouts?," Journal of Corporate Finance, Elsevier, vol. 35(C), pages 43-61.
    2. Hammer, Benjamin & Mettner, Sven & Schweizer, Denis & Wünsche, Norbert, 2023. "Management buyouts in times of economic policy uncertainty," Finance Research Letters, Elsevier, vol. 52(C).
    3. Kamoto, Shinsuke, 2017. "Managerial innovation incentives, management buyouts, and shareholders' intolerance of failure," Journal of Corporate Finance, Elsevier, vol. 42(C), pages 55-74.
    4. Mittoo, Usha & Ng, Dennis & Yan, Meng, 2020. "Managerial ownership, credit market conditions, undervaluation and offer premiums in management (MBOs) and leveraged buyouts (LBOs)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    5. Baschieri, Giulia & Carosi, Andrea & Mengoli, Stefano, 2015. "Local IPOs, local delistings, and the firm location premium," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 67-83.
    6. Renneboog, Luc & Vansteenkiste, Cara, 2017. "Leveraged Buyouts : A Survey of the Literature," Discussion Paper 2017-015, Tilburg University, Center for Economic Research.
    7. Yost, Benjamin P., 2023. "Do tax-based proprietary costs discourage public listing?," Journal of Accounting and Economics, Elsevier, vol. 75(2).

  44. Heij, C. & van Dijk, D.J.C. & Groenen, P.J.F., 2006. "Improved Construction of diffusion indexes for macroeconomic forecasting," Econometric Institute Research Papers EI 2006-03-REV, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Jorge L.M. Andraz & Pedro M.D.C.B. Gouveia & Paulo M.M. Rodrigues, 2009. "Modelling and Forecasting the UK Tourism Growth Cycle in Algarve," Tourism Economics, , vol. 15(2), pages 323-338, June.

  45. De Pooter, Michiel & Ravazzolo, Francesco & van Dijk, Dick, 2006. "Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information," MPRA Paper 2512, University Library of Munich, Germany, revised 03 Mar 2007.

    Cited by:

    1. Coroneo, Laura & Nyholm, Ken & Vidova-Koleva, Rositsa, 2008. "How arbitrage-free is the Nelson-Siegel Model?," Working Paper Series 874, European Central Bank.
    2. Dauwe, Alexander & Moura, Marcelo L., 2011. "Forecasting the term structure of the Euro Market using Principal Component Analysis," Insper Working Papers wpe_233, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    3. Clive G. Bowsher & Roland Meeks, 2008. "The dynamics of economics functions: modelling and forecasting the yield curve," Working Papers 0804, Federal Reserve Bank of Dallas.
    4. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    5. Guriev, Sergei & Durnev, Art, 2007. "The Resource Curse: A Corporate Transparency Channel," CEPR Discussion Papers 6547, C.E.P.R. Discussion Papers.
    6. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2007. "The Affine Arbitrage-Free Class of Nelson-Siegel Term Structure Models," PIER Working Paper Archive 07-029, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    7. Peter Exterkate & Dick Van Dijk & Christiaan Heij & Patrick J. F. Groenen, 2013. "Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(3), pages 193-214, April.
    8. Diana Zigraiova & Petr Jakubik, 2017. "Updating the Ultimate Forward Rate over Time: A Possible Approach," Working Papers 2017/03, Czech National Bank.
    9. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," Working Papers 662, Queen Mary University of London, School of Economics and Finance.
    10. David Bolder & Yuliya Romanyuk, 2008. "Combining Canadian Interest-Rate Forecasts," Staff Working Papers 08-34, Bank of Canada.
    11. Penikas, Henry, 2008. "Forecasting for the Bank's Asset-Liability Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 3-26.
    12. Stephen Hall & Kavita Sirichand, 2010. "Decision-Based Forecast Evaluation of UK Interest Rate Predictability," Discussion Papers in Economics 10/09, Division of Economics, School of Business, University of Leicester.
    13. Goodarzi, Milad & Meinerding, Christoph, 2023. "Asset allocation with recursive parameter updating and macroeconomic regime identifiers," Discussion Papers 06/2023, Deutsche Bundesbank.
    14. Penikas, Henry & Simakova, Varvara, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 3-36.
    15. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute.
    16. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  46. Martens, M.P.E. & van Dijk, D.J.C., 2006. "Measuring volatility with the realized range," Econometric Institute Research Papers EI 2006-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Gagan Deep Sharma & Mandeep Mahendru & Mrinalini Srivastava, 2019. "Can Central Banking Policies Make a Difference in Financial Market Performance in Emerging Economies? The Case of India," Economies, MDPI, vol. 7(2), pages 1-19, May.
    2. Jui-Cheng Hung & Tien-Wei Lou & Yi-Hsien Wang & Jun-De Lee, 2013. "Evaluating and improving GARCH-based volatility forecasts with range-based estimators," Applied Economics, Taylor & Francis Journals, vol. 45(28), pages 4041-4049, October.
    3. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2020. "Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Bentes, Sonia R. & Menezes, Rui, 2013. "On the predictability of realized volatility using feasible GLS," Journal of Asian Economics, Elsevier, vol. 28(C), pages 58-66.
    5. Yuta Kurose, 2021. "Stochastic volatility model with range-based correction and leverage," Papers 2110.00039, arXiv.org, revised Oct 2021.
    6. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    7. Liu, Qiang & Liu, Yiqi & Liu, Zhi & Wang, Li, 2018. "Estimation of spot volatility with superposed noisy data," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 62-79.
    8. Ruben Hipp, 2020. "On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity," Staff Working Papers 20-42, Bank of Canada.
    9. Massimiliano Caporin & Gabriel G. Velo, 2011. "Modeling and forecasting realized range volatility," "Marco Fanno" Working Papers 0128, Dipartimento di Scienze Economiche "Marco Fanno".
    10. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    11. Caiya Zhang & Kaihong Xu & Lianfen Qian, 2020. "Asymptotic properties of the QMLE in a log-linear RealGARCH model with Gaussian errors," Statistical Papers, Springer, vol. 61(6), pages 2313-2330, December.
    12. Christensen, Kim & Podolskij, Mark, 2006. "Range-Based Estimation of Quadratic Variation," Technical Reports 2006,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    13. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    15. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    16. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    17. Oleg Sokolinskiy & Dick van Dijk, 2011. "Forecasting Volatility with Copula-Based Time Series Models," Tinbergen Institute Discussion Papers 11-125/4, Tinbergen Institute.
    18. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    19. Simard Clarence & Rémillard Bruno, 2015. "Forecasting time series with multivariate copulas," Dependence Modeling, De Gruyter, vol. 3(1), pages 1-24, May.
    20. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    21. Ozgur (Ozzy) Akay & Mark D. Griffiths & Drew B. Winters, 2010. "On The Robustness Of Range‐Based Volatility Estimators," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 179-199, June.
    22. Sutton, Maxwell & Vasnev, Andrey L. & Gerlach, Richard, 2019. "Mixed interval realized variance: A robust estimator of stock price volatility," Econometrics and Statistics, Elsevier, vol. 11(C), pages 43-62.
    23. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 15(3), pages 94-138.
    24. Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
    25. Dinghai Xu, 2010. "A Threshold Stochastic Volatility Model with Realized Volatility," Working Papers 1003, University of Waterloo, Department of Economics, revised May 2010.
    26. Alex Frino & Michael Garcia, 2018. "Should macroeconomic information be released during trading breaks in futures markets?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(7), pages 775-787, July.
    27. Denisa BANULESCU-RADU & Elena Ivona DUMITRESCU, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," LEO Working Papers / DR LEO 2709, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    28. Massimiliano Caporin & Angelo Ranaldo & Paolo Santucci de Magistris, 2011. "On the Predictability of Stock Prices: A Case for High and Low Prices," "Marco Fanno" Working Papers 0136, Dipartimento di Scienze Economiche "Marco Fanno".
    29. Cathy Ning & Dinghai Xu & Tony Wirjanto, 2010. "Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data," Working Papers 1001, University of Waterloo, Department of Economics, revised Jan 2010.
    30. Xie, Haibin & Qi, Nan & Wang, Shouyang, 2019. "A new variant of RealGARCH for volatility modeling," Finance Research Letters, Elsevier, vol. 28(C), pages 438-443.
    31. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    32. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
    33. Korkusuz, Burak & Kambouroudis, Dimos & McMillan, David G., 2023. "Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets," Finance Research Letters, Elsevier, vol. 55(PB).
    34. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    35. Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, vol. 30(C), pages 34-50.
    36. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    37. Svetlana Lapinova & Alexander Saichev & Maria Tarakanova, 2012. "Volatility estimation based on extremes of the bridge (in Russian)," Quantile, Quantile, issue 10, pages 73-90, December.
    38. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    39. Lin, Xiaoqiang & Fei, Fangyu & Wang, Yudong, 2011. "Analysis of the efficiency of the Shanghai stock market: A volatility perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3486-3495.
    40. Stefano Lovo & Philippe Raimbourg & Federica Salvadè, 2022. "Credit Rating Agencies, Information Asymmetry and US Bond Liquidity," Working Papers hal-03890565, HAL.
    41. Cathy Ning & Dinghai Xu & Tony Wirjanto, 2014. "Is Volatility Clustering of Asset Returns Asymmetric?," Working Papers 050, Ryerson University, Department of Economics.
    42. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    43. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
    44. Fianu, Emmanuel Senyo & Ahelegbey, Daniel Felix & Grossi, Luigi, 2022. "Modeling risk contagion in the Italian zonal electricity market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 656-679.
    45. Wei Kuang, 2021. "Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1398-1419, December.
    46. Alexander Subbotin & Thierry Chauveau & Kateryna Shapovalova, 2009. "Volatility Models: from GARCH to Multi-Horizon Cascades," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00390636, HAL.
    47. Chou, Ray Yeutien & Liu, Nathan, 2010. "The economic value of volatility timing using a range-based volatility model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2288-2301, November.
    48. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    49. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "Emerging versus developed volatility indices. The comparison of VIW20 and VIX indices," Working Papers 2009-11, Faculty of Economic Sciences, University of Warsaw.
    50. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 535-551.
    51. Sensoy, Ahmet & Uzun, Sevcan & Lucey, Brian M., 2021. "Commonality in FX liquidity: High-frequency evidence," Finance Research Letters, Elsevier, vol. 39(C).
    52. Vortelinos, Dimitrios I., 2010. "The properties of realized correlation: Evidence from the French, German and Greek equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 273-290, August.
    53. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    54. Bannouh, K. & van Dijk, D.J.C. & Martens, M.P.E., 2008. "Range-based covariance estimation using high-frequency data: The realized co-range," Econometric Institute Research Papers EI 2007-53, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    55. Marcel Aloy & Gilles de Truchis & Gilles Dufrénot & Benjamin Keddad, 2014. "Shift-Volatility Transmission in East Asian Equity Markets," AMSE Working Papers 1402, Aix-Marseille School of Economics, France, revised Mar 2014.
    56. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    57. de Vilder, Robin G. & Visser, Marcel P., 2007. "Volatility Proxies for Discrete Time Models," MPRA Paper 4917, University Library of Munich, Germany.
    58. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    59. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    60. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
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    63. Victor Bello Accioly & Beatriz Vaz de Melo Mendes, 2016. "Assessing the Impact of the Realized Range on the (E)GARCH Volatility: Evidence from Brazil," Brazilian Business Review, Fucape Business School, vol. 13(2), pages 1-26, March.
    64. Linlan Xiao & Vigdis Boasson & Sergey Shishlenin & Victoria Makushina, 2018. "Volatility forecasting: combinations of realized volatility measures and forecasting models," Applied Economics, Taylor & Francis Journals, vol. 50(13), pages 1428-1441, March.
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    1. Peter Exterkate & Dick Van Dijk & Christiaan Heij & Patrick J. F. Groenen, 2013. "Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(3), pages 193-214, April.
    2. Heij, Christiaan & Groenen, Patrick J.F. & van Dijk, Dick, 2007. "Forecast comparison of principal component regression and principal covariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3612-3625, April.

  48. Roger Lord & Remmert Koekkoek & Dick van Dijk, 2006. "A Comparison of Biased Simulation Schemes for Stochastic Volatility Models," Tinbergen Institute Discussion Papers 06-046/4, Tinbergen Institute, revised 07 Jun 2007.

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    44. Cui, Zhenyu & Lars Kirkby, J. & Nguyen, Duy, 2017. "A general framework for discretely sampled realized variance derivatives in stochastic volatility models with jumps," European Journal of Operational Research, Elsevier, vol. 262(1), pages 381-400.
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    64. Marcellino Gaudenzi & Michel Vellekoop, 2018. "Exact Solutions for Optimal Investment Strategies and Indifference Prices under Non-Differentiable Preferences," Papers 1809.11010, arXiv.org.
    65. C. Kaebe & J. Maruhn & E. Sachs, 2009. "Adjoint-based Monte Carlo calibration of financial market models," Finance and Stochastics, Springer, vol. 13(3), pages 351-379, September.
    66. Medvedev, Alexey & Scaillet, Olivier, 2010. "Pricing American options under stochastic volatility and stochastic interest rates," Journal of Financial Economics, Elsevier, vol. 98(1), pages 145-159, October.
    67. Christian Bayer & Chiheb Ben Hammouda & Ra'ul Tempone, 2021. "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing," Papers 2111.01874, arXiv.org, revised Jun 2022.
    68. Ralph Rudd & Thomas A. McWalter & Joerg Kienitz & Eckhard Platen, 2020. "Robust Product Markovian Quantization," Papers 2006.15823, arXiv.org.
    69. Campillo, Fabien & Joannides, Marc & Larramendy-Valverde, Irène, 2014. "Approximation of the Fokker–Planck equation of the stochastic chemostat," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 99(C), pages 37-53.
    70. Qiang Liu & Yuhan Jiao & Shuxin Guo, 2022. "GARCH pricing and hedging of VIX options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1039-1066, June.
    71. Lech A. Grzelak & Cornelis W. Oosterlee & Sacha Van Weeren, 2012. "Extension of stochastic volatility equity models with the Hull--White interest rate process," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 89-105, July.
    72. Buchner, Axel, 2015. "Equilibrium option pricing: A Monte Carlo approach," Finance Research Letters, Elsevier, vol. 15(C), pages 138-145.
    73. Escobar, Marcos & Fang, Lin, 2020. "Stochastic volatility models for the implied correlation index," Finance Research Letters, Elsevier, vol. 35(C).
    74. Nicola Bruti-Liberati, 2007. "Numerical Solution of Stochastic Differential Equations with Jumps in Finance," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2007.
    75. Calvet, Laurent E. & Fearnley, Marcus & Fisher, Adlai J. & Leippold, Markus, 2015. "What is beneath the surface? Option pricing with multifrequency latent states," Journal of Econometrics, Elsevier, vol. 187(2), pages 498-511.
    76. Simon Scheidegger & Adrien Treccani, 2021. "Pricing American Options under High-Dimensional Models with Recursive Adaptive Sparse Expectations [Telling from Discrete Data Whether the Underlying Continuous-Time Model Is a Diffusion]," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 258-290.
    77. Maya Briani & Lucia Caramellino & Giulia Terenzi & Antonino Zanette, 2019. "Numerical Stability Of A Hybrid Method For Pricing Options," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(07), pages 1-46, November.
    78. Andrei Cozma & Christoph Reisinger, 2017. "Strong order 1/2 convergence of full truncation Euler approximations to the Cox-Ingersoll-Ross process," Papers 1704.07321, arXiv.org, revised Oct 2018.
    79. Marcos Escobar‐Anel & Zhenxian Gong, 2020. "The mean‐reverting 4/2 stochastic volatility model: Properties and financial applications," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(5), pages 836-856, September.
    80. Mascagni Michael & Hin Lin-Yee, 2013. "Parallel pseudo-random number generators: A derivative pricing perspective with the Heston stochastic volatility model," Monte Carlo Methods and Applications, De Gruyter, vol. 19(2), pages 77-105, July.
    81. F. Antonelli & A. Ramponi & S. Scarlatti, 2010. "Exchange option pricing under stochastic volatility: a correlation expansion," Review of Derivatives Research, Springer, vol. 13(1), pages 45-73, April.
    82. Antonelli, Fabio & Ramponi, Alessandro & Scarlatti, Sergio, 2022. "Approximate value adjustments for European claims," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1149-1161.
    83. Wenbin Hu & Junzi Zhou, 2017. "Backward simulation methods for pricing American options under the CIR process," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1683-1695, November.
    84. Lenkšas, A. & Mackevičius, V., 2015. "Weak approximation of Heston model by discrete random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 113(C), pages 1-15.
    85. Kay Giesecke & Dmitry Smelov, 2013. "Exact Sampling of Jump Diffusions," Operations Research, INFORMS, vol. 61(4), pages 894-907, August.
    86. João Pedro Vidal Nunes & Tiago Ramalho Viegas Alcaria, 2016. "Valuation of forward start options under affine jump-diffusion models," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 727-747, May.
    87. Simon J. A. Malham & Jiaqi Shen & Anke Wiese, 2020. "Series expansions and direct inversion for the Heston model," Papers 2008.08576, arXiv.org, revised Jan 2021.
    88. C'onall Kelly & Gabriel J. Lord, 2021. "An adaptive splitting method for the Cox-Ingersoll-Ross process," Papers 2112.09465, arXiv.org, revised Feb 2023.
    89. Renata Rendek, 2013. "Modeling Diversified Equity Indices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2013.
    90. del Baño Rollin, Sebastian & Ferreiro-Castilla, Albert & Utzet, Frederic, 2010. "On the density of log-spot in the Heston volatility model," Stochastic Processes and their Applications, Elsevier, vol. 120(10), pages 2037-2063, September.
    91. Goudenège, Ludovic & Molent, Andrea & Zanette, Antonino, 2022. "Moving average options: Machine learning and Gauss-Hermite quadrature for a double non-Markovian problem," European Journal of Operational Research, Elsevier, vol. 303(2), pages 958-974.
    92. Dupret, Jean-Loup & Barbarin, Jérôme & Hainaut, Donatien, 2021. "Impact of rough stochastic volatility models on long-term life insurance pricing," LIDAM Discussion Papers ISBA 2021017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  49. Ravazzolo, F. & van Dijk, D.J.C. & Paap, R. & Franses, Ph.H.B.F., 2006. "Bayesian Model Averaging in the Presence of Structural Breaks," Econometric Institute Research Papers EI 2006-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Schrimpf, Andreas, 2008. "International Stock Return Predictability Under Model Uncertainty," ZEW Discussion Papers 08-048, ZEW - Leibniz Centre for European Economic Research.
    2. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    3. Massimo Guidolin & Francesco Ravazzolo & Andrea Tortora, 2014. "Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 477-523, November.
    4. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    5. Guidolin, Massimo & Ravazzolo, Francesco & Tortora, Andrea Donato, 2013. "Alternative econometric implementations of multi-factor models of the U.S. financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(2), pages 87-111.
    6. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    7. Massimo Guidolin & Francesco Ravazzolo & Andrea Donato Tortora, 2011. "Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate. Evidence from Multi-Factor Asset Pricing Models of REIT Returns," Working Papers 416, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. Ravazzolo, F. & van Dijk, H.K. & Verbeek, M.J.C.M., 2007. "Predictive gains from forecast combinations using time-varying model weights," Econometric Institute Research Papers EI 2007-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    10. Francesco Ravazzolo & Marco J. Lombardi, 2012. "Oil price density forecasts: Exploring the linkages with stock markets," Working Papers No 3/2012, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Ravazzolo, F., 2007. "Evaluating real-time forecasts in real-time," Econometric Institute Research Papers EI 2007-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    13. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis.

  50. Michiel de Pooter & Martin Martens & Dick van Dijk, 2005. "Predicting the Daily Covariance Matrix for S&P 100 Stocks using Intraday Data - But which Frequency to use?," Tinbergen Institute Discussion Papers 05-089/4, Tinbergen Institute, revised 03 Jan 2006.

    Cited by:

    1. Audrino, Francesco & Fengler, Matthias, 2013. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Economics Working Paper Series 1311, University of St. Gallen, School of Economics and Political Science.
    2. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    3. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    4. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    5. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    6. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    7. Lucey, Brian & Sevic, Aleksandar, 2010. "Investigating the determinants of banking coexceedances in Europe in the summer of 2008," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(3), pages 275-283, July.
    8. Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
    9. Walid Bakry & Audil Rashid & Somar Al-Mohamad & Nasser El-Kanj, 2021. "Bitcoin and Portfolio Diversification: A Portfolio Optimization Approach," JRFM, MDPI, vol. 14(7), pages 1-24, June.
    10. LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," LIDAM Discussion Papers CORE 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Post-Print hal-01982032, HAL.
    12. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    13. Vortelinos, Dimitrios I., 2013. "Portfolio analysis of intraday covariance matrix in the Greek equity market," Research in International Business and Finance, Elsevier, vol. 27(1), pages 66-79.
    14. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    15. Vincenzo Candila, 2013. "A Comparison of the Forecasting Performances of Multivariate Volatility Models," Working Papers 3_228, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    16. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2009. "Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading," Global COE Hi-Stat Discussion Paper Series gd08-037, Institute of Economic Research, Hitotsubashi University.
    17. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.
    19. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    20. Yu‐Lun Chen & Yin‐Feng Gau, 2022. "The information effect of order flows in foreign currency futures and spot markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1549-1572, August.
    21. Oguzhan Cepni & Rangan Gupta & I. Ethem Guney & M. Hasan Yilmaz, 2019. "Forecasting Local Currency Bond Risk Premia of Emerging Markets: The Role of Cross-Country Macro-Financial Linkages," Working Papers 201957, University of Pretoria, Department of Economics.
    22. Tim Bollerslev & Morten Ø. Nielsen & Per Houmann Frederiksen & Torben G. Andersen, 2008. "Continuous-time Models, Realized Volatilities, And Testable Distributional Implications For Daily Stock Returns," Working Paper 1173, Economics Department, Queen's University.
    23. Santos, André Alves Portela & Ferreira, Alexandre R., 2017. "On the choice of covariance specifications for portfolio selection problems," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(1), May.
    24. Qianqiu Liu, 2009. "On portfolio optimization: How and when do we benefit from high-frequency data?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 560-582.
    25. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    26. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    27. Maria Elvira Mancino & Simona Sanfelici, 2011. "Covariance Estimation and Dynamic Asset-Allocation under Microstructure Effects via Fourier Methodology," Palgrave Macmillan Books, in: Greg N. Gregoriou & Razvan Pascalau (ed.), Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures, chapter 1, pages 3-32, Palgrave Macmillan.
    28. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model," Working Papers on Finance 1211, University of St. Gallen, School of Finance.
    29. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    30. Chan, Kam Fong & Powell, John G. & Treepongkaruna, Sirimon, 2014. "Currency jumps and crises: Do developed and emerging market currencies jump together?," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 132-157.
    31. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, vol. 147(1), pages 34-46, November.
    32. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    33. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    34. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    35. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    36. Sanfelici Simona & Uboldi Adamo, 2014. "Assessing the quality of volatility estimators via option pricing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(2), pages 1-22, April.
    37. Ciciretti, Vito & Bucci, Andrea, 2023. "Building optimal regime-switching portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    38. Jurkatis, Simon, 2020. "Inferring trade directions in fast markets," Bank of England working papers 896, Bank of England.
    39. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    40. Vortelinos, Dimitrios I., 2015. "The Greek equity market in European equity portfolios," Economic Modelling, Elsevier, vol. 49(C), pages 144-153.
    41. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    42. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2011. "The Merit of High-Frequency Data in Portfolio Allocation," SFB 649 Discussion Papers SFB649DP2011-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    43. Sharma, Prateek & Vipul,, 2015. "Performance of risk-based portfolios under different market conditions: Evidence from India," Research in International Business and Finance, Elsevier, vol. 34(C), pages 397-411.
    44. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    45. Wang, Jiazhen & Jiang, Yuexiang & Zhu, Yanjian & Yu, Jing, 2020. "Prediction of volatility based on realized-GARCH-kernel-type models: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 91(C), pages 428-444.
    46. Haugom, Erik & Lien, Gudbrand & Veka, Steinar & Westgaard, Sjur, 2014. "Covariance estimation using high-frequency data: Sensitivities of estimation methods," Economic Modelling, Elsevier, vol. 43(C), pages 416-425.
    47. Kalotychou, Elena & Staikouras, Sotiris K. & Zhao, Gang, 2014. "The role of correlation dynamics in sector allocation," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 1-12.

  51. Hafner, C.M. & van Dijk, D.J.C. & Franses, Ph.H.B.F., 2005. "Semi-Parametric Modelling of Correlation Dynamics," Econometric Institute Research Papers EI 2005-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH model," SSE/EFI Working Paper Series in Economics and Finance 0652, Stockholm School of Economics.
    2. Herwartz, Helmut & Golosnoy, Vasyl, 2007. "Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance," Economics Working Papers 2007-23, Christian-Albrechts-University of Kiel, Department of Economics.
    3. Feng, Yuanhua, 2006. "A local dynamic conditional correlation model," MPRA Paper 1592, University Library of Munich, Germany.
    4. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Multivariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 669, Stockholm School of Economics, revised 18 Jan 2008.
    5. Philippe Charlot & Vêlayoudom Marimoutou, 2008. "Hierarchical hidden Markov structure for dynamic correlations: the hierarchical RSDC model," Working Papers halshs-00285866, HAL.
    6. Wasel Shadat & Chris Orme, 2011. "An investigation of parametric tests of CCC assumption," Economics Discussion Paper Series 1109, Economics, The University of Manchester.

  52. van der Hart, J. & de Zwart, G.J. & van Dijk, D.J.C., 2005. "The Success Of Stock Selection Strategies In Emerging Markets: Is It Risk Or Behavioral Bias?," ERIM Report Series Research in Management ERS-2005-012-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    2. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    3. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    4. de Groot, W.A. & Pang, J. & Swinkels, L.A.P., 2012. "The Cross-Section of Stock Returns in Frontier Emerging Markets," ERIM Report Series Research in Management ERS-2012-012-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Blitz, David & Pang, Juan & van Vliet, Pim, 2013. "The volatility effect in emerging markets," Emerging Markets Review, Elsevier, vol. 16(C), pages 31-45.
    6. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    7. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    8. Galariotis, Emilios C., 2010. "What should we know about momentum investing? The case of the Australian Security Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 18(4), pages 369-389, September.
    9. Hanauer, Matthias X. & Lauterbach, Jochim G., 2019. "The cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 38(C), pages 265-286.
    10. I-Cheng Yeh & Yi-Cheng Liu, 2020. "Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-28, December.
    11. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    12. Huij, Joop & Post, Thierry, 2011. "On the performance of emerging market equity mutual funds," Emerging Markets Review, Elsevier, vol. 12(3), pages 238-249, September.
    13. Teplova, Tamara & Mikova, Evgeniya & Nazarov, Nikolai, 2017. "Stop losses momentum strategy: From profit maximization to risk control under White’s Bootstrap Reality Check," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 240-258.
    14. Brown, Stephen & Ghon Rhee, S. & Zhang, Liang, 2008. "The return to value in Asian stock markets," Emerging Markets Review, Elsevier, vol. 9(3), pages 194-205, September.
    15. Emilios C. C Galariotis, 2010. "What should investors know about the stability of momentum investing and its riskiness? The case of the Australian Security Exchange," Post-Print hal-00917587, HAL.
    16. Conover, C. Mitchell & Jensen, Gerald R. & Johnson, Robert R. & Szakmary, Andrew C., 2017. "Emerging markets: Is the trend still your friend?," Global Finance Journal, Elsevier, vol. 32(C), pages 128-148.
    17. Waszczuk, Antonina, 2013. "A risk-based explanation of return patterns—Evidence from the Polish stock market," Emerging Markets Review, Elsevier, vol. 15(C), pages 186-210.
    18. de Zwart, G.J. & van Dijk, D.J.C., 2008. "The Inefficient Use of Macroeconomic Information in Analysts' Earnings Forecasts in Emerging Markets," ERIM Report Series Research in Management ERS-2008-007-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  53. Dick van Dijk & Haris Munandar & Christian M. Hafner, 2005. "The Euro Introduction and Non-Euro Currencies," Tinbergen Institute Discussion Papers 05-044/4, Tinbergen Institute, revised 08 Jun 2006.

    Cited by:

    1. Marie Brière & Ombretta Signori, 2009. "Do Inflation‐Linked Bonds Still Diversify?," European Financial Management, European Financial Management Association, vol. 15(2), pages 279-297, March.
    2. Saart, Patrick W. & Xia, Yingcun, 2022. "Functional time series approach to analyzing asset returns co-movements," Journal of Econometrics, Elsevier, vol. 229(1), pages 127-151.
    3. Dao, Thong M. & McGroarty, Frank & Urquhart, Andrew, 2019. "The Brexit vote and currency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 153-164.
    4. C S Savva & D R Osborn & L Gill, 2005. "Spillovers and Correlations between US and Major European Stock Markets: The Role of the Euro," Economics Discussion Paper Series 0515, Economics, The University of Manchester.
    5. Gaetano, D'Adamo, 2009. "Measuring exchange rate flexibility in Europe," MPRA Paper 26612, University Library of Munich, Germany.
    6. Mohini GUPTA & Purwa SRIVASTAVA & Amritkant MISHRA & Malayaranjan SAHOO, 2021. "Time-varying volatility spillover of foreign exchange rate in three Asian markets: Based on DCC-GARCH approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(629), W), pages 105-120, Winter.
    7. Denise R. Osborn & Christos S. Savva & Len Gill, 2008. "Periodic Dynamic Conditional Correlations between Stock Markets in Europe and the US," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 307-325, Summer.
    8. Kühl, Michael, 2009. "Excess comovements between the Euro/US dollar and British pound/US dollar exchange rates," University of Göttingen Working Papers in Economics 89, University of Goettingen, Department of Economics.
    9. Nicolas Koch, 2014. "Dynamic linkages among carbon, energy and financial markets: a smooth transition approach," Applied Economics, Taylor & Francis Journals, vol. 46(7), pages 715-729, March.
    10. Christos Savva & Denise R Osborn & Len Gill, 2005. "Volatility, spillover Effects and Correlations in US and Major European Markets," Money Macro and Finance (MMF) Research Group Conference 2005 23, Money Macro and Finance Research Group.
    11. Łukasz Goczek & Dagmara Mycielska, 2014. "Monetary policy and nominal convergence in CEE countries with inflation targeting," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 38.
    12. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2013. "Predicting Covariance Matrices with Financial Conditions Indexes," Tinbergen Institute Discussion Papers 13-113/III, Tinbergen Institute.
    13. Kleinbrod, Vincent M. & Li, Xiao-Ming, 2017. "Order flow and exchange rate comovement," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 199-215.
    14. Savva, Christos S., 2009. "International stock markets interactions and conditional correlations," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 645-661, October.
    15. Lean, Hooi Hooi & Teng, Kee Tuan, 2013. "Integration of world leaders and emerging powers into the Malaysian stock market: A DCC-MGARCH approach," Economic Modelling, Elsevier, vol. 32(C), pages 333-342.
    16. Łukasz Goczek & Dagmara Mycielska, 2019. "Actual monetary policy independence in a small open economy: the Polish perspective," Empirical Economics, Springer, vol. 56(2), pages 499-522, February.
    17. D'Adamo, Gaetano, 2010. "Estimating Central Bank preferences in a small open economy: Sweden 1995-2009," MPRA Paper 26575, University Library of Munich, Germany.
    18. Nektarios Aslanidis & Christos S. Savva, 2011. "Are There Still Portfolio Diversification Benefits In Eastern Europe? Aggregate Versus Sectoral Stock Market Data," Manchester School, University of Manchester, vol. 79(6), pages 1323-1352, December.
    19. Jessica Leutert, 2018. "The Swiss franc safety premium," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-21, December.

  54. Giordani, P. & Kohn, R. & van Dijk, D.J.C., 2005. "A unified approach to nonlinearity, structural change and outliers," Econometric Institute Research Papers EI 2005-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Pierre Perron & Tatsuma Wada, 2015. "Measuring Business Cycles with Structural Breaks and Outliers: Applications to International Data," Boston University - Department of Economics - Working Papers Series wp2015-016, Boston University - Department of Economics.
    2. Geweke, John & Jiang, Yu, 2011. "Inference and prediction in a multiple-structural-break model," Journal of Econometrics, Elsevier, vol. 163(2), pages 172-185, August.
    3. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2008. "Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation," Centre for Growth and Business Cycle Research Discussion Paper Series 109, Economics, The University of Manchester.
    4. Markus Jochmann, 2015. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 537-558, May.
    5. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2013. "Real-Time Inflation Forecasting in a Changing World," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 29-44, January.
    6. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    7. GwanSeon Kim & Tyler Mark, 2017. "Impacts of corn price and imported beef price on domestic beef price in South Korea," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 5(1), pages 1-13, December.
    8. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. Van Dijk & Marno Verbeek, 2010. "Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 251-269.
    9. Siok Kun Sek, 2023. "A new look at asymmetric effect of oil price changes on inflation: Evidence from Malaysia," Energy & Environment, , vol. 34(5), pages 1524-1547, August.
    10. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
    11. Fiorentini, G. & Planas, C. & Rossi, A., 2012. "The marginal likelihood of dynamic mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2650-2662.
    12. Joshua C.C. Chan & Garry Koop & Roberto Leon Gonzales & Rodney W. Strachan, 2010. "Time Varying Dimension Models," ANU Working Papers in Economics and Econometrics 2010-523, Australian National University, College of Business and Economics, School of Economics.
    13. Rimstad, Kjartan & Omre, Henning, 2013. "Approximate posterior distributions for convolutional two-level hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 187-200.
    14. Bernardi, Mauro & Della Corte, Giuseppe & Proietti, Tommaso, 2008. "Extracting the Cyclical Component in Hours Worked: a Bayesian Approach," MPRA Paper 8967, University Library of Munich, Germany.
    15. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    16. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    17. Grossi, Luigi & Laurini, Fabrizio, 2009. "A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2251-2263, April.
    18. Altansukh, Gantungalag & Becker, Ralf & Bratsiotis, George J. & Osborn, Denise R., 2017. "What is the Globalisation of Inflation?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74, pages 1-27.
    19. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
    20. Perron, Pierre & Wada, Tatsuma, 2009. "Let's take a break: Trends and cycles in US real GDP," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 749-765, September.
    21. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    22. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2011. "Multi-regime nonlinear capital asset pricing models," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1421-1438, April.
    23. Sjoerd van den Hauwe & Richard Paap & Dick J.C. van Dijk, 2011. "An Alternative Bayesian Approach to Structural Breaks in Time Series Models," Tinbergen Institute Discussion Papers 11-023/4, Tinbergen Institute.
    24. Tatsuma Wada & Pierre Perron, 2005. "An Alternative Trend-Cycle Decomposition using a State Space Model with Mixtures of Normals: Specifications and Applications to International Data," Boston University - Department of Economics - Working Papers Series WP2005-44, Boston University - Department of Economics.
    25. John M. Maheu & Yong Song, 2012. "A New Structural Break Model with Application to Canadian Inflation Forecasting," Working Paper series 27_12, Rimini Centre for Economic Analysis.
    26. Dr. James Mitchell, 2009. "Macro Modelling with Many Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 337, National Institute of Economic and Social Research.
    27. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    28. Jaehee Kim & Chulwoo Jeong, 2016. "A Bayesian multiple structural change regression model with autocorrelated errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1690-1705, July.
    29. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
    30. Massimo Guidolin & Francesco Ravazzolo & Andrea Donato Tortora, 2011. "Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate. Evidence from Multi-Factor Asset Pricing Models of REIT Returns," Working Papers 416, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    31. Tatsuma Wada & Pierre Perron, 2006. "State Space Model with Mixtures of Normals: Specifications and Applications to International Data," Boston University - Department of Economics - Working Papers Series WP2006-029, Boston University - Department of Economics.
    32. Candelon, Bertrand & Metiu, Norbert & Straetmans, Stefan, 2013. "Disentangling economic recessions and depressions," Discussion Papers 43/2013, Deutsche Bundesbank.
    33. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    34. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    35. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
    36. Jiawen Xu & Pierre Perron, 2023. "Forecasting in the presence of in-sample and out-of-sample breaks," Empirical Economics, Springer, vol. 64(6), pages 3001-3035, June.
    37. Daniele Bianchi & Massimo Guidolin & Francesco Ravazzolo, 2015. "Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section," Working Papers 550, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    38. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2010. "Falling and explosive, dormant, and rising markets via multiple‐regime financial time series models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 28-49, January.
    39. Maddalena Cavicchioli, 2016. "Weak VARMA representations of regime-switching state-space models," Statistical Papers, Springer, vol. 57(3), pages 705-720, September.
    40. Planas, C. & Roeger, W. & Rossi, A., 2013. "The information content of capacity utilization for detrending total factor productivity," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 577-590.
    41. Shirinbakhsh, Shamsollah & Moghaddas Bayat, Maryam, 2011. "An Evaluation of Asymmetric and Symmetric Effects of Oil Exports Shocks on Non-Tradable Sector of Iranian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 106-124, March.
    42. Ngene, Geoffrey M. & Mungai, Ann Nduati, 2022. "Stock returns, trading volume, and volatility: The case of African stock markets," International Review of Financial Analysis, Elsevier, vol. 82(C).
    43. Mahadeva, Lavan, 2007. "Monetary Policy and Data Uncertainty: A Case Study of Distribution, Hotels and Catering Growth," Discussion Papers 19, Monetary Policy Committee Unit, Bank of England.
    44. Gantungalag Altansukh & Ralf Becker & George Bratsiotis & Denise R. Osborn, 2018. "Structural Breaks in International Inflation Linkages for OECD Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 240, Economics, The University of Manchester.
    45. Kim, Jaeho, 2015. "Bayesian Inference in a Non-linear/Non-Gaussian Switching State Space Model: Regime-dependent Leverage Effect in the U.S. Stock Market," MPRA Paper 67153, University Library of Munich, Germany.
    46. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    47. Nalan Basturk & Cem Cakmakli & Pinar Ceyhan & Herman K. van Dijk, 2013. "Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series," Tinbergen Institute Discussion Papers 13-011/III, Tinbergen Institute.
    48. Cathy Chen & Richard Gerlach, 2013. "Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity," Computational Statistics, Springer, vol. 28(3), pages 1103-1131, June.
    49. Gary Koop & Simon Potter, 2010. "A flexible approach to parametric inference in nonlinear and time varying time series models," Post-Print hal-00732535, HAL.
    50. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    51. Jiawen Xu & Pierre Perron, 2017. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series WP2018-014, Boston University - Department of Economics, revised Nov 2018.
    52. Lin, Edward M.H. & Chen, Cathy W.S. & Gerlach, Richard, 2012. "Forecasting volatility with asymmetric smooth transition dynamic range models," International Journal of Forecasting, Elsevier, vol. 28(2), pages 384-399.
    53. Bernardi Mauro & Della Corte Giuseppe & Proietti Tommaso, 2011. "Extracting the Cyclical Component in Hours Worked," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-28, May.
    54. Luigi Grossi & Fany Nan, 2018. "The influence of renewables on electricity price forecasting: a robust approach," Working Papers 2018/10, Institut d'Economia de Barcelona (IEB).
    55. Johnson, Lorne D. & Sakoulis, Georgios, 2008. "Maximizing equity market sector predictability in a Bayesian time-varying parameter model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3083-3106, February.
    56. Wali, Muammer & Chan, Felix & Manzur, Meher, 2017. "Nonlinear dependence in exchange rate returns: How do emerging Asian currencies compare with major currencies?," Journal of Asian Economics, Elsevier, vol. 50(C), pages 62-72.
    57. Pitt, Michael K. & Silva, Ralph dos Santos & Giordani, Paolo & Kohn, Robert, 2012. "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter," Journal of Econometrics, Elsevier, vol. 171(2), pages 134-151.

  55. Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2005. "Forecast comparison of principal component regression and principal covariate regression," Econometric Institute Research Papers EI 2005-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Cornillon, P.-A. & Imam, W. & Matzner-Lober, E., 2008. "Forecasting time series using principal component analysis with respect to instrumental variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1269-1280, January.
    2. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    3. Mestekemper, Thomas & Windmann, Michael & Kauermann, Göran, 2010. "Functional hourly forecasting of water temperature," International Journal of Forecasting, Elsevier, vol. 26(4), pages 684-699, October.
    4. Tsay, Ruey S. & Ando, Tomohiro, 2012. "Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3345-3365.
    5. Peter Exterkate & Dick Van Dijk & Christiaan Heij & Patrick J. F. Groenen, 2013. "Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(3), pages 193-214, April.
    6. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237, April.
    7. Guidolin, Massimo & Hyde, Stuart, 2012. "Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3546-3566.
    8. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2008. "Forecasting binary longitudinal data by a functional PC-ARIMA model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3187-3197, February.
    9. Simon Lineu Umbach, 2020. "Forecasting with supervised factor models," Empirical Economics, Springer, vol. 58(1), pages 169-190, January.
    10. Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2006. "Time series forecasting by principal covariate regression," Econometric Institute Research Papers EI 2006-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2008. "Discussion of different logistic models with functional data. Application to Systemic Lupus Erythematosus," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 151-163, September.

  56. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.

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    1. António Afonso & Michael G. Arghyrou & María Dolores Gadea & Alexandros Kontonikas, 2017. ""Whatever it takes" to resolve the European sovereign debt crisis? Bond pricing regime switches and monetary policy effects," Working Papers REM 2017/02, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Jinzhao Chen, 2015. "Interprovincial Competitiveness and Economic Growth: Evidence from Chinese Provincial Data (1992–2008)," Pacific Economic Review, Wiley Blackwell, vol. 20(3), pages 388-414, August.
    3. Cécile Couharde & Rémi Generoso, 2015. "Hydro-climatic thresholds and economic growth reversals in developing countries: an empirical investigation," Working Papers hal-04141392, HAL.
    4. Gilbert Colletaz & Christophe Hurlin, 2006. "Threshold Effects of the Public Capital Productivity : An International Panel Smooth Transition Approach," Working Papers halshs-00008056, HAL.
    5. Hermann Ndoya hegueu & Aristophane Djeufack dongmo, 2021. "Urbanization, Governance and Informal Economy: an African Tale," Economics Bulletin, AccessEcon, vol. 41(3), pages 1525-1540.
    6. Virginie Coudert & Valérie Mignon, 2013. "The ‘Forward Premium Puzzle’ and the Sovereign Default risk," Post-Print hal-01385839, HAL.
    7. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    8. Simplice A. Asongu & Nicholas M. Odhiambo, 2019. "Insurance Policy Thresholds for Economic Growth in Africa," CEREDEC Working Papers 19/037, Centre de Recherche pour le Développement Economique (CEREDEC).
    9. Virginie Coudert & Cécile Couharde & Valérie Mignon, 2013. "Pegging emerging currencies in the face of dollar swings," Post-Print hal-01411740, HAL.
    10. Mensi, Walid & Hammoudeh, Shawkat & Tiwari, Aviral Kumar & Al-Yahyaee, Khamis Hamed, 2020. "Impact of Islamic banking development and major macroeconomic variables on economic growth for Islamic countries: Evidence from panel smooth transition models," Economic Systems, Elsevier, vol. 44(1).
    11. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets: Evidence from the ECB survey of professional forecasters," Discussion Papers 311, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    12. Joshua Aizenman & Nan Geng, 2009. "Adjustment of State Owned and Foreign-Funded Enterprises in China to Economic Reforms,1980s-2007: a logistic smooth transition regression (LSTR) approach," NBER Working Papers 15274, National Bureau of Economic Research, Inc.
    13. Yıldırım, Durmuş Çağrı & Esen, Ömer & Yıldırım, Seda, 2022. "The nonlinear effects of environmental innovation on energy sector-based carbon dioxide emissions in OECD countries," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    14. Tue Gørgens & Allan H. Würtz, 2019. "Threshold Regression with Endogeneity for Short Panels," Econometrics, MDPI, vol. 7(2), pages 1-8, May.
    15. Gabriel Gomes, 2016. "On the impact of dollar movements on oil currencies," Working Papers 2016-11, CEPII research center.
    16. Anne-Laure Delatte & Julien Fouquau, 2012. "What Drove the Massive Hoarding of International Reserves in Emerging Economies? A Time-Varying Approach," Post-Print hal-01410598, HAL.
    17. Coudert, Virginie & Couharde, Cécile & Mignon, Valérie, 2015. "On the impact of volatility on the real exchange rate – terms of trade nexus: Revisiting commodity currencies," Journal of International Money and Finance, Elsevier, vol. 58(C), pages 110-127.
    18. Po-Chin Wu & Chung-Chih Lee, 2018. "The non-linear impact of monetary policy on international reserves: macroeconomic variables nexus," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 165-185, February.
    19. K. Peren Arin & Emin Gahramanov & Tolga Omay & Mehmet A. Ulubasoglu, 2019. "A tale of two taxes: State-dependency of tax policy," CAMA Working Papers 2019-68, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    20. Simplice A. Asongu & Nicholas M. Odhiambo, 2020. "The role of Globalization in Modulating the Effect of Environmental Degradation on Inclusive Human Development," Working Papers of the African Governance and Development Institute. 20/015, African Governance and Development Institute..
    21. Simplice A. Asongu & Jean R. F. K. Bouanza & Peter Agyemang-Mintah, 2024. "Globalization in Lifelong Gender Inclusive Education for Structural Transformation in Africa," Working Papers of the African Governance and Development Institute. 24/013, African Governance and Development Institute..
    22. Couharde, Cécile & Sallenave, Audrey, 2013. "How do currency misalignments’ threshold affect economic growth?," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 106-120.
    23. Cécile Couharde & Serge Rey & Audrey Sallenave, 2016. "External debt and real exchange rates’ adjustment in the euro area: new evidence from a nonlinear NATREX model," Applied Economics, Taylor & Francis Journals, vol. 48(11), pages 966-986, March.
    24. Simplice A. Asongu & Hillary C. Ezeaku, 2020. "Aid Grants vs. Technical Cooperation Grants: Implications for Inclusive Growth in Sub-Saharan Africa, 1984-2018," Working Papers of the African Governance and Development Institute. 20/091, African Governance and Development Institute..
    25. Jeannine Bailliu & Doga Bilgin & Kun Mo & Kurt Niquidet & Benjamin Sawatzky, 2019. "Global Commodity Markets and Rebalancing in China: The Case of Copper," Discussion Papers 2019-3, Bank of Canada.
    26. Arghyrou, Michael G & Gadea, Mar a Dolores, 2019. "Private bank deposits and macro/fiscal risk in the euro-area," Cardiff Economics Working Papers E2019/6, Cardiff University, Cardiff Business School, Economics Section.
    27. Natalia Zugravu-Soilita & Vincent Geronimi & Jessy Tsang & Christine Le Gargasson, 2020. "Promoting heritage for a sustainable development: the case of tourism in the island economies [Promouvoir le patrimoine pour un développement soutenable : le cas du tourisme dans les économies insu," Post-Print hal-03709168, HAL.
    28. Miao, Ke & Su, Liangjun & Wang, Wendun, 2020. "Panel threshold regressions with latent group structures," Journal of Econometrics, Elsevier, vol. 214(2), pages 451-481.
    29. Cho, Dooyeon, 2015. "The role of covered interest parity in explaining the forward premium anomaly within a nonlinear panel framework," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 229-238.
    30. Po-Chin Wu & Chia-Jui Chang, 2017. "Nonlinear impacts of debt ratio and term spread on inward FDI performance persistence," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(3), pages 369-388, December.
    31. Hela Namouri & Fredj Jawadi & Zied Ftiti & Néjib Hachicha, 2018. "Threshold effect in the relationship between investor sentiment and stock market returns: a PSTR specification," Applied Economics, Taylor & Francis Journals, vol. 50(5), pages 559-573, January.
    32. Alexandru Minea & Patrick Villieu, 2007. "Investissement public et effets non linéaires des déficits budgétaires," Post-Print halshs-00261101, HAL.
    33. Zhang, Mingming & Zhang, Shichang & Lee, Chien-Chiang & Zhou, Dequn, 2021. "Effects of trade openness on renewable energy consumption in OECD countries: New insights from panel smooth transition regression modelling," Energy Economics, Elsevier, vol. 104(C).
    34. JUDE, Cristina & LEVIEUGE, Gregory, 2013. "Growth effect of FDI in developing economies: The role of institutional quality," MPRA Paper 49321, University Library of Munich, Germany.
    35. Julien Fouquau & Christophe Hurlin & Isabelle Rabaud, 2008. "The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach," Post-Print halshs-00292472, HAL.
    36. Ibrahim Ahamada & Dramane Coulibaly, 2011. "How does financial development influence the impact of remittances on growth volatility?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00629898, HAL.
    37. Combes, Jean-Louis & Minea, Alexandru & Sawadogo, Pegdéwendé Nestor, 2021. "Does the composition of government spending matter for government bond spreads?," Economic Modelling, Elsevier, vol. 96(C), pages 409-420.
    38. Po-Chin Wu & Shiao-Yen Liu & Sheng-Chieh Pan, 2014. "Nonlinear relationship between health care expenditure and its determinants: a panel smooth transition regression model," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(4), pages 713-729, November.
    39. Muhammad Khan, 2013. "Inflation and Sectoral Output Growth Variability in Bulgaria," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 55(4), pages 687-704, December.
    40. Delatte, Anne-Laure & Gex, Mathieu & López-Villavicencio, Antonia, 2012. "Has the CDS market influenced the borrowing cost of European countries during the sovereign crisis?," Journal of International Money and Finance, Elsevier, vol. 31(3), pages 481-497.
    41. Djeneba Doumbia, 2018. "The quest for pro-poor and inclusive growth: The role of governance," Working Papers 458, ECINEQ, Society for the Study of Economic Inequality.
    42. Jean-Pierre Allegret & Cécile Couharde & Dramane Coulibaly & Valérie Mignon, 2013. "Current Accounts and Oil Price Fluctuations in Oil-Exporting Countries: the Role of Financial Development," Working Papers 2013-19, CEPII research center.
    43. Liu, Bing & Yin, Weijun & Chen, Gang & Yao, Jing, 2023. "The threshold effect of climate risk and the non-linear role of climate policy uncertainty on insurance demand: Evidence from OECD countries," Finance Research Letters, Elsevier, vol. 55(PA).
    44. Anne-Laure Delatte & Julien Fouquau, 2011. "The determinants of International Reserves in the Emerging countries: a non linear approach," Post-Print hal-00822326, HAL.
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    299. Wang, Yu Shan, 2013. "Oil price effects on personal consumption expenditures," Energy Economics, Elsevier, vol. 36(C), pages 198-204.
    300. Wang, Zheng-Xin & Jv, Yue-Qi, 2022. "A smooth difference-in-differences model for assessing gradual policy effects: Revisiting the impact of banking deregulation on income distribution," Finance Research Letters, Elsevier, vol. 50(C).
    301. Po-Chin Wu & Shiao-Yen Liu & Sheng-Chieh Pan, 2014. "Does Misery Index Matter for the Persistence of Health Spending? Evidence from OECD Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 893-910, September.
    302. Hellwagner, Timon & Weber, Enzo, 2021. "Labour Market Adjustments to Population Decline," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242455, Verein für Socialpolitik / German Economic Association.
    303. Artur Tarassow, 2015. "Financial Investment Constraints. A Panel Threshold Application to German Firm Level Data," Macroeconomics and Finance Series 201405, University of Hamburg, Department of Socioeconomics.
    304. Geng-Nan Chiang & Wei-Ying Sung & Wen-Guu Lei, 2017. "Regime-Switching Effect of Tourism Specialization on Economic Growth in Asia Pacific Countries," Economies, MDPI, vol. 5(3), pages 1-14, June.
    305. Nicholas Apergis, 2015. "Economic freedom and income inequality: further evidence from 58 countries in the long-run," Financial Theory and Practice, Institute of Public Finance, vol. 39(4), pages 349-370.
    306. SIGUE, Moussa & SIRPE, Gnanderman, 2019. "Non-linear effects of investment in road infrastructure on the structural competitiveness of the economy: the case of Burkina Faso," MPRA Paper 96142, University Library of Munich, Germany.
    307. Le, Dieu Thanh & Park, Hail, 2020. "Valuation effects of capital inflows: Evidence from emerging market economies," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    308. Po-Chin Wu & Sheng-Chieh Pan & Xue-Ling Tai, 2015. "Non-linearity, persistence and spillover effects in stock returns: the role of the volatility index," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 597-613, August.
    309. He, Changli & Sandberg, Rickard, 2005. "Inference for Unit Roots in a Panel Smooth Transition Autoregressive Model where the Time Dimension is Fixed," SSE/EFI Working Paper Series in Economics and Finance 581, Stockholm School of Economics, revised 18 Feb 2005.
    310. Nicholas Apergis & Arusha Cooray, 2017. "Economic Freedom and Income Inequality: Evidence from a Panel of Global Economies— A Linear and a Non-Linear Long-Run Analysis," Manchester School, University of Manchester, vol. 85(1), pages 88-105, January.
    311. López-Villavicencio, Antonia & Mignon, Valérie, 2011. "On the impact of inflation on output growth: Does the level of inflation matter?," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 455-464, September.
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    313. Yu-Shan Chen & Chun-Yu Shih & Ching-Hsun Chang, 2014. "Explore the new relationship between patents and market value: a panel smooth transition regression (PSTR) approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1145-1159, February.
    314. Fang, Guochang & Chen, Gang & Yang, Kun & Yin, Weijun & Tian, Lixin, 2023. "Can green tax policy promote China's energy transformation?— A nonlinear analysis from production and consumption perspectives," Energy, Elsevier, vol. 269(C).
    315. Bernard Njindan Iyke, 2018. "The real effect of currency misalignment on productivity growth: evidence from middle-income economies," Empirical Economics, Springer, vol. 55(4), pages 1637-1659, December.
    316. Wu, Po-Chin & Liu, Shiao-Yen & Chen, Che-Ying, 2016. "Re-examining risk premiums in the Fama–French model: The role of investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 154-171.
    317. Wu, Po-Chin & Liu, Shiao-Yen & Hsiao, Juei-Ming & Huang, Tsai-Yuan, 2016. "Nonlinear and time-varying growth-tourism causality," Annals of Tourism Research, Elsevier, vol. 59(C), pages 45-59.
    318. Thanh, Su Dinh & Canh, Nguyen Phuc, 2020. "Taxation and capital formation: Non-linear effects and asymmetry between developing and developed countries," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    319. Nauro Campos & Ekaterina Glebkina & Menelaos Karanasos & Panagiotis Koutroumpis, 2023. "Financial Development, Political Instability, Trade Openness and Growth in Brazil: Evidence from a New Dataset, 1890-2003," Open Economies Review, Springer, vol. 34(4), pages 831-861, September.
    320. Lawrence Adu Asamoah, 2021. "Institutional Quality and Income Inequality in Developing Countries: A Dynamic Panel Threshold Analysis," Progress in Development Studies, , vol. 21(2), pages 123-143, April.
    321. Chiu, Yi-Bin, 2017. "Carbon dioxide, income and energy: Evidence from a non-linear model," Energy Economics, Elsevier, vol. 61(C), pages 279-288.
    322. Jian Chai & Limin Xing & Quanying Lu & Ting Liang & Kin Keung Lai & Shouyang Wang, 2016. "The Non-Linear Effect of Chinese Financial Developments on Energy Supply Structures," Sustainability, MDPI, vol. 8(10), pages 1-21, October.
    323. Mohsen Mehrara, 2011. "Pollution, Energy Consumption and Economic Growth: evidence from India, China and Brazil," Journal of Social and Development Sciences, AMH International, vol. 2(5), pages 233-242.
    324. Law, Siong Hook & Ng, Chee Hung & Kutan, Ali M. & Law, Zhi Kei, 2021. "Public debt and economic growth in developing countries: Nonlinearity and threshold analysis," Economic Modelling, Elsevier, vol. 98(C), pages 26-40.
    325. Mustafa Koroglu, 2019. "Growth and Debt: An Endogenous Smooth Coefficient Approach," JRFM, MDPI, vol. 12(1), pages 1-22, February.
    326. Weining LI & Sen ZHANG & Jing ZHANG, 2015. "Top Management Team Heterogeneity, Diversification, And Corporate Performance: A Panel Smooth Transition Regression Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 129-142, March.
    327. Tzu-Yi Yang & Chieh Liu & Yu-Tai Yang & Ssu-Han Chen, 2023. "The dynamic effect of trading between China and Taiwan under exchange rate fluctuations," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    328. Lee, Chien-Chiang & Chiu, Yi-Bin, 2012. "The impact of real income on insurance premiums: Evidence from panel data," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 246-260.
    329. Wang, Yongpei & Yan, Weilong & Komonpipat, Supak, 2019. "How does the capacity utilization of thermal power generation affect pollutant emissions? Evidence from the panel data of China's provinces," Energy Policy, Elsevier, vol. 132(C), pages 440-451.
    330. Lee, Chia-Hao & Chou, Pei-I, 2019. "Information dissemination and investors’ sensitivity," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 242-250.
    331. Tran, Ngan, 2018. "Debt threshold for fiscal sustainability assessment in emerging economies," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 375-394.

  57. de Pooter, M.D. & van Dijk, D.J.C., 2004. "Testing for changes in volatility in heteroskedastic time series - a further examination," Econometric Institute Research Papers EI 2004-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Vasiliki Chatzikonstanti, 2017. "Breaks and outliers when modelling the volatility of the U.S. stock market," Applied Economics, Taylor & Francis Journals, vol. 49(46), pages 4704-4717, October.
    2. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    3. Gebka, Bartosz & Wohar, Mark E., 2013. "Causality between trading volume and returns: Evidence from quantile regressions," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 144-159.
    4. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    5. Barassi, Marco & Horvath, Lajos & Zhao, Yuqian, 2018. "Change Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," MPRA Paper 87837, University Library of Munich, Germany.
    6. Ali Babikir & Rangan Gupta & Chance Mwabutwa & Emmanuel Owusu-Sekyere, 2010. "Structural Breaks and GARCH Models of Stock Return Volatility: The Case of South Africa," Working Papers 201030, University of Pretoria, Department of Economics.
    7. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    8. Bala A. Dahiru & Pam W. Jim & Kalu N. Nwonyuku, 2017. "Equity markets volatility dynamics in developed and newly emerging economies: EGARCH-with-skewed-t density approach," Economics Bulletin, AccessEcon, vol. 37(4), pages 2394-2412.
    9. Efe Çağlar Çağli & Pinar Evrim Mandaci & Pinar Hakan Kahyaoğlu, 2011. "Volatility Shifts and Persistence in Variance: Evidence from the Sector Indices of Istanbul Stock Exchange," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 4(3), pages 119-140, December.
    10. F. Dilvin Taşkin & Efe Çağlar Çağlı & Umut Halaç, 2016. "The impact of oil price shocks on the volatility of the Turkish stock market," International Journal of Accounting and Finance, Inderscience Enterprises Ltd, vol. 6(1), pages 1-23.
    11. Taewook Lee & Moosup Kim & Changryong Baek, 2015. "Tests for Volatility Shifts in Garch Against Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 127-153, March.
    12. Haejune Oh & Sangyeol Lee, 2019. "Modified residual CUSUM test for location-scale time series models with heteroscedasticity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1059-1091, October.
    13. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Reboredo, Juan Carlos & Wen, Xiaoqian, 2014. "Dependence of stock and commodity futures markets in China: Implications for portfolio investment," Emerging Markets Review, Elsevier, vol. 21(C), pages 183-200.
    14. Mofleh Alshogeathri & Jamel Jouini, 2017. "Linkages Between Equity and Global Food Markets: New Evidence from Including Structural Changes," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(3), pages 166-198, June.
    15. Vivian, Andrew & Wohar, Mark E., 2012. "Commodity volatility breaks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(2), pages 395-422.
    16. Badagian Baharian, Ana Laura & Kaiser Remiro, Regina & Peña, Daniel, 2013. "The change-point problem and segmentation of processes with conditional heteroskedasticity," DES - Working Papers. Statistics and Econometrics. WS ws131718, Universidad Carlos III de Madrid. Departamento de Estadística.

  58. Fok, D. & van Dijk, D.J.C. & Franses, Ph.H.B.F., 2004. "Forecasting aggregates using panels of nonlinear time series," Econometric Institute Research Papers EI 2004-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Oliveira, André Barbosa & Valls Pereira, Pedro Luiz, 2018. "Asset Allocation with Markovian Regime Switching: Efficient Frontier and Tangent Portfolio with Regime Switching," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(1), May.
    2. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    3. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    4. Chang, Tsangyao & Chiang, Gengnan, 2012. "Transitional Behavior of Government Debt Ratio on Growth: The Case of OECD Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 24-37, June.
    5. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Nina Vujanovic & Bruno Casella & Richard Bolwijn, . "Forecasting global FDI: a panel data approach," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.
    8. Allenby, Greg M., 2017. "Structural forecasts for marketing data," International Journal of Forecasting, Elsevier, vol. 33(2), pages 433-441.
    9. Kausik Chaudhuri & Saumitra N. Bhaduri, 2019. "Inflation Forecast: Just use the Disaggregate or Combine it with the Aggregate," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 331-343, June.
    10. Cai, Charlie X. & Kyaw, Khine & Zhang, Qi, 2012. "Stock index return forecasting: The information of the constituents," Economics Letters, Elsevier, vol. 116(1), pages 72-74.
    11. Oliveira, André Barbosa & Pereira, Pedro L. Valls, 2018. "Uncertainty times for portfolio selection at financial market," Textos para discussão 473, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

  59. Karen Watkins & Dick van Dijk & Jaap Spronk, 2004. "Macroeconomic Crisis and Individual Firm Performance: The Mexican Experience," Tinbergen Institute Discussion Papers 04-057/2, Tinbergen Institute.

    Cited by:

  60. D van Dijk & D R Osborn & M Sensier, 2004. "Testing for causality in variance in the presence of breaks," Centre for Growth and Business Cycle Research Discussion Paper Series 45, Economics, The University of Manchester.

    Cited by:

    1. Mario Reyna Cerecero & Diana Salazar Cavazos & Héctor Salgado Banda, 2009. "La curva de rendimiento y su relación con la actividad económica: una aplicación para México," Monetaria, CEMLA, vol. 0(3), pages 297-357, octubre-d.
    2. Tetsuji Tanaka & Jin Guo, 2020. "How does the self-sufficiency rate affect international price volatility transmissions in the wheat sector? Evidence from wheat-exporting countries," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    3. Gebka, Bartosz & Serwa, Dobromil, 2007. "Intra- and inter-regional spillovers between emerging capital markets around the world," Research in International Business and Finance, Elsevier, vol. 21(2), pages 203-221, June.
    4. Stolbov, Mikhail, 2014. "The causal linkages between sovereign CDS prices for the BRICS and major European economies," Economics Discussion Papers 2014-9, Kiel Institute for the World Economy (IfW Kiel).
    5. Korkmaz, Turhan & Çevik, Emrah İ. & Atukeren, Erdal, 2012. "Return and volatility spillovers among CIVETS stock markets," Emerging Markets Review, Elsevier, vol. 13(2), pages 230-252.
    6. Jin Guo & Tetsuji Tanaka, 2019. "Determinants of international price volatility transmissions: the role of self-sufficiency rates in wheat-importing countries," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-13, December.
    7. Stelios Bekiros & Massimiliano Marcellino, 2011. "The Multiscale Causal Dynamics of Foreign Exchange Markets," Economics Working Papers ECO2011/23, European University Institute.
    8. Ahmed, Walid M.A., 2018. "On the interdependence of natural gas and stock markets under structural breaks," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 149-161.
    9. Theologos Pantelidis, 2015. "Testing for causality in the presence of leading variables," Economics and Business Letters, Oviedo University Press, vol. 4(1), pages 17-29.
    10. Caporin, Massimiliano & Malik, Farooq, 2020. "Do structural breaks in volatility cause spurious volatility transmission?," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 60-82.
    11. Ahmed, Walid M.A., 2019. "Islamic and conventional equity markets: Two sides of the same coin, or not?," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 191-205.
    12. Grote, Claudia & Bertram, Philip, 2015. "A comparative Study of Volatility Breaks," Hannover Economic Papers (HEP) dp-558, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    13. Jin Guo & Tetsuji Tanaka, 2020. "Dynamic Transmissions and Volatility Spillovers between Global Price and U.S. Producer Price in Agricultural Markets," JRFM, MDPI, vol. 13(4), pages 1-20, April.
    14. Guo, Jin, 2018. "Co-movement of international copper prices, China's economic activity, and stock returns: Structural breaks and volatility dynamics," Global Finance Journal, Elsevier, vol. 36(C), pages 62-77.
    15. Gardebroek, Cornelis & Hernandez, Manuel A., 2013. "Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets," Energy Economics, Elsevier, vol. 40(C), pages 119-129.
    16. Soylu, Pınar Kaya & Güloğlu, Bülent, 2019. "Financial contagion and flight to quality between emerging markets and U.S. bond market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    17. Yang, Yao & Karali, Berna, 2022. "How far is too far for volatility transmission?," Journal of Commodity Markets, Elsevier, vol. 26(C).
    18. González, Mariano, 2016. "Asymmetric causality in-mean and in-variance among equity markets indexes," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 49-68.
    19. Okur, Mustafa & Cevik, Emrah Ismail, 2013. "Testing intraday volatility spillovers in Turkish capital markets: evidence from ISE," MPRA Paper 71477, University Library of Munich, Germany, revised 2013.
    20. Stelios Bekiros, 2014. "Timescale Analysis with an Entropy-Based Shift-Invariant Discrete Wavelet Transform," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 231-251, August.
    21. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2012. "International market links and volatility transmission," Journal of Econometrics, Elsevier, vol. 170(1), pages 117-141.
    22. Rodrigues, Paulo M.M. & Rubia, Antonio, 2007. "Testing for causality in variance under nonstationarity in variance," Economics Letters, Elsevier, vol. 97(2), pages 133-137, November.
    23. Grydaki, Maria & Bezemer, Dirk, 2013. "The role of credit in the Great Moderation: A multivariate GARCH approach," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4615-4626.
    24. Pablo Mendieta Ossio & Sergio Cerezo Aguirre & Javier Cossío Medinacelli, 2009. "¿La inflación está de vuelta en Sudamérica?. Choques exógenos, expectativas y credibilidad de la política monetaria," Revista de Análisis del BCB, Banco Central de Bolivia, vol. 11(1), pages 111-146, December.
    25. Dimitrios Kartsonakis-Mademlis & Nikolaos Dritsakis, 2022. "Asymmetric volatility transmission in Japanese stock market in the presence of structural breaks," The Japanese Economic Review, Springer, vol. 73(4), pages 647-677, October.
    26. Yıldırım, Durmuş Çağrı & Cevik, Emrah Ismail & Esen, Ömer, 2020. "Time-varying volatility spillovers between oil prices and precious metal prices," Resources Policy, Elsevier, vol. 68(C).
    27. Bezemer, Dirk J & Grydaki, Maria, 2012. "Mortgage Lending and the Great moderation: a multivariate GARCH Approach," MPRA Paper 36356, University Library of Munich, Germany.
    28. Guillermo Benavides & Carlos Capistrán, 2009. "Una nota sobre las volatilidades de la tasa de interés y del tipo de cambio según diferentes instrumentos de política monetaria: México 1998-2008," Monetaria, CEMLA, vol. 0(3), pages 391-412, octubre-d.
    29. González-Sánchez, Mariano, 2018. "Causality in the EMU sovereign bond markets," Finance Research Letters, Elsevier, vol. 26(C), pages 281-290.
    30. Javier Pereda, 2009. "Estimación de la curva de rendimiento para el Perú y su uso para el análisis monetario," Monetaria, CEMLA, vol. 0(3), pages 413-450, octubre-d.
    31. Tamakoshi, Go & Hamori, Shigeyuki, 2014. "Spillovers among CDS indexes in the US financial sector," The North American Journal of Economics and Finance, Elsevier, vol. 27(C), pages 104-113.
    32. Roy, Archi & Soni, Anchal & Deb, Soudeep, 2023. "A wavelet-based methodology to compare the impact of pandemic versus Russia–Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets," Energy Economics, Elsevier, vol. 124(C).
    33. Jeffrey Jarrett & Zhenzhen Sun, 2011. "Evidence and explanations for the association among six Asian (Pacific-Basin) financial markets," Applied Economics, Taylor & Francis Journals, vol. 43(12), pages 1485-1496.
    34. Cevik, Nuket Kirci & Cevik, Emrah I. & Dibooglu, Sel, 2020. "Oil prices, stock market returns and volatility spillovers: Evidence from Turkey," Journal of Policy Modeling, Elsevier, vol. 42(3), pages 597-614.
    35. Sinem Derindere KOSEOGLU & Emrah Ismail CEVIK, 2013. "Testing for Causality in Mean and Variance between the Stock Market and the Foreign Exchange Market: An Application to the Major Central and Eastern European Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(1), pages 65-86, March.
    36. Emrah İ. Çevik & Erdal Atukeren & Turhan Korkmaz, 2018. "Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis," Energies, MDPI, vol. 11(10), pages 1-22, October.
    37. Güloğlu, Bülent & Kaya, Pınar & Aydemir, Resul, 2016. "Volatility transmission among Latin American stock markets under structural breaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 330-340.
    38. Benavides Guillermo & Capistrán Carlos, 2009. "A Note on the Volatilities of the Interest Rate and the Exchange Rate Under Different Monetary Policy Instruments: Mexico 1998-2008," Working Papers 2009-10, Banco de México.

  61. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.

    Cited by:

    1. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2010. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," Working Papers 10-06, Duke University, Department of Economics.
    3. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    4. Hooper, Vincent J. & Ng, Kevin & Reeves, Jonathan J., 2008. "Quarterly beta forecasting: An evaluation," International Journal of Forecasting, Elsevier, vol. 24(3), pages 480-489.
    5. Guglielmo Maria Caporale & Luis A. Gil-Alana & Carlos Poza, 2021. "The Covid-19 Pandemic and the Degree of Persistence of US Stock Prices and Bond Yields," CESifo Working Paper Series 8976, CESifo.
    6. Xu, Jiawen & Perron, Pierre, 2014. "Forecasting return volatility: Level shifts with varying jump probability and mean reversion," International Journal of Forecasting, Elsevier, vol. 30(3), pages 449-463.
    7. Shi, Yanlin & Ho, Kin-Yip, 2015. "Modeling high-frequency volatility with three-state FIGARCH models," Economic Modelling, Elsevier, vol. 51(C), pages 473-483.
    8. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
    9. David E. Allen & Michael McAleer & Marcel Scharth, 2010. "Realized Volatility Risk," KIER Working Papers 753, Kyoto University, Institute of Economic Research.
    10. Di Sanzo, Silvestro, 2018. "A Markov switching long memory model of crude oil price return volatility," Energy Economics, Elsevier, vol. 74(C), pages 351-359.
    11. Borusyak, K., 2011. "Nonlinear Dynamics of the Russian Stock Market in Problems of Risk Management," Journal of the New Economic Association, New Economic Association, issue 11, pages 85-105.
    12. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Tinbergen Institute Discussion Papers 14-075/III, Tinbergen Institute.
    13. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    14. Yanlin Shi & Yang Yang, 2018. "Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model," Risks, MDPI, vol. 6(2), pages 1-28, March.
    15. Torben G. Andersen & Tim Bollerslev & Xin Huang, 2007. "A Reduced Form Framework for Modeling Volatility of Speculative Prices based on Realized Variation Measures," CREATES Research Papers 2007-14, Department of Economics and Business Economics, Aarhus University.
    16. Richard T. Baillie & Claudio Morana, 2014. "Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach," Working Papers 593, Queen Mary University of London, School of Economics and Finance.
    17. Marcelo C. Carvalho & Marco Aurélio S. Freire & Marcelo Cunha Medeiros & Leonardo R. Souza, 2006. "Modeling and Forecasting the Volatility of Brazilian Asset Returns: a Realized Variance Approach," Brazilian Review of Finance, Brazilian Society of Finance, vol. 4(1), pages 55-77.
    18. MArcelo Carvalho & MArco Aurelio Freire & Marcelo Cunha Medeiros & Leonardo Souza, 2006. "Modeling and forecasting the volatility of Brazilian asset returns," Textos para discussão 530, Department of Economics PUC-Rio (Brazil).
    19. Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
    20. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    21. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    22. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    23. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 199-229.
    24. Offer Lieberman & Peter Phillips, 2008. "Refined Inference on Long Memory in Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 254-267.
    25. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    26. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    27. Belkhouja, Mustapha & Boutahary, Mohamed, 2011. "Modeling volatility with time-varying FIGARCH models," Economic Modelling, Elsevier, vol. 28(3), pages 1106-1116, May.
    28. Marcel Scharth & Marcelo Cunha Medeiros, 2006. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," Textos para discussão 532, Department of Economics PUC-Rio (Brazil).
    29. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
    30. Adnen Ben Nasr & Mohamed Boutahar & Abdelwahed Trabelsi, 2010. "Fractionally integrated time varying GARCH model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 399-430, August.
    31. Nasr, Adnen Ben & Lux, Thomas & Ajm, Ahdi Noomen & Gupta, Rangan, 2014. "Forecasting the volatility of the dow jones islamic stock market index: Long memory vs. regime switching," Economics Working Papers 2014-07, Christian-Albrechts-University of Kiel, Department of Economics.
    32. Corsi, Fulvio & Kretschmer, Uta & Mittnik, Stefan & Pigorsch, Christian, 2005. "The volatility of realized volatility," CFS Working Paper Series 2005/33, Center for Financial Studies (CFS).
    33. Gabriel Rodríguez, 2015. "Modeling Latin-American Stock Markets Volatility: Varying Probabilities and Mean Reversion in a Random Level Shifts Model," Documentos de Trabajo / Working Papers 2015-403, Departamento de Economía - Pontificia Universidad Católica del Perú.
    34. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    35. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cam," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    36. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    37. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    38. Claudio Morana, 2007. "On the macroeconomic causes of exchange rates volatility," ICER Working Papers 8-2007, ICER - International Centre for Economic Research.
    39. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics 0509006, University Library of Munich, Germany.
    40. Eric Hillebrand & Marcelo Medeiros, 2010. "The Benefits of Bagging for Forecast Models of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 571-593.
    41. Adnan Kasman & Erdost Torun, 2007. "Long Memory in the Turkish Stock Market Return and Volatility," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 13-27.
    42. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    43. Gabriel Rodríguez & José Carlos Gonzáles Tanaka, 2016. "An Empirical Application of a Random Level Shifts Model with Time-Varying Probability and Mean Reversion to the Volatility of Latin-American Forex Markets Returns [Una aplicación empírica de un modelo," Documentos de Trabajo / Working Papers 2016-415, Departamento de Economía - Pontificia Universidad Católica del Perú.
    44. Trunin, Pavel (Трунин, Павел), 2015. "Analysis of the Level of Development of the Financial System in the Russian Federation [Анализ Уровня Развития Финансовой Системы В Российской Федерации]," Published Papers mn38, Russian Presidential Academy of National Economy and Public Administration.
    45. Kasman, Adnan & Kasman, Saadet & Torun, Erdost, 2009. "Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets," Emerging Markets Review, Elsevier, vol. 10(2), pages 122-139, June.
    46. Claudio Morana, 2007. "Estimating, Filtering and Forecasting Realized Betas," ICER Working Papers - Applied Mathematics Series 6-2007, ICER - International Centre for Economic Research.

  62. Timo Teräsvirta & Dick van Dijk & Marcelo Cunha Medeiros, 2004. "Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination," Textos para discussão 485, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Aslanidis, Nektarios & Christiansen, Charlotte, 2012. "Smooth transition patterns in the realized stock–bond correlation," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 454-464.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    4. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    5. Hinterlang, Natascha & Hollmayr, Josef, 2022. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Journal of Macroeconomics, Elsevier, vol. 74(C).
    6. Canepa, Alessandra & Zanetti Chini, Emilio & Alqaralleh, Huthaifa, 2023. "Modelling and Forecasting Energy Market Cycles: A Generalized Smooth Transition Approach," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202318, University of Turin.
    7. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    8. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    9. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009," CREATES Research Papers 2011-28, Department of Economics and Business Economics, Aarhus University.
    10. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    11. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    12. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    13. Orlando Gomes, 2008. "Stability under Learning: the Endogenous Growth Problem," Working Papers Series 1 ercwp1708, ISCTE-IUL, Business Research Unit (BRU-IUL).
    14. Christopher Ball & Adam Richardson & Thomas van Florenstein Mulder, 2020. "Using job transitions data as a labour market indicator," Reserve Bank of New Zealand Analytical Notes series AN2020/02, Reserve Bank of New Zealand.
    15. Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022. "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202212, University of Turin.
    16. Hou, Linke & Lv, Yuxia & Geng, Hao & Li, Feiyue, 2019. "To tell the truth or the perceived truth: Structural estimation of peer effects in China’s macroeconomic forecast," Economic Systems, Elsevier, vol. 43(2), pages 1-1.
    17. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    18. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.
    19. Jeong, Kwang-Seuk & Kim, Dong-Kyun & Jung, Jong-Mun & Kim, Myoung-Chul & Joo, Gea-Jae, 2008. "Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics," Ecological Modelling, Elsevier, vol. 211(3), pages 292-300.
    20. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    21. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2011. "Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes," Working Papers 1103, University of Nevada, Las Vegas , Department of Economics.
    22. Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," SSE/EFI Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
    23. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    24. Mihaela Bratu (Simionescu), 2013. "How to Improve the SPF Forecasts?," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(2), pages 153-165, April.
    25. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    26. Giancarlo Bruno, 2008. "Forecasting Using Functional Coefficients Autoregressive Models," ISAE Working Papers 98, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    27. BRATU SIMIONESCU, Mihaela, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
    28. Cheng, Che-Hui & Wu, Po-Chin, 2013. "Nonlinear earnings persistence," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 156-168.
    29. Sigl-Grüb, C. & Schiereck, D., 2010. "Speculation and Nonlinear Price Dynamics in Commodity Futures Markets," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56603, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    30. Balagtas, Joseph Valdes & Holt, Matthew T., 2006. "Unit Roots, TV-STARs, and the Commodity Terms of Trade: A Further Assessment of the Prebisch-Singer Hypothesis," 2006 Annual meeting, July 23-26, Long Beach, CA 21405, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    31. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
    32. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," ifo Working Paper Series 57, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    33. Emilio Zanetti Chini, 2017. "Generalizing Smooth Transition Autoregressions," DEM Working Papers Series 138, University of Pavia, Department of Economics and Management.
    34. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    35. Bruno, Giancarlo, 2012. "Consumer confidence and consumption forecast: a non-parametric approach," MPRA Paper 41312, University Library of Munich, Germany.
    36. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    37. Tea Šestanović & Josip Arnerić, 2021. "Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
    38. Alessandra Canepa & Emilio Zanetti Chini & Huthaifa Alqaralleh, 2022. "Global Cities and Local Challenges: Booms and Busts in the London Real Estate Market," The Journal of Real Estate Finance and Economics, Springer, vol. 64(1), pages 1-29, January.
    39. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
    40. Peter Exterkate, 2012. "Model Selection in Kernel Ridge Regression," CREATES Research Papers 2012-10, Department of Economics and Business Economics, Aarhus University.
    41. Bou-Hamad, Imad & Jamali, Ibrahim, 2020. "Forecasting financial time-series using data mining models: A simulation study," Research in International Business and Finance, Elsevier, vol. 51(C).
    42. Mihaela BRATU (SIMIONESCU), 2012. "A Strategy To Improve The Gdp Index Forcasts In Romania Using Moving Average Models Of Historical Errors Of The Dobrescu Macromodel," Romanian Journal of Economics, Institute of National Economy, vol. 35(2(44)), pages 128-138, December.
    43. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2006. "Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models," Discussion Paper Series 2006_6, Department of Economics, Loughborough University, revised Mar 2006.
    44. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
    45. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.
    46. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series DP2019/03, Reserve Bank of New Zealand.
    47. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2009. "Non-linear predictability in stock and bond returns: when and where is it exploitable?," Working Papers 2008-010, Federal Reserve Bank of St. Louis.
    48. Yoon, Gawon, 2010. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?," Economic Modelling, Elsevier, vol. 27(2), pages 605-612, March.
    49. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
    50. Resat Ceylan & Mehmet Ivrendi & Muhammed Shahbaz & Tolga Omay, 2022. "Oil and stock prices: New evidence from a time varying homogenous panel smooth transition VECM for seven developing countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1085-1100, January.
    51. Ralf Becker & Denise R. Osborn, 2012. "Weighted Smooth Transition Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 795-811, August.
    52. Goodwin, Barry K. & Holt, Matthew T. & Prestemon, Jeffery P., 2008. "The Commodity Terms of Trade, Unit Roots, and Nonlinear Alternatives: A Smooth Transition Approach," MPRA Paper 9684, University Library of Munich, Germany.
    53. Mehdi Hajamini, 2019. "Asymmetric Causality Between Inflation and Uncertainty: Evidences from 33 Developed and Developing Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 287-309, June.
    54. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
    55. Alexey Ponomarenko & Anna Rozhkova & Sergei Seleznev, 2018. "Macro-financial linkages: the role of liquidity dependence," BIS Working Papers 716, Bank for International Settlements.
    56. Tea Šestanović & Josip Arnerić, 2021. "Neural network structure identification in inflation forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 62-79, January.
    57. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
    58. Teddy, S.D. & Ng, S.K., 2011. "Forecasting ATM cash demands using a local learning model of cerebellar associative memory network," International Journal of Forecasting, Elsevier, vol. 27(3), pages 760-776, July.
    59. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    60. Lorenzo Fratoni & Susanna Levantesi & Massimiliano Menzietti, 2022. "Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    61. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
    62. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    63. Vijverberg, Chu-Ping C., 2009. "A time deformation model and its time-varying autocorrelation: An application to US unemployment data," International Journal of Forecasting, Elsevier, vol. 25(1), pages 128-145.
    64. Vito Polito & Yunyi Zhang, 2021. "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," CESifo Working Paper Series 9395, CESifo.
    65. Patrick T. kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 201416, University of Pretoria, Department of Economics.
    66. Francq, Christian & Horvath, Lajos & Zakoian, Jean-Michel, 2008. "Sup-tests for linearity in a general nonlinear AR(1) model when the supremum is taken over the full parameter space," MPRA Paper 16669, University Library of Munich, Germany.
    67. Arnerić Josip & Poklepović Tea & Teai Juin Wen, 2018. "Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data," Business Systems Research, Sciendo, vol. 9(2), pages 18-34, July.
    68. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    69. Francq, Christian & Horvath, Lajos & Zakoïan, Jean-Michel, 2010. "Sup-Tests For Linearity In A General Nonlinear Ar(1) Model," Econometric Theory, Cambridge University Press, vol. 26(4), pages 965-993, August.
    70. Hinterlang, Natascha & Hollmayr, Josef, 2020. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers 51/2020, Deutsche Bundesbank.
    71. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    72. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    73. Canepa, Alessandra & Alqaralleh, Huthaifa, 2019. "Housing Market Cycles in Large Urban Areas," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201903, University of Turin.
    74. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
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    77. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," AQR Working Papers 201802, University of Barcelona, Regional Quantitative Analysis Group, revised Apr 2018.
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    Cited by:

    1. Po-Chin Wu & Chung-Chih Lee, 2018. "The non-linear impact of monetary policy on international reserves: macroeconomic variables nexus," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 165-185, February.
    2. Xiaoping He & Xin Yao, 2017. "Foreign Direct Investments and the Environmental Kuznets Curve: New Evidence from Chinese Provinces," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(1), pages 12-25, January.
    3. Oleg Korenok & Bruce Mizrach, 2004. "The Microeconomics of Macroeconomic Asymmetries: Sectoral Driving Forces and Firm Level Characteristics," Computing in Economics and Finance 2004 266, Society for Computational Economics.

  64. Dick van Dijk, 2004. "Forecasting US Inflation Using Model Averaging," Econometric Society 2004 Australasian Meetings 143, Econometric Society.

    Cited by:

    1. Marcin Blazejowski & Jacek Kwiatkowski, 2018. "Bayesian Averaging of Classical Estimates (BACE) for gretl," gretl working papers 6, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.

  65. M Sensier & D van Dijk, 2003. "Testing for Volatility Changes in US Macroeconomic Time Series," Centre for Growth and Business Cycle Research Discussion Paper Series 36, Economics, The University of Manchester.

    Cited by:

    1. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
    2. Mohitosh Kejriwal, 2020. "A Robust Sequential Procedure for Estimating the Number of Structural Changes in Persistence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(3), pages 669-685, June.
    3. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Discussion Paper 2016-019, Tilburg University, Center for Economic Research.
    4. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    5. Ćorić, Bruno & Pugh, Geoff, 2013. "Foreign direct investment and output growth volatility: A worldwide analysis," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 260-271.
    6. van Dijk, Dick & Hans Franses, Philip & Peter Boswijk, H., 2007. "Absorption of shocks in nonlinear autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4206-4226, May.
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    138. Ai Deng & Pierre Perron, 2005. "The Limit Distribution of the CUSUM of Square Test Under Genreal MIxing Conditions," Boston University - Department of Economics - Working Papers Series WP2005-046, Boston University - Department of Economics.
    139. Enders, Walter & Ma, Jun, 2011. "Sources of the great moderation: A time-series analysis of GDP subsectors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 67-79, January.
    140. Clements, Michael P. & Galvao, Ana Beatriz, 2020. "Density Forecasting with BVAR Models under Macroeconomic Data Uncertainty," EMF Research Papers 36, Economic Modelling and Forecasting Group.
    141. Raihan, Tasneem, 2017. "Performance of Markov-Switching GARCH Model Forecasting Inflation Uncertainty," MPRA Paper 82343, University Library of Munich, Germany.
    142. Balázs Romhányi, 2005. "A learning hypothesis of the term structure of interest rates," Macroeconomics 0503001, University Library of Munich, Germany.
    143. Congregado, Emilio & Esteve, Vicente, 2022. "Cointegration with structural changes and classical model of inflation in Spain, 1830–1998," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 376-388.
    144. David Shepherd & Robert Dixon, 2010. "The not-so-great moderation? Evidence on changing volatility from Australian regions," Department of Economics - Working Papers Series 1090, The University of Melbourne.

  66. Paap, R. & Franses, Ph.H.B.F. & van Dijk, D.J.C., 2003. "Does Africa grow slower than Asia and Latin America?," Econometric Institute Research Papers EI 2003-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    2. Grinis, Inna, 2017. "Trend growth durations & shifts," LSE Research Online Documents on Economics 85126, London School of Economics and Political Science, LSE Library.
    3. P.H. Franses & D. Fok & D. van Dijk, 2004. "A Multi-Level Panel Smooth Transition Autoregression for US Sectoral Production," Econometric Society 2004 Australasian Meetings 267, Econometric Society.

  67. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    2. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    3. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working Papers 1209, University of Nevada, Las Vegas , Department of Economics.
    4. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    5. Ghosh, Sunandan & Kundu, Srikanta, 2019. "Central Bank Intervention in Foreign Exchange Market under Managed Float: A Three Regime Threshold VAR Analysis of Indian Rupee-US Dollar Exchange Rate," MPRA Paper 93466, University Library of Munich, Germany.
    6. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    7. Goodness C. Aye & Mehmet Balcilar & Adél Bosch & Rangan Gupta & Francois Stofberg, 2013. "The out-of-sample forecasting performance of non-linear models of real exchange rate behaviour: The case of the South African Rand," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 10(1), pages 121-148, April.
    8. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics 0509006, University Library of Munich, Germany.
    9. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    10. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    11. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    12. Giancarlo Bruno, 2009. "Non-linear relation between industrial production and business surveys data," ISAE Working Papers 119, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    13. Rodriguez, Nestor & Eales, James S., 2015. "Structural Change via Threshold Effects: Estimating U.S. Meat Demand Using Smooth Transition Functions and the Effects of More Women in the Labor Force," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206522, Agricultural and Applied Economics Association.

  68. van Dijk, D.J.C. & Franses, Ph.H.B.F., 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Econometric Institute Research Papers EI 2003-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    2. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    3. Buncic, Daniel, 2009. "Understanding forecast failure in ESTAR models of real exchange rates," MPRA Paper 13121, University Library of Munich, Germany.
    4. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Paper series 42_09, Rimini Centre for Economic Analysis.
    5. Mehmet Balcilar & Nico Katzke & Rangan Gupta, 2015. "Do Precious Metal Prices Help in Forecasting South African Inflation?," Working Papers 03/2015, Stellenbosch University, Department of Economics.
    6. Fok, D. & van Dijk, D.J.C. & Franses, Ph.H.B.F., 2004. "Forecasting aggregates using panels of nonlinear time series," Econometric Institute Research Papers EI 2004-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Avino, Davide & Nneji, Ogonna, 2012. "Are CDS spreads predictable? An analysis of linear and non-linear forecasting models," MPRA Paper 42848, University Library of Munich, Germany.
    8. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers 1416, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2017.
    10. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    11. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Post-Print hal-00834423, HAL.
    12. Mili, Mehdi & Sahut, Jean-Michel & Teulon, Frédéric, 2012. "Non linear and asymmetric linkages between real growth in the Euro area and global financial market conditions: New evidence," Economic Modelling, Elsevier, vol. 29(3), pages 734-741.
    13. Goodness C. Aye & Mehmet Balcilar Author-Name-First Mehmet & Rangan Gupta & Anandamayee Majumdar, 2014. "Forecasting Aggregate Retail Sales: The Case of South Africa," Working Papers 15-21, Eastern Mediterranean University, Department of Economics.
    14. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2011. "Predictive ability of business cycle indicators under test: A case study for the Euro area industrial production," Munich Reprints in Economics 19953, University of Munich, Department of Economics.
    15. Mehmet Balcilar & Rangan Gupta & Charl Jooste, 2014. "The Role of Economic Policy Uncertainty in Forecasting US Inflation Using a VARFIMA Model," Working Papers 201460, University of Pretoria, Department of Economics.
    16. Engel, J. & Haugh, D. & Pagan, A., 2005. "Some methods for assessing the need for non-linear models in business cycle analysis," International Journal of Forecasting, Elsevier, vol. 21(4), pages 651-662.
    17. Jean-michel Sahut & Medhi Mili & Frédéric Teulon, 2012. "What is the linkage between real growth in the Euro area and global financial market conditions?," Economics Bulletin, AccessEcon, vol. 32(3), pages 2464-2480.
    18. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2009. "Non-linear predictability in stock and bond returns: when and where is it exploitable?," Working Papers 2008-010, Federal Reserve Bank of St. Louis.
    19. Mehmet Balcilar & Rangan Gupta & Renee van Eyden & Kirsten Thompson, 2015. "Comparing the Forecasting Ability of Financial Conditions Indices: The Case of South Africa," Working Papers 15-06, Eastern Mediterranean University, Department of Economics.
    20. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
    21. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
    22. Massimo Guidolin & Daniel L. Thornton, 2010. "Predictions of short-term rates and the expectations hypothesis," Working Papers 2010-013, Federal Reserve Bank of St. Louis.
    23. Milas, Costas & Naraidoo, Ruthira, 2012. "Financial conditions and nonlinearities in the European Central Bank (ECB) reaction function: In-sample and out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 173-189, January.
    24. Goodness C. Aye & Mehmet Balcilar & Adél Bosch & Rangan Gupta & Francois Stofberg, 2013. "The out-of-sample forecasting performance of non-linear models of real exchange rate behaviour: The case of the South African Rand," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 10(1), pages 121-148, April.
    25. George Athanasopoulos & Rob J Hyndman & Raffaele Mattera, 2023. "Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering," Monash Econometrics and Business Statistics Working Papers 17/23, Monash University, Department of Econometrics and Business Statistics.
    26. E Pavlidis & I Paya & D Peel, 2009. "Forecasting the Real Exchange Rate using a Long Span of Data. A Rematch: Linear vs Nonlinear," Working Papers 601190, Lancaster University Management School, Economics Department.
    27. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    28. Florackis, Chris & Giorgioni, Gianluigi & Kostakis, Alexandros & Milas, Costas, 2014. "On stock market illiquidity and real-time GDP growth," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 210-229.

  69. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2002. "A simple test for PPP among traded goods," Econometric Institute Research Papers EI 2002-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation for Research in Economics, Yale University.
    2. Hwa-Taek Lee & Gawon Yoon, 2013. "Does purchasing power parity hold sometimes? Regime switching in real exchange rates," Applied Economics, Taylor & Francis Journals, vol. 45(16), pages 2279-2294, June.

  70. D van Dijk & D R Osborn & M Sensier, 2002. "Changes in Variability of the Business Cycle in the G7 Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 16, Economics, The University of Manchester.

    Cited by:

    1. Peter M. Summers, 2005. "What caused the Great Moderation? : some cross-country evidence," Economic Review, Federal Reserve Bank of Kansas City, vol. 90(Q III), pages 5-32.
    2. Dong Jin Lee, 2021. "Bootstrap tests for structural breaks when the regressors and the serially correlated error term are unstable," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 212-229, April.
    3. Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2010. "Testing for co-integration in vector autoregressions with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 158(1), pages 7-24, September.
    4. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2009. "Changes in International Business Cycle Affiliations," Economics Discussion Paper Series 0924, Economics, The University of Manchester.
    5. Pedro Perez & Denise Osborn & Marianne Sensier, 2007. "Business cycle affiliations in the context of European integration," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 199-214.
    6. Niels Haldrup & Robinson Kruse & Timo Teräsvirta & Rasmus T. Varneskov, 2012. "Unit roots, nonlinearities and structural breaks," CREATES Research Papers 2012-14, Department of Economics and Business Economics, Aarhus University.
    7. Dong Jin Lee, 2011. "Bootstrap Tests for Structural Breaks When the Regressors and Error Term are Nonstationary," Working papers 2011-05, University of Connecticut, Department of Economics.
    8. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    9. Erdenebat Bataa & Denise R.Osborn & Marianne Sensier, 2016. "China's Increasing Global Influence: Changes in International Growth Spillovers," Centre for Growth and Business Cycle Research Discussion Paper Series 221, Economics, The University of Manchester.
    10. Giuseppe Cavaliere & A. M. Robert Taylor, 2008. "Time‐Transformed Unit Root Tests for Models with Non‐Stationary Volatility," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 300-330, March.
    11. Giuseppe Cavaliere & Peter C. B. Phillips & Stephan Smeekes & A. M. Robert Taylor, 2015. "Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 512-536, April.
    12. Tara Sinclair & Sinchan Mitra, 2008. "Output Fluctuations in the G-7: An Unobserved Components Approach," Working Papers 2008-04, The George Washington University, Institute for International Economic Policy.
    13. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    14. Kushal Banik Chowdhury & Srikanta Kundu & Nityananda Sarkar, 2018. "Regime‐dependent effects of uncertainty on inflation and output growth: evidence from the United Kingdom and the United States," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(4), pages 390-413, September.
    15. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed? Evidence and Explanations," Working Papers 2003-2, Princeton University. Economics Department..
    16. Kushal Banik Chowdhury & Kaustav Kanti Sarkar & Srikanta Kundu, 2021. "Nonlinear relationships between inflation, output growth and uncertainty in India: New evidence from a bivariate threshold model," Bulletin of Economic Research, Wiley Blackwell, vol. 73(3), pages 469-493, July.
    17. Brian M. Doyle & Jon Faust, 2003. "Breaks in the variability and co-movement of G-7 economic growth," International Finance Discussion Papers 786, Board of Governors of the Federal Reserve System (U.S.).
    18. Penelope A. Smith & Peter M. Summers, 2002. "Regime Switches in GDP Growth and Volatility: Some International Evidence and Implications for Modelling Business Cycles," Melbourne Institute Working Paper Series wp2002n21, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    19. Heather Anderson & Farshid Vahid, 2003. "The Decline in Income Growth Volatility in the United States: Evidence from Regional Data," Monash Econometrics and Business Statistics Working Papers 21/03, Monash University, Department of Econometrics and Business Statistics.
    20. William Martin & Robert Rowthorn, 2004. "Will Stability Last?," CESifo Working Paper Series 1324, CESifo.
    21. Hao Jin & Si Zhang & Jinsuo Zhang, 2017. "Spurious regression due to neglected of non-stationary volatility," Computational Statistics, Springer, vol. 32(3), pages 1065-1081, September.
    22. Ossama Mikhail, 2004. "No More Rocking Horses: Trading Business-Cycle Depth for Duration Using an Economy-Specific Characteristic," Macroeconomics 0402026, University Library of Munich, Germany.
    23. Ossama Mikhail, 2006. "Trading Business-Cycle Depth for Duration using an economy-specific characteristic," Economics Bulletin, AccessEcon, vol. 5(7), pages 1-12.
    24. Kemal Çag̃lar Gög̃ebakan & Burak Alparslan Eroglu, 2022. "Non-parametric seasonal unit root tests under periodic non-stationary volatility," Computational Statistics, Springer, vol. 37(5), pages 2581-2636, November.
    25. Cavaliere, Giuseppe & Taylor, A.M. Robert, 2007. "Testing for unit roots in time series models with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 140(2), pages 919-947, October.

  71. Sensier, Marianne & Dick van Dijk, 2002. "Short-term Volatility versus Long-term Growth: Evidence in US Macroeconomic Time Series," Royal Economic Society Annual Conference 2002 164, Royal Economic Society.

    Cited by:

    1. D van Dijk & D R Osborn & M Sensier, 2002. "Changes in Variability of the Business Cycle in the G7 Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 16, Economics, The University of Manchester.
    2. Giuseppe Cavaliere & A. M. Robert Taylor, 2006. "Testing for a Change in Persistence in the Presence of a Volatility Shift," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 761-781, December.
    3. Jonathan McCarthy & Egon Zakrajšek, 2002. "Inventory dynamics and business cycles: what has changed?," Staff Reports 156, Federal Reserve Bank of New York.
    4. Jean-Yves Pitarakis, 2003. "Least Squares Estimation and Tests of Breaks in Mean and Variance under Misspecification," Econometrics 0312004, University Library of Munich, Germany.
    5. Marcelle Chauvet & Simon Potter, 2001. "Recent Changes in the US Business Cycle," Manchester School, University of Manchester, vol. 69(5), pages 481-508, October.
    6. Magda Kandil, 2006. "Asymmetric Effects Of Aggregate Demand Shocks Across U.S. Industries: Evidence And Implications," Eastern Economic Journal, Eastern Economic Association, vol. 32(2), pages 259-283, Spring.
    7. James H. Stock & Mark W. Watson, 2002. "Has the Business Cycle Changed and Why?," NBER Working Papers 9127, National Bureau of Economic Research, Inc.
    8. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed? Evidence and Explanations," Working Papers 2003-2, Princeton University. Economics Department..
    9. Emilio Congregado & Silviano Carmen Díaz-Roldán & Vicente Esteve, 2023. "Deficit sustainability and the Fiscal Theory of the Price Level: the case of Italy, 1861-2020," Working Papers 2301, Department of Applied Economics II, Universidad de Valencia.
    10. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    11. James H. Stock & Mark W. Watson, 2003. "Has the business cycle changed?," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 9-56.
    12. Lazzarini, S. G. & Madalozzo, R. C & Artes, R. & Siqueira, J. O., 2004. "Measuring trust: An experiment in Brazil," Insper Working Papers wpe_42, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    13. Gerba, Eddie, 2015. "Have the US macro-financial linkages changed? The balance sheet dimension," LSE Research Online Documents on Economics 59886, London School of Economics and Political Science, LSE Library.
    14. Ana Beatriz C. Galvão, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487, May.

  72. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2001. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," Econometric Institute Research Papers EI 2001-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Malgorzata Olszak & Mateusz Pipien & Iwona Kowalska & Sylwia Roszkowska, 2015. "Do regulations and supervision shape the capital crunch effect of large banks in the EU?," Faculty of Management Working Paper Series 32015, University of Warsaw, Faculty of Management.
    2. Xiao, Yi & Liu, John J. & Hu, Yi & Wang, Yingfeng & Lai, Kin Keung & Wang, Shouyang, 2014. "A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting," Journal of Air Transport Management, Elsevier, vol. 39(C), pages 1-11.
    3. Ubilava, David, 2014. "On the Relationship between Financial Instability and Economic Performance: Stressing the Business of Nonlinear Modelling," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170222, Agricultural and Applied Economics Association.
    4. Norman Swanson & Richard Urbach, 2013. "Prediction and Simulation Using Simple Models Characterized by Nonstationarity and Seasonality," Departmental Working Papers 201323, Rutgers University, Department of Economics.
    5. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2017. "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," ETA: Economic Theory and Applications 253725, Fondazione Eni Enrico Mattei (FEEM).
    6. Francisco Salas-Molina & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Montserrat Guillen & Francisco J. Martin, 2016. "Empirical analysis of daily cash flow time series and its implications for forecasting," Papers 1611.04941, arXiv.org, revised Jun 2017.
    7. Daniel Dzikowski & Carsten Jentsch, 2024. "Structural Periodic Vector Autoregressions," Papers 2401.14545, arXiv.org.
    8. Łukasz Lenart, 2017. "Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(1), pages 29-67, March.
    9. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    10. Beckmann, Joscha, 2011. "Nonlinear Adjustment, Purchasing Power Parity and the Role of Nominal Exchange Rates and Prices," Ruhr Economic Papers 272, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    11. Andrea BASTIANIN & Marzio GALEOTTI & Matteo MANERA, 2011. "Forecast evaluation in call centers: combined forecasts, flexible loss functions and economic criteria," Departmental Working Papers 2011-08, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    12. Malgorzata Olszak & Mateusz Pipien & Iwona Kowalska & Sylwia Roszkowska, 2015. "The Impact Of Capital On Lending In Economic Downturns And Investor Protection – The Case Of Large Eu Banks," Faculty of Management Working Paper Series 62015, University of Warsaw, Faculty of Management.
    13. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    14. Yi Xiao & Shouyang Wang & Ming Xiao & Jin Xiao & Yi Hu, 2017. "The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 851-863, May.
    15. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    16. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    17. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    18. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    19. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    20. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707.
    21. Hasanov, Mübariz & Araç, Aysen & Telatar, Funda, 2010. "Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey," Economic Modelling, Elsevier, vol. 27(5), pages 1103-1115, September.
    22. Marc Lavoie & Gabriel Rodríguez, 2005. "The Economic Impact of Professional Teams on Monthly Hotel Occupancy Rates of Canadian Cities," Journal of Sports Economics, , vol. 6(3), pages 314-324, August.
    23. Elena Ganón & Ina Tiscordio, 2007. "Un análisis de variables fiscales del Gobierno Central del Uruguay para el período 1989-2006," Documentos de trabajo 2007005, Banco Central del Uruguay.
    24. Giancarlo Bruno, 2009. "Non-linear relation between industrial production and business surveys data," ISAE Working Papers 119, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    25. Charles S. Bos & Siem Jan Koopman, 2010. "Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production," Tinbergen Institute Discussion Papers 10-017/4, Tinbergen Institute.
    26. Xiao Yi & Liu John J. & Wang Yingfeng & Hu Yi, 2014. "Time Series Forecasting Using a Hybrid Adaptive Particle Swarm Optimization and Neural Network Model," Journal of Systems Science and Information, De Gruyter, vol. 2(4), pages 335-344, August.

  73. van Dijk, Dick & Strikholm, Birgit & Teräsvirta, Timo, 2001. "The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series," SSE/EFI Working Paper Series in Economics and Finance 0429, Stockholm School of Economics, revised 01 Jun 2004.

    Cited by:

    1. Siem Jan Koopman & Kai Ming Lee, 0000. "Seasonality with Trend and Cycle Interactions in Unobserved Components Models," Tinbergen Institute Discussion Papers 08-028/4, Tinbergen Institute.
    2. Craig, Lee & Holt, Matthew T., 2012. "The Role of Mechanical Refrigeration in Spatial and Temporal Price Dynamics for Regional U.S. Egg Markets, 1880–1911," MPRA Paper 39554, University Library of Munich, Germany.
    3. Wang, Yajie & Yu, Huan & Zhang, Hongda & Chen, Tianyu, 2021. "Non-linear analysis of effects of energy consumption on economic growth in China: Role of real exchange rate," Economic Modelling, Elsevier, vol. 104(C).
    4. Nermeen Harb & Mamdouh Abdelmoula M. Abdelsalam, 2019. "Effect Of Oil Prices On Stock Markets: Evidence From New Generation Of Star Model," Bulletin of Economic Research, Wiley Blackwell, vol. 71(3), pages 466-482, July.
    5. Michael Funke & Marc Gronwald, 2008. "The undisclosed Renminbi Basket: are the markets telling us something about where the Renminbi - US Dollar Exchange Rate is going?," Quantitative Macroeconomics Working Papers 20812b, Hamburg University, Department of Economics.
    6. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
    7. Matthew T. Holt & Timo Teräsvirta, 2012. "Global Hemispheric Temperature Trends and Co–Shifting: A Shifting Mean Vector Autoregressive Analysis," CREATES Research Papers 2012-54, Department of Economics and Business Economics, Aarhus University.
    8. Matas-Mir, Antonio & Osborn, Denise R., 2004. "Does seasonality change over the business cycle? An investigation using monthly industrial production series," European Economic Review, Elsevier, vol. 48(6), pages 1309-1332, December.
    9. Walter Enders & Matthew T. Holt, 2014. "The Evolving Relationships between Agricultural and Energy Commodity Prices: A Shifting-Mean Vector Autoregressive Analysis," NBER Chapters, in: The Economics of Food Price Volatility, pages 135-187, National Bureau of Economic Research, Inc.
    10. Franses, Philip Hans & van Dijk, Dick, 2005. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," International Journal of Forecasting, Elsevier, vol. 21(1), pages 87-102.
    11. D R Osborn & A Matas-Mir, 2003. "The Extent of Seasonal/Business Cycle Interactions in European Industrial Production," Centre for Growth and Business Cycle Research Discussion Paper Series 38, Economics, The University of Manchester.
    12. Ahdi Ajmi & Adnen Ben Nasr & Mohamed Boutahar, 2008. "Seasonal Nonlinear Long Memory Model for the US Inflation Rates," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 243-254, April.
    13. Michael Dueker & Martin Sola & Fabio Spagnolo, 2006. "Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting," Department of Economics Working Papers 2006-04, Universidad Torcuato Di Tella.
    14. Holt, Matthew T. & Teräsvirta, Timo, 2020. "Global hemispheric temperatures and co-shifting: A vector shifting-mean autoregressive analysis," Journal of Econometrics, Elsevier, vol. 214(1), pages 198-215.
    15. João Paulo Martin Faleiros & Denisard Cnéio de Oliveira Alves, 2006. "Não Linearidade Nos Ciclos De Negócios: Modelo Auto-Regressivo “Smooth Transition” Para O Índice Geral De Produção Industrial Brasileiro E Bens De Capital," Anais do XXXIV Encontro Nacional de Economia [Proceedings of the 34th Brazilian Economics Meeting] 10, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    16. Changli He & Jian Kang & Timo Teräsvirta & Shuhua Zhang, 2018. "The Shifting Seasonal Mean Autoregressive Model and Seasonality in the Central England Monthly Temperature Series, 1772-2016," CREATES Research Papers 2018-15, Department of Economics and Business Economics, Aarhus University.
    17. Dalu Zhang & Peter Moffatt, 2013. "Time series non-linearity in the real growth / recession-term spread relationship," University of East Anglia Applied and Financial Economics Working Paper Series 047, School of Economics, University of East Anglia, Norwich, UK..
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    19. João Paulo Martin Faleiros & Denisard Cnéio de Oliveira Alves, 2008. "Modelo de Crescimento Baseado nas Exportações: Evidências empíricas para Chile, Brasil e México, em uma perspectiva Não Linear," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807170923500, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    20. Gnidchenko, Andrey, 2011. "Моделирование Технологических И Институциональных Эффектов В Макроэкономическом Прогнозировании [Technological and Institutional Effects Modeling in Macroeconomic Forecasting]," MPRA Paper 35484, University Library of Munich, Germany, revised May 2011.
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    25. Ana Beatriz C. Galvão, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487, May.
    26. Kumawat, Lokendra, 2010. "Effect of Rainfall on Seasonals in Indian Manufacturing Production: Evidence from Sectoral Data," MPRA Paper 25300, University Library of Munich, Germany.

  74. Swanson, N.R. & van Dijk, D.J.C., 2001. "Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry," Econometric Institute Research Papers EI 2001-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    1. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
    2. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    3. Hobler, Stephan, 2022. "Multi-layered rational inattention and time-varying volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 138(C).
    4. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    5. Paap, R. & Segers, R. & van Dijk, D.J.C., 2007. "Do leading indicators lead peaks more than troughs?," Econometric Institute Research Papers EI 2007-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    7. Franses, Philip Hans, 2013. "Data revisions and periodic properties of macroeconomic data," Economics Letters, Elsevier, vol. 120(2), pages 139-141.
    8. Jan Jacobs & Jan-Egbert Sturm, 2009. "The information content of KOF indicators on Swiss current account data revisions," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(2), pages 161-181.
    9. Francisco de Castro & Javier J. Pérez & Marta Rodríguez Vives, 2011. "Fiscal data revisions in Europe," Working Papers 1106, Banco de España.
    10. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.
    11. Pascal Bührig & Klaus Wohlrabe, 2015. "Revisions of German Industrial Production Statistics and Ifo Indicators," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(21), pages 27-31, November.
    12. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
    13. Tara M. Sinclair & Fred Joutz & Herman O. Stekler, 2008. "Are 'unbiased' forecasts really unbiased? Another look at the Fed forecasts," Working Papers 2008-010, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    14. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    15. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    16. Anthony Garratt & Gary Koop & Shaun P. Vahey, 2008. "Forecasting Substantial Data Revisions in the Presence of Model Uncertainty," Economic Journal, Royal Economic Society, vol. 118(530), pages 1128-1144, July.
    17. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    18. Norman R. Swanson & Valentina Corradi & Andres Fernandez, 2011. "Information in the Revision Process of Real-Time Datasets," Departmental Working Papers 201107, Rutgers University, Department of Economics.
    19. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    20. Heij, C. & van Dijk, D.J.C. & Groenen, P.J.F., 2009. "Macroeconomic forecasting with real-time data: an empirical comparison," Econometric Institute Research Papers EI 2009-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    21. Oller, Lars-Erik & Teterukovsky, Alex, 2007. "Quantifying the quality of macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 23(2), pages 205-217.
    22. Anthony Garratt & Shaun P Vahey, 2006. "UK Real-Time Macro Data Characteristics," Economic Journal, Royal Economic Society, vol. 116(509), pages 119-135, February.
    23. Dean Croushore, 2008. "Frontiers of real-time data analysis," Working Papers 08-4, Federal Reserve Bank of Philadelphia.
    24. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    25. Philip Hans Franses, 2009. "Why is GDP typically revised upwards?," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(2), pages 125-130, May.
    26. Mr. Manik L. Shrestha & Mr. Marco Marini, 2013. "Quarterly GDP Revisions in G-20 Countries: Evidence from the 2008 Financial Crisis," IMF Working Papers 2013/060, International Monetary Fund.
    27. Tara M. Sinclair, 2012. "Characteristics and Implications of Chinese Macroeconomic Data Revisions," Working Papers 2012-09, The George Washington University, Institute for International Economic Policy.
    28. Thomas A. Knetsch & Hans‐Eggert Reimers, 2009. "Dealing with Benchmark Revisions in Real‐Time Data: The Case of German Production and Orders Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(2), pages 209-235, April.
    29. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    30. Altavilla, Carlo & Ciccarelli, Matteo, 2010. "Evaluating the effect of monetary policy on unemployment with alternative inflation forecasts," Economic Modelling, Elsevier, vol. 27(1), pages 237-253, January.
    31. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.
    32. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Working Papers 0807, Banco de España.
    33. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage Combination from a Real-Time Dataset," CESifo Working Paper Series 3372, CESifo.
    34. Tara M. Sinclair & Fred Joutz & Herman O. Stekler, 2009. "Can the Fed Predict the State of the Economy?," Working Papers 2009-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Mar 2010.
    35. Jan P.A.M. Jacobs & Simon van Norden, 2010. "Lessons From the Latest Data on U.S. Productivity," CAMA Working Papers 2010-33, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    36. Theo S. Eicher & David J. Kuenzel & Mr. Chris Papageorgiou & Mr. Charalambos Christofides, 2018. "Forecasts in Times of Crises," IMF Working Papers 2018/048, International Monetary Fund.
    37. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
    38. Binz, Oliver & Mayew, William J. & Nallareddy, Suresh, 2022. "Firms’ response to macroeconomic estimation errors," Journal of Accounting and Economics, Elsevier, vol. 73(2).
    39. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Ravazzolo, F., 2007. "Evaluating real-time forecasts in real-time," Econometric Institute Research Papers EI 2007-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    40. Katharina Glass, 2018. "Predictability of Euro Area Revisions," Macroeconomics and Finance Series 201801, University of Hamburg, Department of Socioeconomics.
    41. Juan Bógalo & Pilar Poncela & Eva Senra, 2021. "Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    42. Laakkonen, Helinä & Lanne, Markku, 2009. "The Relevance of Accuracy for the Impact of Macroeconomic News on Volatility," MPRA Paper 23718, University Library of Munich, Germany.
    43. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.
    44. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    45. Jacobs, Jan P.A.M. & van Norden, Simon, 2016. "Why are initial estimates of productivity growth so unreliable?," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 200-213.
    46. Lixiong Yang, 2020. "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, vol. 59(6), pages 2663-2687, December.
    47. Baetje, Fabian & Friedrici, Karola, 2016. "Does cross-sectional forecast dispersion proxy for macroeconomic uncertainty? New empirical evidence," Economics Letters, Elsevier, vol. 143(C), pages 38-43.
    48. Xie, Zixiong & Hsu, Shih-Hsun, 2016. "Time varying biases and the state of the economy," International Journal of Forecasting, Elsevier, vol. 32(3), pages 716-725.
    49. Boysen-Hogrefe, Jens & Neuwirth, Stefan, 2012. "The impact of seasonal and price adjustments on the predictability of German GDP revisions," Kiel Working Papers 1753, Kiel Institute for the World Economy (IfW Kiel).
    50. Michael Pedersen, 2024. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," Papers 2404.04105, arXiv.org.
    51. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    52. Daniel Culbertson & Tara Sinclair, 2014. "The Failure of Forecasts in the Great Recession," Challenge, Taylor & Francis Journals, vol. 57(6), pages 34-45.
    53. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    54. Ciccarelli, Matteo & Altavilla, Carlo, 2007. "Information combination and forecast (st)ability evidence from vintages of time-series data," Working Paper Series 846, European Central Bank.

  75. Jaap van der Hart & Erica Slagter & Dick van Dijk, 2001. "Stock Selection Strategies in Emerging Markets," Tinbergen Institute Discussion Papers 01-009/4, Tinbergen Institute.

    Cited by:

    1. van der Hart, Jaap & de Zwart, Gerben & van Dijk, Dick, 2005. "The success of stock selection strategies in emerging markets: Is it risk or behavioral bias?," Emerging Markets Review, Elsevier, vol. 6(3), pages 238-262, September.
    2. Mayank Gupta & Jan Novotny, 2016. "The Dynamics of Value Comovement across Global Equity Markets," CERGE-EI Working Papers wp560, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    3. Bekaert, Geert & Harvey, Campbell R., 2003. "Emerging markets finance," Journal of Empirical Finance, Elsevier, vol. 10(1-2), pages 3-56, February.
    4. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    5. Dimitris Andriosopoulos & Michael Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Post-Print hal-02880149, HAL.
    6. de Groot, W.A. & Pang, J. & Swinkels, L.A.P., 2012. "The Cross-Section of Stock Returns in Frontier Emerging Markets," ERIM Report Series Research in Management ERS-2012-012-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Alexei Goriaev & Alexei Zabotkin, 2006. "Risks of investing in the Russian stock market: Lessons of the first decade," Working Papers w0077, New Economic School (NES).
    8. Sharma, Susan Sunila & Narayan, Paresh Kumar & Thuraisamy, Kannan & Laila, Nisful, 2019. "Is Indonesia's stock market different when it comes to predictability?," Emerging Markets Review, Elsevier, vol. 40(C), pages 1-1.
    9. I-Cheng Yeh, 2023. "Synergy frontier of multi-factor stock selection model," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 445-480, March.
    10. Moreno, David & Olmeda, Ignacio, 2007. "Is the predictability of emerging and developed stock markets really exploitable?," European Journal of Operational Research, Elsevier, vol. 182(1), pages 436-454, October.
    11. Lischewski, Judith & Voronkova, Svitlana, 2010. "Size, value and liquidity: Do they really matter on an emerging stock market?," ZEW Discussion Papers 10-070, ZEW - Leibniz Centre for European Economic Research.
    12. Blitz, David & Pang, Juan & van Vliet, Pim, 2013. "The volatility effect in emerging markets," Emerging Markets Review, Elsevier, vol. 16(C), pages 31-45.
    13. Ali Fayyaz Munir & Shahrin Saaid Shaharuddin & Mohd Edil Abd. Sukor, 2020. "Long-Term, Short-Term and Time-Varying Profitability of Reversals: The Role of Market State and Volatility," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 501-520.
    14. Demirer, Rıza & Yuksel, Asli & Yuksel, Aydin, 2017. "Flight to quality and the predictability of reversals: The role of market states and global factors," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1445-1454.
    15. Luis Muga & Rafael Santamaria, 2007. "The momentum effect: omitted risk factors or investor behaviour? Evidence from the Spanish stock market," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 637-650.
    16. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    17. Hanauer, Matthias X. & Lauterbach, Jochim G., 2019. "The cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 38(C), pages 265-286.
    18. Lischewski, Judith & Voronkova, Svitlana, 2012. "Size, value and liquidity. Do They Really Matter on an Emerging Stock Market?," Emerging Markets Review, Elsevier, vol. 13(1), pages 8-25.
    19. I-Cheng Yeh & Yi-Cheng Liu, 2020. "Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-28, December.
    20. Galariotis, Emilios C. & Holmes, Phil & Ma, Xiaodong S., 2007. "Contrarian and momentum profitability revisited: Evidence from the London Stock Exchange 1964-2005," Journal of Multinational Financial Management, Elsevier, vol. 17(5), pages 432-447, December.
    21. Sharma, Susan Sunila & Thuraisamy, Kannan & Madyan, Muhammad & Laila, Nisful, 2019. "Evidence of price discovery on the Indonesian stock exchange," Economic Modelling, Elsevier, vol. 83(C), pages 2-7.
    22. Alexsandro Broedel Lopes & Fernando Caio Galdi, 2008. "Limits to arbitrage and returns to value investing," Fucape Working Papers 10, Fucape Business School.
    23. Kortas, Mohamed & L'Her, Jean-Francois & Roberge, Mathieu, 2005. "Country selection of emerging equity markets: benefits from country attribute diversification," Emerging Markets Review, Elsevier, vol. 6(1), pages 1-19, April.
    24. Barry, Christopher B. & Goldreyer, Elizabeth & Lockwood, Larry & Rodriguez, Mauricio, 2002. "Robustness of size and value effects in emerging equity markets, 1985-2000," Emerging Markets Review, Elsevier, vol. 3(1), pages 1-30, March.
    25. Cheng Yan & Ji Yan, 2021. "Optimal and naive diversification in an emerging market: Evidence from China's A‐shares market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3740-3758, July.
    26. Seif, Mostafa & Docherty, Paul & Shamsuddin, Abul, 2017. "Seasonal anomalies in advanced emerging stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 169-181.
    27. Huij, Joop & Post, Thierry, 2011. "On the performance of emerging market equity mutual funds," Emerging Markets Review, Elsevier, vol. 12(3), pages 238-249, September.
    28. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    29. Manuel Galea & Patricia Giménez, 2019. "Local influence diagnostics for the test of mean–variance efficiency and systematic risks in the capital asset pricing model," Statistical Papers, Springer, vol. 60(1), pages 293-312, February.
    30. Hoguet, George R., 2006. "Earnings estimates in emerging markets--an update," Emerging Markets Review, Elsevier, vol. 7(3), pages 213-227, September.
    31. Chiao-Yi Chang, 2013. "Daily momentum profits with firm characteristics and investors’ optimism in the Taiwan market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(2), pages 253-273, April.
    32. Hatgioannides, John & Mesomeris, Spyros, 2007. "On the returns generating process and the profitability of trading rules in emerging capital markets," Journal of International Money and Finance, Elsevier, vol. 26(6), pages 948-973, October.
    33. Chen Su, 2021. "A comprehensive investigation into style momentum strategies in China," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 101-144, March.
    34. Sina Badreddine & Emilios C. C Galariotis & Phil Holmes, 2012. "The relevance of information and trading costs in explaining momentum profits: Evidence from optioned and non-optioned stocks," Post-Print hal-00956948, HAL.
    35. Emilios C. C Galariotis & Phil Holmes & Vasileios Kallinterakis & Xiaodong S. Ma, 2014. "Market states, expectations, sentiment and momentum: How naive are investors?," Post-Print hal-00943345, HAL.
    36. Bartram, Söhnke M. & Grinblatt, Mark, 2021. "Global market inefficiencies," Journal of Financial Economics, Elsevier, vol. 139(1), pages 234-259.
    37. Auer, Benjamin R., 2016. "On time-varying predictability of emerging stock market returns," Emerging Markets Review, Elsevier, vol. 27(C), pages 1-13.
    38. XingYu Fu & JinHong Du & YiFeng Guo & MingWen Liu & Tao Dong & XiuWen Duan, 2018. "A Machine Learning Framework for Stock Selection," Papers 1806.01743, arXiv.org, revised Aug 2018.
    39. Minh Phuong Doan & Vitali Alexeev & Robert Brooks, 2016. "Concurrent momentum and contrarian strategies in the Australian stock market," Australian Journal of Management, Australian School of Business, vol. 41(1), pages 77-106, February.
    40. Teplova, Tamara & Tomtosov, Aleksandr, 2021. "Can high trading volume and volatility switch boost momentum to show greater inefficiency and avoid crashes in emerging markets? The economic relationship in factor investing in emerging markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 210-223.
    41. Hoque, Hafiz A.A.B. & Kim, Jae H. & Pyun, Chong Soo, 2007. "A comparison of variance ratio tests of random walk: A case of Asian emerging stock markets," International Review of Economics & Finance, Elsevier, vol. 16(4), pages 488-502.
    42. Kearney, Colm, 2012. "Emerging markets research: Trends, issues and future directions," Emerging Markets Review, Elsevier, vol. 13(2), pages 159-183.
    43. Yan, Cheng & Zhang, Huazhu, 2017. "Mean-variance versus naïve diversification: The role of mispricing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 61-81.
    44. Kinnunen, Jyri, 2013. "Dynamic return predictability in the Russian stock market," Emerging Markets Review, Elsevier, vol. 15(C), pages 107-121.
    45. Pereiro, Luis E., 2006. "The practice of investment valuation in emerging markets: Evidence from Argentina," Journal of Multinational Financial Management, Elsevier, vol. 16(2), pages 160-183, April.
    46. Cheema, Muhammad A. & Nartea, Gilbert V., 2017. "Momentum returns, market states, and market dynamics: Is China different?," International Review of Economics & Finance, Elsevier, vol. 50(C), pages 85-97.
    47. Conover, C. Mitchell & Jensen, Gerald R. & Johnson, Robert R. & Szakmary, Andrew C., 2017. "Emerging markets: Is the trend still your friend?," Global Finance Journal, Elsevier, vol. 32(C), pages 128-148.
    48. Waszczuk, Antonina, 2013. "A risk-based explanation of return patterns—Evidence from the Polish stock market," Emerging Markets Review, Elsevier, vol. 15(C), pages 186-210.
    49. Chao, Hsiao-Ying & Collver, Charles & Limthanakom, Natcha, 2012. "Global style momentum," Journal of Empirical Finance, Elsevier, vol. 19(3), pages 319-333.

  76. Boswijk, H.P. & van Dijk, D. & Franses, P.H., 2000. "Asymmetric and Common Abssorbtion of Shocks in Nonlinear Autoregressive Models," CeNDEF Working Papers 00-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.

    Cited by:

    1. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    2. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    3. Galvão, Ana Beatriz C., 2003. "Multivariate Threshold Models: TVARs and TVECMs," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(1), May.
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    1. Raimundo Soto, 2000. "Ajuste Estacional e Integración en Variables Macroeconómicas," Working Papers Central Bank of Chile 73, Central Bank of Chile.
    2. Adnen Ben Nasr & Mehmet Balcilar & Ahdi N. Ajmi & Goodness C. Aye & Rangan Gupta & Reneé van Eyden, 2014. "Causality between Inflation and Inflation Uncertainty in South Africa: Evidence from a Markov-Switching Vector Autoregressive Model," Working Papers 201453, University of Pretoria, Department of Economics.
    3. Ahdi Ajmi & Adnen Ben Nasr & Mohamed Boutahar, 2008. "Seasonal Nonlinear Long Memory Model for the US Inflation Rates," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 243-254, April.
    4. Nicholas Taylor, 2004. "A New Econometric Model Of Index Arbitrage," Royal Economic Society Annual Conference 2004 69, Royal Economic Society.
    5. João Paulo Martin Faleiros & Denisard Cnéio de Oliveira Alves, 2008. "Modelo de Crescimento Baseado nas Exportações: Evidências empíricas para Chile, Brasil e México, em uma perspectiva Não Linear," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807170923500, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    6. Pami Dua & Lokendra Kumawat, 2007. "Modelling Seasonal Dynamics in Indian Industrial Production--An Extention of TV-STAR Model," Working papers 162, Centre for Development Economics, Delhi School of Economics.
    7. Kumawat, Lokendra, 2010. "Effect of Rainfall on Seasonals in Indian Manufacturing Production: Evidence from Sectoral Data," MPRA Paper 25300, University Library of Munich, Germany.

  78. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," SSE/EFI Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.

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    1. Michael J. Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2013. "State-Dependent Threshold Smooth Transition Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(6), pages 835-854, December.
    2. Hamulczuk, Mariusz, 2020. "Spatial Integration of Agricultural Commodity Markets – Methodological Problems," Problems of Agricultural Economics / Zagadnienia Ekonomiki Rolnej 311225, Institute of Agricultural and Food Economics - National Research Institute (IAFE-NRI).
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    4. Costas Milas, 2009. "Does high M4 money growth trigger large increases in UK inflation? Evidence from a regime-switching model," Oxford Economic Papers, Oxford University Press, vol. 61(1), pages 168-182, January.
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    7. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    8. Munehisa Kasuya, 2005. "Regime-switching approach to monetary policy effects," Applied Economics, Taylor & Francis Journals, vol. 37(3), pages 307-326.
    9. Gilles Dufrénot & Guillaume A. Khayat, 2017. "Monetary Policy Switching in the Euro Area and Multiple Steady States: An Empirical Investigation," Post-Print hal-01590000, HAL.
    10. Samira Haddou, 2010. "Non-linéarité de la fonction de réaction des autorités monétaires tunisiennes," Économie et Prévision, Programme National Persée, vol. 195(4), pages 99-110.
    11. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
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  79. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Paap, R., 2000. "A nonlinear long memory model for US unemployment," Econometric Institute Research Papers EI 2000-30/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2004. "Non-linearities and fractional integration in the US unemployment rate," HWWA Discussion Papers 259, Hamburg Institute of International Economics (HWWA).

  80. Phillip Rothman & Dick van Dijk & Philip Hans Franses, 2000. "A Multivariate STAR Analysis of the Relationship Between Money and Output," Working Papers 0012, East Carolina University, Department of Economics.

    Cited by:

    1. Jonathan B. Hill, 2004. "Consistent LM-Tests for Linearity Against Compound Smooth Transition Alternatives," Econometric Society 2004 North American Summer Meetings 42, Econometric Society.
    2. Randall E. Parker & Phillip Rothman & Original: August 2000. This version: June 2003., "undated". "An Examination of the Asymmetric Effects of Money Supply Shocks in the Pre-World War I and Interwar Periods," Working Papers 0011, East Carolina University, Department of Economics.
    3. Gabriella Legrenzi & Costas Milas, 2004. "Non-linear real exchange rate effects in the UK labour market," International Finance 0411007, University Library of Munich, Germany.
    4. Dimitris K. Christopoulos & Miguel A. León-Ledesma, 2008. "Testing for Granger (non-)causality in a time-varying coefficient VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 293-303.
    5. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    6. Jawadi Fredj & Koubaa Yousra, 2004. "Threshold Cointegration between Stock Returns : An application of STECM Models," Econometrics 0412001, University Library of Munich, Germany.
    7. Robert Sollis, 2005. "Evidence on purchasing power parity from univariate models: the case of smooth transition trend-stationarity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 79-98.
    8. Jonathan B. Hill, 2004. "Efficient Tests of Long-Run Causation in Trivariate VAR Processes with a Rolling Window Study of the Money-Income Relationship," Macroeconomics 0407013, University Library of Munich, Germany, revised 15 Feb 2006.
    9. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    10. Mendoza Lugo, Omar & Pedauga, Luis Enrique, 2006. "Efecto transferencia (pass-through) del tipo de cambio en los precios de bienes y servicios en Venezuela [Exchange rate pass-through on prices of goods and services in Venezuela]," MPRA Paper 14874, University Library of Munich, Germany.
    11. Turuntseva, M. & Zyamalov, V., 2016. "Stock Markets under the Changing Terms of Trade," Journal of the New Economic Association, New Economic Association, vol. 31(3), pages 93-109.
    12. Jonathan B. Hill, 2005. "Causation Delays and Causal Neutralization up to Three Steps Ahead: The Money-Output Relationship Revisited," Econometrics 0503016, University Library of Munich, Germany, revised 23 Mar 2005.
    13. Valerie Herzberg & George Kapetanios & Simon Price, 2003. "Import prices and exchange rate pass-through: theory and evidence from the United Kingdom," Bank of England working papers 182, Bank of England.
    14. Michael J. Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2007. "Multivariate contemporaneous threshold autoregressive models," Working Papers 2007-019, Federal Reserve Bank of St. Louis.
    15. Maximo Camacho, 2002. "Nonlinear stochastic trends and economic fluctuations," Computing in Economics and Finance 2002 274, Society for Computational Economics.
    16. Peter Martey Addo, 2014. "Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input," Papers 1407.7738, arXiv.org.
    17. Nedeljkovic, Milan, 2008. "Testing for Smooth Transition Nonlinearity in Adjustments of Cointegrating Systems," The Warwick Economics Research Paper Series (TWERPS) 876, University of Warwick, Department of Economics.
    18. Camacho, Maximo, 2005. "Markov-switching stochastic trends and economic fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 135-158, January.

  81. Lundbergh, Stefan & Teräsvirta, Timo & van Dijk, Dick, 2000. "Time-Varying Smooth Transition Autoregressive Models," SSE/EFI Working Paper Series in Economics and Finance 376, Stockholm School of Economics.

    Cited by:

    1. Luis Eduardo Arango & Andrés González & Carlos Esteban Posada, 2001. "Returns and Interest Rate: A Nonlinear Relationship in the Bogotá Stock Market," Borradores de Economia 169, Banco de la Republica de Colombia.
    2. M Sensier & D van Dijk, 2001. "Short-term Volatility versus Long-term Growth: Evidence in US Macroeconomic Time Series," Centre for Growth and Business Cycle Research Discussion Paper Series 08, Economics, The University of Manchester.
    3. Offer Lieberman & Peter C.B. Phillips, 2013. "Norming Rates and Limit Theory for Some Time-Varying Coefficient Autoregressions," Cowles Foundation Discussion Papers 1916, Cowles Foundation for Research in Economics, Yale University.
    4. Oleg Korenok & Stanislav Radchenko, 2005. "The smooth transition autoregressive target zone model with the Gaussian stochastic volatility and TGARCH error terms with applications," Working Papers 0505, VCU School of Business, Department of Economics.
    5. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 113-149.
    6. Maertens Odria, Luís Ricardo & Castillo, Paul & Rodriguez, Gabriel, 2012. "Does the exchange rate pass-through into prices change when inflation targeting is adopted? The Peruvian case study between 1994 and 2007," Journal of Macroeconomics, Elsevier, vol. 34(4), pages 1154-1166.
    7. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    8. Canepa, Alessandra & Zanetti Chini, Emilio & Alqaralleh, Huthaifa, 2023. "Modelling and Forecasting Energy Market Cycles: A Generalized Smooth Transition Approach," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202318, University of Turin.
    9. Tolga Omay & Mübariz Hasanov & Yongcheol Shin, 2018. "Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 167-193, June.
    10. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
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    Cited by:

    1. Ivan Paya & David Peel, 2005. "The process followed by PPP data. On the properties of linearity tests," Applied Economics, Taylor & Francis Journals, vol. 37(21), pages 2515-2522.
    2. Magdalena Osinska & Joanna Górka, 2006. "Identification of Non-linearity in Economic Time Series," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 7, pages 83-92.

  83. Nick Taylor & Dick van Dijk & Philip Hans Franses & André Lucas, 1999. "SETS, Arbitrage Activity, and Stock Price Dynamics," Tinbergen Institute Discussion Papers 99-003/4, Tinbergen Institute.

    Cited by:

    1. Angelidis, Timotheos & Andrikopoulos, Andreas, 2010. "Idiosyncratic risk, returns and liquidity in the London Stock Exchange: A spillover approach," International Review of Financial Analysis, Elsevier, vol. 19(3), pages 214-221, June.
    2. Wanbing Zhang & Sisi Zhang & Peibiao Zhao, 2019. "On Double Value at Risk," Risks, MDPI, vol. 7(1), pages 1-22, March.
    3. Patricia Chelley-Steeley & Antonios Siganos, 2005. "Momentum Profits in Alternative Stock Market Structures," Money Macro and Finance (MMF) Research Group Conference 2005 63, Money Macro and Finance Research Group.
    4. Chelley-Steeley, Patricia L., 2008. "Market quality changes in the London Stock Market," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2248-2253, October.
    5. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
    6. Garrett Ian & Taylor Nicholas, 2001. "Intraday and Interday Basis Dynamics: Evidence from the FTSE 100 Index Futures Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(2), pages 1-22, July.
    7. Lekkos, Ilias & Milas, Costas, 2004. "Time-varying excess returns on UK government bonds: A non-linear approach," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 45-62, January.
    8. Joseph K.W. Fung & Philip Yu, 2007. "Order Imbalance and the Dynamics of Index and Futures Prices," Working Papers 072007, Hong Kong Institute for Monetary Research.
    9. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    10. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    11. Robles-Fernandez M. Dolores & Nieto Luisa & Fernandez M. Angeles, 2004. "Nonlinear Intraday Dynamics in Eurostoxx50 Index Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(4), pages 1-28, December.
    12. Juan A. Lafuente & Manuel Illueca Muñoz, 2003. "The Effect Of Futures Trading Activity On The Distribution Of Spot Market Returns," Working Papers. Serie EC 2003-23, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    13. Yiuman Tse & Paramita Bandyopadhyay & Yang‐Pin Shen, 2006. "Intraday Price Discovery in the DJIA Index Markets," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1572-1585, November.
    14. Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
    15. Yiu‐Kuen Tse & Wai‐Sum Chan, 2010. "The Lead–Lag Relation Between The S&P500 Spot And Futures Markets: An Intraday‐Data Analysis Using A Threshold Regression Model," The Japanese Economic Review, Japanese Economic Association, vol. 61(1), pages 133-144, March.
    16. Nicholas Taylor, 2004. "A New Econometric Model Of Index Arbitrage," Royal Economic Society Annual Conference 2004 69, Royal Economic Society.
    17. Canto, Bea & Kräussl, Roman, 2007. "Electronic trading systems and intraday non-linear dynamics: An examination of the FTSE 100 cash and futures returns," CFS Working Paper Series 2007/20, Center for Financial Studies (CFS).
    18. Taylor, Nicholas, 2004. "Trading intensity, volatility, and arbitrage activity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 1137-1162, May.
    19. Assaf, Ata, 2006. "The stochastic volatility in mean model and automation: Evidence from TSE," The Quarterly Review of Economics and Finance, Elsevier, vol. 46(2), pages 241-253, May.
    20. Ivan Paya & David A. Peel, 2011. "Systematic sampling of nonlinear models: Evidence on speed of adjustment in index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(2), pages 192-203, February.
    21. Charlie X. Cai & Robert Hudson & Kevin Keasey, 2004. "Intra Day Bid‐Ask Spreads, Trading Volume and Volatility: Recent Empirical Evidence from the London Stock Exchange," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 647-676, June.
    22. Jürgen Gaul & Erik Theissen, 2015. "A Partially Linear Approach to Modeling the Dynamics of Spot and Futures Prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(4), pages 371-384, April.
    23. Christopher L. Gilbert & Herbert A. Rijken, 2006. "How is Futures Trading Affected by the Move to a Computerized Trading System? Lessons from the LIFFE FTSE 100 Contract," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(7‐8), pages 1267-1297, September.
    24. Chen, Shiyi & Chng, Michael T. & Liu, Qingfu, 2021. "The implied arbitrage mechanism in financial markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 468-483.
    25. Stephen Norman, 2009. "Testing for a unit root against ESTAR nonlinearity with a delay parameter greater than one," Economics Bulletin, AccessEcon, vol. 29(3), pages 2152-2173.
    26. Tse, Yiuman & Xiang, Ju, 2005. "Market quality and price discovery: Introduction of the E-mini energy futures," Global Finance Journal, Elsevier, vol. 16(2), pages 164-179, December.

  84. Escribano, A. & Franses, Ph.H.B.F. & van Dijk, D.J.C., 1998. "Nonlinearities and outliers: robust specification of STAR models," Econometric Institute Research Papers EI 9832, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    1. Samira Haddou, 2011. "Is Tunisian Real Effective Exchange Rate Mean Reverting? Evidence from Nonlinear Models," Transition Studies Review, Springer;Central Eastern European University Network (CEEUN), vol. 18(1), pages 164-178, September.
    2. López Villavicencio, Antonia, 2008. "Nonlinearities or outliers in real exchange rates?," Economic Modelling, Elsevier, vol. 25(4), pages 714-730, July.

  85. Berben, R-P. & van Dijk, D.J.C., 1998. "Does the absence of cointegration explain the typical findings in long horizon regressions?," Econometric Institute Research Papers EI 9814, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Nelson Mark & Donggyu Sul, 1998. "Norminal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-Bretton Woods Panel," Working Papers 98-19, Ohio State University, Department of Economics.
    2. Christopher J. Neely & Lucio Sarno, 2002. "How well do monetary fundamentals forecast exchange rates?," Review, Federal Reserve Bank of St. Louis, vol. 84(Sep), pages 51-74.
    3. Jon Faust & John H. Rogers & Jonathan H. Wright, 2001. "Exchange rate forecasting: the errors we've really made," International Finance Discussion Papers 714, Board of Governors of the Federal Reserve System (U.S.).
    4. Barbara Rossi, 2013. "Exchange rate predictability," Economics Working Papers 1369, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Mark E. Wohar & David E. Rapach, 2005. "Valuation ratios and long-horizon stock price predictability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 327-344.
    6. mamatzakis, e & Christodoulakis, G, 2013. "Behavioural Asymmetries in the G7 Foreign Exchange Market," MPRA Paper 51615, University Library of Munich, Germany.
    7. Ekong, Christopher N. & Onye, Kenneth U., 2013. "The Failure of the Monetary Exchange Rate Model for the Naira-Dollar," MPRA Paper 88238, University Library of Munich, Germany.
    8. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," CEPR Discussion Papers 7008, C.E.P.R. Discussion Papers.
    9. Taylor, Mark & Kilian, Lutz, 2001. "Why is it so Difficult to Beat the Random Walk Forecast of Exchange Rates?," CEPR Discussion Papers 3024, C.E.P.R. Discussion Papers.
    10. Rapach, David E. & Wohar, Mark E., 2004. "Testing the monetary model of exchange rate determination: a closer look at panels," Journal of International Money and Finance, Elsevier, vol. 23(6), pages 867-895, October.
    11. Joakim Westerlund & Syed A. Basher, 2007. "Can panel data really improve the predictability of the monetary exchange rate model?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 365-383.
    12. Ghosh, Taniya & Bhadury, Soumya, 2018. "Money's causal role in exchange rate: Do divisia monetary aggregates explain more?," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 402-417.
    13. Adawo, Monday A. & Effiong, Ekpeno L., 2013. "Monetary exchange rate model as a long-run phenomenon: evidence from Nigeria," MPRA Paper 46407, University Library of Munich, Germany.
    14. Nelson C. Mark & Donggyu Sul, 2004. "The Use of Predictive Regressions at Alternative Horizons in Finance and Economics," NBER Technical Working Papers 0298, National Bureau of Economic Research, Inc.
    15. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    16. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
    17. Ryan Ratcliff, 2010. "Predicting nominal exchange rate movements using skewness information from options prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 75-92.
    18. Siregar, Reza, 2011. "The Concepts of Equilibrium Exchange Rate: A Survey of Literature," MPRA Paper 28987, University Library of Munich, Germany.
    19. Junttila, Juha & Korhonen, Marko, 2011. "Nonlinearity and time-variation in the monetary model of exchange rates," Journal of Macroeconomics, Elsevier, vol. 33(2), pages 288-302, June.
    20. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
    21. Jan J.J. Groen, 1998. "The Monetary Exchange Rate Model as a Long-Run Phenomenon," Tinbergen Institute Discussion Papers 98-082/2, Tinbergen Institute.
    22. Uz, Idil & Ketenci, Natalya, 2008. "Panel analysis of the monetary approach to exchange rates: Evidence from ten new EU members and Turkey," Emerging Markets Review, Elsevier, vol. 9(1), pages 57-69, March.
    23. Ekpeno L. Effiong, 2014. "Exchange Rate Dynamics and Monetary Fundamentals: A Cointegrated SVAR Approach for Nigeria," Global Business Review, International Management Institute, vol. 15(2), pages 205-221, June.
    24. Ibhagui, Oyakhilome W., 2019. "Does the long-run monetary model hold for Sub-Saharan Africa? A time series and panel-cointegration study," Research in International Business and Finance, Elsevier, vol. 47(C), pages 279-303.
    25. López-Suárez, Carlos Felipe & Rodríguez-López, José Antonio, 2011. "Nonlinear exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 30(5), pages 877-895, September.
    26. Lucio Sarno, 2003. "Nonlinear Exchange Rate Models: A Selective Overview," Rivista di Politica Economica, SIPI Spa, vol. 93(4), pages 3-46, July-Augu.
    27. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    28. Wu, Jyh-Lin & Hu, Yu-Hau, 2009. "New evidence on nominal exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 28(6), pages 1045-1063, October.
    29. Rapach, David E. & Wohar, Mark E., 2002. "Testing the monetary model of exchange rate determination: new evidence from a century of data," Journal of International Economics, Elsevier, vol. 58(2), pages 359-385, December.

  86. Philip Hans Franses & Dick van Dijk & André Lucas, 1998. "Short Patches of Outliers, ARCH and Volatility Modeling," Tinbergen Institute Discussion Papers 98-057/4, Tinbergen Institute.

    Cited by:

    1. F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
    2. Lanne, Markku & Saikkonen, Pentti, 2005. "A Multivariate Generalized Orthogonal Factor GARCH Model," MPRA Paper 23714, University Library of Munich, Germany.
    3. Carnero, María Ángeles & Peña, Daniel & Ruiz Ortega, Esther, 2004. "Spurious and hidden volatility," DES - Working Papers. Statistics and Econometrics. WS ws042007, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Miralles-Quirós, José Luis & Daza-Izquierdo, Julio, 2015. "Do DOW returns really influence the intraday Spanish stock market behavior?," Research in International Business and Finance, Elsevier, vol. 33(C), pages 99-126.
    5. Grossi, Luigi & Laurini, Fabrizio, 2009. "A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2251-2263, April.
    6. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    7. González-Sánchez, Mariano, 2021. "Is there a relationship between the time scaling property of asset returns and the outliers? Evidence from international financial markets," Finance Research Letters, Elsevier, vol. 38(C).
    8. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    9. Christos Avdoulas & Stelios Bekiros & Sabri Boubaker, 2018. "Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets," Annals of Operations Research, Springer, vol. 262(2), pages 307-333, March.
    10. Amado Peir, 2016. "Changes in the Unconditional Variance and Autoregressive Conditional Heteroscedasticity," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1338-1343.
    11. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    12. L. Grossi & G. Morelli, 2006. "Robust volatility forecasts and model selection in financial time series," Economics Department Working Papers 2006-SE02, Department of Economics, Parma University (Italy).
    13. Pavel Cizek & Wolfgang Härdle, 2006. "Robust Econometrics," SFB 649 Discussion Papers SFB649DP2006-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Beatriz Catalan & F. Javier Trivez, 2007. "Forecasting volatility in GARCH models with additive outliers," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 591-596.
    15. Kyrtsou, Catherine & Malliaris, Anastasios G., 2009. "The impact of information signals on market prices when agents have non-linear trading rules," Economic Modelling, Elsevier, vol. 26(1), pages 167-176, January.
    16. Par Sjolander, 2010. "A stationary unbiased finite sample ARCH-LM test procedure," Applied Economics, Taylor & Francis Journals, vol. 43(8), pages 1019-1033.
    17. Jose Luis Miralles-Marcelo & Jose Luis Miralles-Quiros & Maria del Mar Miralles-Quiros, 2010. "Intraday linkages between the Spanish and the US stock markets: evidence of an overreaction effect," Applied Economics, Taylor & Francis Journals, vol. 42(2), pages 223-235.

  87. Franses, Ph.H.B.F. & Neele, J. & van Dijk, D.J.C., 1998. "Forecasting volatility with switching persistence GARCH models," Econometric Institute Research Papers EI 9819, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Felix Chan & Michael McAleer, 2001. "Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers," ISER Discussion Paper 0539, Institute of Social and Economic Research, Osaka University.
    2. Claude Diebolt & Mohamed Chikhi, 2021. "Testing The Weak Form Efficiency Of The French Etf Market With Lstar-Anlstgarch Approach Using A Semiparametric Estimation," Working Papers 09-21, Association Française de Cliométrie (AFC).
    3. Arouri, Mohamed El Hédi & Jawadi, Fredj & Nguyen, Duc Khuong, 2012. "Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS," Economic Modelling, Elsevier, vol. 29(3), pages 884-892.
    4. Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
    5. Gilles DUFRENOT & Laurent MATHIEU & Val=E9rie MIGNON & Anne PEGUIN-FEISSOLE, 2003. "Persistent misalignments of the European exchanges rates: some evidence from nonlinear cointegration," International Finance 0309003, University Library of Munich, Germany.
    6. Eskandar A. Tooma, 2003. "Modeling and Forecasting Egyptian Stock Market Volatility Before and After Price Limits," Working Papers 0310, Economic Research Forum, revised Apr 2003.
    7. Mohamed Chikhi & Claude Diebolt, 2019. "Testing Nonlinearity through a Logistic Smooth Transition AR Model with Logistic Smooth Transition GARCH Errors," Working Papers of BETA 2019-06, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    8. Melike Bildirici & Nilgun Guler Bayazit & Yasemen Ucan, 2020. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM," Energies, MDPI, vol. 13(11), pages 1-18, June.

  88. van Dijk, D.J.C. & Franses, Ph.H.B.F., 1997. "Nonlinear Error-Correction Models for Interest Rates in The Netherlands," Econometric Institute Research Papers EI 9704-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Roberto Golinelli, 1998. "Fatti stilizzati e metodi econometrici "moderni": una rivisitazione della curva di Phillips per l'Italia (1951-1996)," Politica economica, Società editrice il Mulino, issue 3, pages 411-446.
    2. Mark J. Holmes & Ping Wang, 2005. "Do African Countries Move Asymmetrically Towards Purchasing Power Parity?," South African Journal of Economics, Economic Society of South Africa, vol. 73(2), pages 292-301, June.
    3. Samuel S Jibao & Niek Schoeman & Ruthira Naraidoo, 2010. "Fiscal Regime Changes and the Sustainability of Fiscal Imbalance in South Africa: A Smooth Transition Error-Correction Approach," Working Papers 201023, University of Pretoria, Department of Economics.
    4. Holmes, Mark J. & Maghrebi, Nabil, 2006. "Are international real interest rate linkages characterized by asymmetric adjustments?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(4), pages 384-396, October.
    5. Gilles Dufrénot & Valérie Mignon, 2002. "La cointégration non linéaire : une note méthodologique," Économie et Prévision, Programme National Persée, vol. 155(4), pages 117-137.
    6. R. Golinelli, 1998. "Fatti stilizzati e metodi econometrici "Moderni": una rivalutazione della Curva di Phillips per l'Italia (1951-1996)," Working Papers 313, Dipartimento Scienze Economiche, Universita' di Bologna.

  89. van Dijk, D.J.C. & Franses, Ph.H.B.F., 1997. "Modelling Multiple Regimes in the Business Cycle," Econometric Institute Research Papers EI 9734/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Jonathan B. Hill, 2004. "Consistent LM-Tests for Linearity Against Compound Smooth Transition Alternatives," Econometric Society 2004 North American Summer Meetings 42, Econometric Society.
    2. Samira Haddou, 2010. "Non-linéarité de la fonction de réaction des autorités monétaires tunisiennes," Économie et Prévision, Programme National Persée, vol. 195(4), pages 99-110.
    3. Panayiotis C. Andreou & Christodoulos Louca & Christos S. Savva, 2016. "Short-horizon event study estimation with a STAR model and real contaminated events," Review of Quantitative Finance and Accounting, Springer, vol. 47(3), pages 673-697, October.
    4. Sibel Cengiz & Afsin Sahin, 2014. "Modelling nonlinear behavior of labor force participation rate by STAR: An application for Turkey," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 7(1), pages 113-127, April.
    5. Mr. Kazim Kazimov & Mr. Kirk Hamilton & Mr. Rabah Arezki, 2011. "Resource Windfalls, Macroeconomic Stability and Growth: The Role of Political Institutions," IMF Working Papers 2011/142, International Monetary Fund.
    6. Michael Arghyrou & Christopher Martin & Costas Milas, 2005. "Non-linear inflationary dynamics: evidence from the UK," Oxford Economic Papers, Oxford University Press, vol. 57(1), pages 51-69, January.
    7. David Guerreiro & Valérie Mignon, 2011. "On price convergence in Eurozone," Working Papers hal-04140947, HAL.
    8. Andreas Röthig & Carl Chiarella, 2007. "Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(8), pages 719-737, August.
    9. Carlo Altavilla & Paul De Grauwe, 2010. "Non-linearities in the relation between the exchange rate and its fundamentals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-21.
    10. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    11. Simeon Coleman & Juan Carlos Cuestas & Estefanía Mourelle, 2011. "Investigating the oil price-exchange rate nexus: Evidence from Africa," Working Papers 2011015, The University of Sheffield, Department of Economics, revised May 2011.
    12. Barbara Annicchiarico & Anna Rita Bennato & Emilio Zanetti Chini, 2014. "150 Years of Italian CO2 Emissions and Economic Growth," CEIS Research Paper 320, Tor Vergata University, CEIS, revised 31 Jul 2014.
    13. Rickard Sandberg, 2016. "Testing for unit roots in nonlinear heterogeneous panels with smoothly changing trends: an application to Scandinavian unemployment rates," Empirical Economics, Springer, vol. 51(3), pages 1053-1083, November.
    14. Krauss, Christopher & Herrmann, Klaus & Teis, Stefan, 2015. "On the power and size properties of cointegration tests in the light of high-frequency stylized facts," FAU Discussion Papers in Economics 11/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    15. Kyriakos C. Neanidis & Christos S. Savva, 2010. "Macroeconomic Uncertainty, Inflation and Growth: Regime-Dependent Effects in the G7," Centre for Growth and Business Cycle Research Discussion Paper Series 145, Economics, The University of Manchester.
    16. Munehisa Kasuya, 2003. "Regime-Switching Approach to Monetary Policy Effects: Empirical Studies using a Smooth Transition Vector Autoregressive Model," Bank of Japan Working Paper Series Research and Statistics D, Bank of Japan.
    17. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    18. Michael J. Dueker, 2006. "Using cyclical regimes of output growth to predict jobless recoveries," Review, Federal Reserve Bank of St. Louis, vol. 88(Mar), pages 145-154.
    19. Davinson Stev Abril Salcedo & Luis Fernando Melo Velandia & Daniel Parra Amado, 2015. "Impactos de los fenómenos climáticos sobre el precio de los alimentos en Colombia," Borradores de Economia 902, Banco de la Republica de Colombia.
    20. Jiang, Yu, 2020. "Identification of business cycles and the Great Moderation in the post-war U.S. economy," Economics Letters, Elsevier, vol. 190(C).
    21. Faria, João Ricardo & Cuestas, Juan Carlos & Mourelle, Estefanía, 2010. "Entrepreneurship and unemployment: A nonlinear bidirectional causality?," Economic Modelling, Elsevier, vol. 27(5), pages 1282-1291, September.
    22. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    23. Valérie Mignon & Pauline Bucciarelli & Emmanuel Hache, 2024. "Evaluating criticality of strategic metals: Are the Herfindahl–Hirschman Index and usual concentration thresholds still relevant?," EconomiX Working Papers 2024-3, University of Paris Nanterre, EconomiX.
    24. Gilles DUFRENOT & Laurent MATHIEU & Val=E9rie MIGNON & Anne PEGUIN-FEISSOLE, 2003. "Persistent misalignments of the European exchanges rates: some evidence from nonlinear cointegration," International Finance 0309003, University Library of Munich, Germany.
    25. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    26. Balagtas, Joseph Valdes & Holt, Matthew T., 2006. "Unit Roots, TV-STARs, and the Commodity Terms of Trade: A Further Assessment of the Prebisch-Singer Hypothesis," 2006 Annual meeting, July 23-26, Long Beach, CA 21405, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    27. David Ubilava, 2018. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 239-263.
    28. Emilio Zanetti Chini, 2017. "Generalizing Smooth Transition Autoregressions," DEM Working Papers Series 138, University of Pavia, Department of Economics and Management.
    29. Zhang, Lingxiang, 2013. "Modeling China's inflation dynamics: An MRSTAR approach," Economic Modelling, Elsevier, vol. 31(C), pages 440-446.
    30. Gilles DUFRENOT & Val=E9rie MIGNON & Anne PEGUIN-FEISSOLE, 2003. "Business cycles asymmetry and monetary policy: a further investigatio= n=20 using MRSTAR models," Macroeconomics 0309002, University Library of Munich, Germany.
    31. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
    32. Ahmad, Saad, 2016. "A multiple threshold analysis of the Fed's balancing act during the Great Moderation," Economic Modelling, Elsevier, vol. 55(C), pages 343-358.
    33. Koo, Chao, 2018. "Essays on functional coefficient models," Other publications TiSEM ba87b8a5-3c55-40ec-967d-9, Tilburg University, School of Economics and Management.
    34. Bec, Frédérique & Bouabdallah, Othman & Ferrara, Laurent, 2014. "The way out of recessions: A forecasting analysis for some Euro area countries," International Journal of Forecasting, Elsevier, vol. 30(3), pages 539-549.
    35. Rabah Arezki & Klaus Deininger & Harris Selod, 2012. "What drives the global rush?," NCID Working Papers 02/2012, Navarra Center for International Development, University of Navarra.
    36. Goodwin, Barry K. & Holt, Matthew T. & Prestemon, Jeffery P., 2008. "The Commodity Terms of Trade, Unit Roots, and Nonlinear Alternatives: A Smooth Transition Approach," MPRA Paper 9684, University Library of Munich, Germany.
    37. Gilles Dufrénot & Valérie Mignon & Anne Peguin-Feissolle, 2004. "Business Cycles Asymmetry and Monetary Policy: A Further Investigation using MRSTAR Models," Post-Print halshs-00390154, HAL.
    38. Caggiano, Giovanni & Castelnuovo, Efrem & Groshenny, Nicolas, 2014. "Uncertainty shocks and unemployment dynamics in U.S. recessions," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 78-92.
    39. Di Caro, Paolo, 2014. "Testing and explaining economic resilience with an application to Italian regions," MPRA Paper 60298, University Library of Munich, Germany.
    40. Omar A Mendoza Lugo, 2008. "The differential impact of real interest rates and credit availability on private investment: evidence from Venezuela," BIS Papers chapters, in: Bank for International Settlements (ed.), Transmission mechanisms for monetary policy in emerging market economies, volume 35, pages 501-537, Bank for International Settlements.
    41. Mendoza Lugo, Omar & Pedauga, Luis Enrique, 2006. "Efecto transferencia (pass-through) del tipo de cambio en los precios de bienes y servicios en Venezuela [Exchange rate pass-through on prices of goods and services in Venezuela]," MPRA Paper 14874, University Library of Munich, Germany.
    42. Rickard Sandberg, 2018. "Unit Root Testing in Multiple Smooth Break Models with Nonlinear Dynamics," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 942-952, November.
    43. David Ubilava, 2012. "Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach," Agribusiness, John Wiley & Sons, Ltd., vol. 28(1), pages 29-41, January.
    44. José Cancelo & Estefanía Mourelle, 2005. "Modeling Cyclical Asymmetries in European Imports," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 11(2), pages 135-147, May.
    45. Apergis, Nicholas, 2015. "Financial portfolio choice: Do business cycle regimes matter? Panel evidence from international household surveys," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 14-27.
    46. Beatriz C. Galvao, Ana, 2002. "Can non-linear time series models generate US business cycle asymmetric shape?," Economics Letters, Elsevier, vol. 77(2), pages 187-194, October.
    47. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707.
    48. Ming Luo & Ruguo Fan & Yingqing Zhang, 2017. "A Study on China’s Urban Electricity Productivity Convergence with Spatial Smooth Transition Effect," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
    49. Hasanov, Mübariz & Araç, Aysen & Telatar, Funda, 2010. "Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey," Economic Modelling, Elsevier, vol. 27(5), pages 1103-1115, September.
    50. Jonathan B. Hill, 2004. "Consistent Model Specification Tests Against Smooth Transition Alternatives," Econometrics 0402004, University Library of Munich, Germany, revised 05 Aug 2005.
    51. Jiangze Du & Shaojie Lai & Kin Keung Lai & Shifei Zhou, 2021. "A novel term structure stochastic model with adaptive correlation for trend analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5485-5498, October.
    52. Jeremy Piger & James Morley & Chang-Jin Kim, 2005. "Nonlinearity and the permanent effects of recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 291-309.
    53. Honoré Sèwanoudé HOUNGBEDJI, 2022. "Non linéarité de la fonction de réaction de la Banque centrale des Etats de l’Afrique de l’Ouest," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 56, pages 133-157.
    54. Gilles Dufrénot & Valérie Mignon, 2002. "La cointégration non linéaire : une note méthodologique," Économie et Prévision, Programme National Persée, vol. 155(4), pages 117-137.
    55. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    56. Christopher Krauss & Klaus Herrmann, 2017. "On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts," JRFM, MDPI, vol. 10(1), pages 1-24, February.
    57. Davinson Stev Abril Salcedo & Luis Fernando Melo Velandia & Daniel Parra Amado, 2015. "Impactos de los fenómenos climáticos sobre el precio de los alimentos en Colombia," Borradores de Economia 13648, Banco de la Republica.
    58. Carlo Altavilla & Luigi Landolfo, 2005. "Do central banks act asymmetrically? Empirical evidence from the ECB and the Bank of England," Applied Economics, Taylor & Francis Journals, vol. 37(5), pages 507-519.
    59. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    60. Don Bredin & Stilianos Fountas & Christos Savva, 2021. "Is British Output Growth Related to its Uncertainty? Evidence using Eight Centuries of Data," Discussion Paper Series 2021_02, Department of Economics, University of Macedonia, revised Feb 2021.
    61. Lof, Matthijs, 2010. "Heterogeneity in Stock Pricing: A STAR Model with Multivariate Transition Functions," MPRA Paper 30520, University Library of Munich, Germany.
    62. Switzer, Lorne N. & Picard, Alan, 2016. "Stock market liquidity and economic cycles: A non-linear approach," Economic Modelling, Elsevier, vol. 57(C), pages 106-119.
    63. Bartholomew, Luke & Diggie, Paul, 2022. "'Stall Speed' and 'Escape Velocity': Empty Metaphors or Empirical Realities?," CEPR Discussion Papers 14290, C.E.P.R. Discussion Papers.
    64. Kushal Banik Chowdhury & Nityananda Sarkar, 2019. "Regime Dependent Effect Of Output Growth On Output Growth Uncertainty: Evidence From Oecd Countries," Bulletin of Economic Research, Wiley Blackwell, vol. 71(3), pages 257-282, July.
    65. Timo Teräsvirta, 2017. "Nonlinear models in macroeconometrics," CREATES Research Papers 2017-32, Department of Economics and Business Economics, Aarhus University.
    66. Nidhal Mgadmi & Slim Chaouachi & Wajdi Moussa & Azza Bejaoui, 2021. "Does the Tunisian Central Bank follow an augmented nonlinear Taylor rule?," SN Business & Economics, Springer, vol. 1(1), pages 1-15, January.
    67. Marco Rubilar-González & Gabriel Pino, 2018. "Are Euro-Area expectations about recession phases effective to anticipate consequences of economic crises?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 141-161, June.
    68. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," SSE/EFI Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 29 Apr 2004.
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    70. Jose Maria Fernandez-Crehuet & Luis Alberiko Gil-Alana & Cristina Martí Barco, 2020. "Unemployment and Fertility: A Long Run Relationship," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(3), pages 1177-1196, December.
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  90. Franses, Ph.H.B.F. & van Dijk, D.J.C., 1997. "Do We Often Find ARCH Because Of Neglected Outliers?," Econometric Institute Research Papers EI 9706-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
    2. Beatriz Catalan & F. Javier Trivez, 2007. "Forecasting volatility in GARCH models with additive outliers," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 591-596.
    3. Ermini, Luigi, 1998. "A Tale of Three Seasonal Adjustment Procedures: The Case of Sweden's GDP," SSE/EFI Working Paper Series in Economics and Finance 230, Stockholm School of Economics.

  91. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Lucas, A., 1996. "Testing for ARCH in the Presence of Additive Outliers," Econometric Institute Research Papers EI 9659-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Robin L. Lumsdaine & Serena Ng, 1998. "Testing for ARCH in the Presence of a Possibly Misspecified Conditional Mean," Boston College Working Papers in Economics 370, Boston College Department of Economics.
    2. E. Ruiz & M.A. Carnero & D. Pereira, 2004. "Effects of Level Outliers on the Identification and Estimation of GARCH Models," Econometric Society 2004 Australasian Meetings 21, Econometric Society.
    3. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    4. Fang, WenShwo & Miller, Stephen M., 2009. "Modeling the volatility of real GDP growth: The case of Japan revisited," Japan and the World Economy, Elsevier, vol. 21(3), pages 312-324, August.
    5. Daiki Maki, 2015. "Wild bootstrap tests for unit root in ESTAR models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 475-490, September.
    6. Jinliang Li & Chihwa Kao & Wei David Zhang, 2010. "Bounded influence estimator for GARCH models: evidence from foreign exchange rates," Applied Economics, Taylor & Francis Journals, vol. 42(11), pages 1437-1445.
    7. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    8. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2008. "Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation," Centre for Growth and Business Cycle Research Discussion Paper Series 109, Economics, The University of Manchester.
    9. WenShwo Fang & Stephen M. Miller, 2012. "Output Growth and Its Volatility: The Gold Standard through the Great Moderation," Working papers 2012-11, University of Connecticut, Department of Economics.
    10. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    11. Hotta, Luiz & Trucíos, Carlos & Ruiz Ortega, Esther, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Amélie Charles & Olivier Darné, 2012. "Trends and random walks in macroeconomic time series: A reappraisal," Post-Print hal-00956937, HAL.
    13. Daiki Maki & Yasushi Ota, 2021. "Testing for Time-Varying Properties Under Misspecified Conditional Mean and Variance," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1167-1182, April.
    14. Carnero, María Ángeles & Peña, Daniel & Ruiz Ortega, Esther, 2004. "Spurious and hidden volatility," DES - Working Papers. Statistics and Econometrics. WS ws042007, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Maki, Daiki, 2015. "Wild bootstrap testing for cointegration in an ESTAR error correction model," Economic Modelling, Elsevier, vol. 47(C), pages 292-298.
    16. Bali, Rakesh & Guirguis, Hany, 2007. "Extreme observations and non-normality in ARCH and GARCH," International Review of Economics & Finance, Elsevier, vol. 16(3), pages 332-346.
    17. Cizek, P., 2007. "Efficient Robust Estimation of Time-Series Regression Models," Other publications TiSEM d76eb299-a6b2-4f5a-bb9f-a, Tilburg University, School of Economics and Management.
    18. Grossi, Luigi & Laurini, Fabrizio, 2009. "A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2251-2263, April.
    19. Mohamed Ali Houfi & Ghassen El Montasser, 2010. "Effets des points aberrants sur les tests de normalité et de linéarité. Applications à la bourse de Tokyo," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 13(36), pages 15-51, June.
    20. Dejan Živkov & Jovan Njegić & Mirela Momčilović & Ivan Milenković, 2016. "Exchange Rate Volatility and Uncovered Interest Rate Parity in the European Emerging Economies," Prague Economic Papers, Prague University of Economics and Business, vol. 2016(3), pages 253-270.
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    22. Elena Andreou & Eric Ghysels, 2002. "Detecting multiple breaks in financial market volatility dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 579-600.
    23. Burkhard Raunig, 2003. "Testing for Longer Horizon Predictability of Return Volatility with an Application to the German," Working Papers 86, Oesterreichische Nationalbank (Austrian Central Bank).
    24. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    25. Carnero, María Ángeles & Peña, Daniel & Ruiz Ortega, Esther, 2001. "Outliers and conditional autoregressive heteroscedasticity in time series," DES - Working Papers. Statistics and Econometrics. WS ws010704, Universidad Carlos III de Madrid. Departamento de Estadística.
    26. Paulo M.M. Rodrigues & Antonio Rubia, 2010. "The Effects of Additive Outliers and Measurement Errors when Testing for Structural Breaks in Variance," Working Papers w201011, Banco de Portugal, Economics and Research Department.
    27. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    28. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    29. Martha Cecilia García & Aura María Jalal & Luis Alfonso Garzón & Jorge Mario López, 2013. "Métodos para predecir índices Bursátiles," Revista Ecos de Economía, Universidad EAFIT, December.
    30. Christos Avdoulas & Stelios Bekiros & Sabri Boubaker, 2018. "Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets," Annals of Operations Research, Springer, vol. 262(2), pages 307-333, March.
    31. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    32. Philip Hans Franses & Dick van Dijk & André Lucas, 1998. "Short Patches of Outliers, ARCH and Volatility Modeling," Tinbergen Institute Discussion Papers 98-057/4, Tinbergen Institute.
    33. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2008. "Estimating and Forecasting GARCH Volatility in the Presence of Outiers," Working Papers. Serie AD 2008-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    34. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The Role of Outliers and Oil Price Shocks on Volatility of Metal Prices," Energy: Resources and Markets 208768, Fondazione Eni Enrico Mattei (FEEM).
    35. Changli He & Jian Kang & Timo Teräsvirta & Shuhua Zhang, 2018. "The Shifting Seasonal Mean Autoregressive Model and Seasonality in the Central England Monthly Temperature Series, 1772-2016," CREATES Research Papers 2018-15, Department of Economics and Business Economics, Aarhus University.
    36. Daiki Maki & Yasushi Ota, 2019. "Testing for time-varying properties under misspecified conditional mean and variance," Papers 1907.12107, arXiv.org, revised Aug 2019.
    37. Xibin Zhang & Maxwell L. King, 2002. "Influence Diagnostics in GARCH Processes," Monash Econometrics and Business Statistics Working Papers 19/02, Monash University, Department of Econometrics and Business Statistics.
    38. Grané, Aurea & Veiga, Helena, 2009. "Wavelet-based detection of outliers in volatility models," DES - Working Papers. Statistics and Econometrics. WS ws090403, Universidad Carlos III de Madrid. Departamento de Estadística.
    39. Charles, Amelie & Darne, Olivier, 2005. "Outliers and GARCH models in financial data," Economics Letters, Elsevier, vol. 86(3), pages 347-352, March.
    40. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
    41. Francesco Battaglia & Lia Orfei, 2005. "Outlier Detection And Estimation In NonLinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 107-121, January.
    42. Dilip M. Nachane, 2011. "Selected Problems in the Analysis of Nonstationary & Nonlinear Time Series," Journal of Quantitative Economics, The Indian Econometric Society, vol. 9(1), pages 1-17.
    43. Daiki Maki & Yasushi Ota, 2019. "Robust tests for ARCH in the presence of the misspecified conditional mean: A comparison of nonparametric approches," Papers 1907.12752, arXiv.org, revised Sep 2019.
    44. Lisa Crosato & Luigi Grossi, 2019. "Correcting outliers in GARCH models: a weighted forward approach," Statistical Papers, Springer, vol. 60(6), pages 1939-1970, December.
    45. Markku Lanne, 2013. "Noncausality and Inflation Persistence," Discussion Papers of DIW Berlin 1286, DIW Berlin, German Institute for Economic Research.
    46. Vasiliki Chatzikonstanti & Michail Karoglou, 2022. "Can black swans be tamed with a flexible mean‐variance specification?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3202-3227, July.
    47. Lei Shi & Md. Mostafizur Rahman & Wen Gan & Jianhua Zhao, 2015. "Stepwise local influence in generalized autoregressive conditional heteroskedasticity models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 428-444, February.
    48. Franses, Ph.H.B.F. & van Dijk, D.J.C., 1997. "Do We Often Find ARCH Because Of Neglected Outliers?," Econometric Institute Research Papers EI 9706-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    49. Cízek, Pavel, 2011. "Semiparametrically weighted robust estimation of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 774-788, January.
    50. Marcelo Cunha Medeiros & Alvaro Veiga, 2004. "Modelling multiple regimes in financial volatility with a flexible coefficient GARCH model," Textos para discussão 486, Department of Economics PUC-Rio (Brazil).
    51. Fokianos, Konstantions & Fried, Roland, 2009. "Interventions in ingarch processes," Technical Reports 2009,11, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    52. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707.
    53. Olivier Darné & Amélie Charles, 2011. "Large shocks in U.S. macroeconomic time series: 1860-1988," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(1), pages 79-100, January.
    54. Charles, Amélie & Darné, Olivier & Pop, Adrian, 2015. "Risk and ethical investment: Empirical evidence from Dow Jones Islamic indexes," Research in International Business and Finance, Elsevier, vol. 35(C), pages 33-56.
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    60. Battaglia, Francesco, 2005. "Outliers in functional autoregressive time series," Statistics & Probability Letters, Elsevier, vol. 72(4), pages 323-332, May.
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    62. You‐How Go & Jia‐Jun Teo & Kam Fong Chan, 2023. "The effectiveness of crude oil futures hedging during infectious disease outbreaks in the 21st century," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1559-1575, November.
    63. Grané, Aurea & Martín-Barragán, Belén & Veiga, Helena, 2014. "Outliers in multivariate Garch models," DES - Working Papers. Statistics and Econometrics. WS ws140503, Universidad Carlos III de Madrid. Departamento de Estadística.
    64. Anatolyev, Stanislav & Tarasyuk, Irina, 2015. "Missing mean does no harm to volatility!," Economics Letters, Elsevier, vol. 134(C), pages 62-64.
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  92. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Lucas, A., 1996. "Testing for Smooth Transition Nonlinearity in the Presence of Outliers," Econometric Institute Research Papers EI 9622-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. van Dijk, D.J.C. & Franses, Ph.H.B.F., 1997. "Nonlinear Error-Correction Models for Interest Rates in The Netherlands," Econometric Institute Research Papers EI 9704-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
    3. Nidhaleddine Ben Cheikh & Sami Ben Naceur & Oussama Kanaan & Christophe Rault, 2018. "Oil Prices and GCC Stock Markets: New Evidence from Smooth Transition Models," CESifo Working Paper Series 7072, CESifo.
    4. Daiki Maki, 2015. "Wild bootstrap tests for unit root in ESTAR models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 475-490, September.
    5. Chan Wai-Sum & Hung King-Chi, 2011. "On Robust Testing and Modelling of Threshold-Type Non-Linearity in ASEAN Foreign Exchange Markets," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 5(2), pages 1-16, July.
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    7. Shelley, Gary L. & Wallace, Frederick H., 2011. "Further evidence regarding nonlinear trend reversion of real GDP and the CPI," Economics Letters, Elsevier, vol. 112(1), pages 56-59, July.
    8. Daiki Maki & Yasushi Ota, 2021. "Testing for Time-Varying Properties Under Misspecified Conditional Mean and Variance," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1167-1182, April.
    9. Saikkonen, Pentti & Sandberg, Rickard, 2013. "Testing for a unit root in noncausal autoregressive models," Bank of Finland Research Discussion Papers 26/2013, Bank of Finland.
    10. Maki, Daiki, 2015. "Wild bootstrap testing for cointegration in an ESTAR error correction model," Economic Modelling, Elsevier, vol. 47(C), pages 292-298.
    11. Ben Cheikh, Nidhaleddine & Ben Naceur, Sami & Kanaan, Oussama & Rault, Christophe, 2020. "Investigating the Asymmetric Impact of Oil Prices on GCC Stock Markets," IZA Discussion Papers 13853, Institute of Labor Economics (IZA).
    12. Jesús Otero & Jeremy Smith, 2005. "The KPSS Test with Outliers," Computational Economics, Springer;Society for Computational Economics, vol. 26(3), pages 59-67, November.
    13. Mohamed Ali Houfi & Ghassen El Montasser, 2010. "Effets des points aberrants sur les tests de normalité et de linéarité. Applications à la bourse de Tokyo," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 13(36), pages 15-51, June.
    14. Brannolte Cord & Kim Jeong-Ryeol & Hansen Gerd, 1999. "Nonlinear Error Correction Modeling in German Interest Rates / Ein nichtlineares Fehlerkorrekturmodell für die deutsche Zinsstruktur," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 219(3-4), pages 271-283, June.
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    30. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    31. Hirsch, Tristan & Rinke, Saskia, 2017. "Changes in Persistence in Outlier Contaminated Time Series," Hannover Economic Papers (HEP) dp-583, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
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    35. Rinke, Saskia, 2016. "The Influence of Additive Outliers on the Performance of Information Criteria to Detect Nonlinearity," Hannover Economic Papers (HEP) dp-575, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    36. Mendoza Lugo, Omar & Pedauga, Luis Enrique, 2006. "Efecto transferencia (pass-through) del tipo de cambio en los precios de bienes y servicios en Venezuela [Exchange rate pass-through on prices of goods and services in Venezuela]," MPRA Paper 14874, University Library of Munich, Germany.
    37. Zhou, Jian, 2016. "A high-frequency analysis of the interactions between REIT return and volatility," Economic Modelling, Elsevier, vol. 56(C), pages 102-108.
    38. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.
    39. Arghyrou, Michael G. & Gregoriou, Andros, 2007. "Testing for Purchasing Power Parity correcting for non-normality using the wild bootstrap," Economics Letters, Elsevier, vol. 95(2), pages 285-290, May.
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    41. Olivier Darné & Amélie Charles, 2011. "Large shocks in U.S. macroeconomic time series: 1860-1988," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(1), pages 79-100, January.
    42. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
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    44. Paolo Giordani, 2006. "A cautionary note on outlier robust estimation of threshold models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 37-47.
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    57. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

Articles

  1. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    See citations under working paper version above.
  2. Dick van Dijk & Philip Hans Franses, 2019. "Combining expert‐adjusted forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(5), pages 415-421, August.

    Cited by:

    1. Philip Hans Franses, 2021. "Modeling Judgment in Macroeconomic Forecasts," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 401-417, December.
    2. Liu, Anyu & Vici, Laura & Ramos, Vicente & Giannoni, Sauveur & Blake, Adam, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team," Annals of Tourism Research, Elsevier, vol. 88(C).

  3. Anne Opschoor & Pawel Janus & André Lucas & Dick Van Dijk, 2018. "New HEAVY Models for Fat-Tailed Realized Covariances and Returns," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 643-657, October.

    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    3. Mohamed Chikhi & Claude Diebolt & Tapas Mishra, 2019. "Measuring Success: Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers 11-19, Association Française de Cliométrie (AFC).
    4. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    5. André Lucas & Julia Schaumburg & Bernd Schwaab, 2020. "Dynamic clustering of multivariate panel data," Tinbergen Institute Discussion Papers 20-009/III, Tinbergen Institute.
    6. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    7. Mohamed Chikhi & Claude Diebolt & Tapas Mishra, 2019. "Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model," Working Papers of BETA 2019-24, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    8. Manabu Asai & Mike K. P. So, 2021. "Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 271-294, May.
    9. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
    10. Andre Lucas & Anne Opschoor, 2016. "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns," Tinbergen Institute Discussion Papers 16-069/IV, Tinbergen Institute, revised 07 Jul 2017.
    11. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    12. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
    13. M. Karanasos & S. Yfanti & A. Christopoulos, 2021. "The long memory HEAVY process: modeling and forecasting financial volatility," Annals of Operations Research, Springer, vol. 306(1), pages 111-130, November.
    14. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
    15. Asai Manabu & So Mike K. P., 2023. "Realized BEKK-CAW Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 49-77, January.
    16. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    17. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    18. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers of BETA 2019-43, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    19. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.
    20. Marco Piña & Rodrigo Herrera, 2021. "Risk modeling with option-implied correlations and score-driven dynamics," Working Papers Central Bank of Chile 932, Central Bank of Chile.
    21. Rutger-Jan Lange & Bram van Os & Dick van Dijk, 2022. "Robust Observation-Driven Models Using Proximal-Parameter Updates Abstract We propose an observation-driven modelling framework that permits time variation in the model’s parameters using a proximal-p," Tinbergen Institute Discussion Papers 22-066/III, Tinbergen Institute, revised 20 Dec 2022.
    22. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    23. BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    24. Opschoor, Anne & Lucas, André, 2021. "Observation-driven models for realized variances and overnight returns applied to Value-at-Risk and Expected Shortfall forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 622-633.
    25. Xu, Yongdeng, 2022. "The Exponential HEAVY Model: An Improved Approach to Volatility Modeling and Forecasting," Cardiff Economics Working Papers E2022/5, Cardiff University, Cardiff Business School, Economics Section.
    26. Bram van Os, 2023. "Information-Theoretic Time-Varying Density Modeling," Tinbergen Institute Discussion Papers 23-037/III, Tinbergen Institute.
    27. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.
    28. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.

  4. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    See citations under working paper version above.
  5. Ozturk, Sait R. & van der Wel, Michel & van Dijk, Dick, 2017. "Intraday price discovery in fragmented markets," Journal of Financial Markets, Elsevier, vol. 32(C), pages 28-48.

    Cited by:

    1. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    2. Takaki Hayashi & Yuta Koike, 2017. "Multi-scale analysis of lead-lag relationships in high-frequency financial markets," Papers 1708.03992, arXiv.org, revised May 2020.
    3. Alexandre Aidov & Olesya Lobanova, 2021. "Volatility and Depth in Commodity and FX Futures Markets," JRFM, MDPI, vol. 14(11), pages 1-16, November.
    4. Nidhi Aggarwal & Susan Thomas, 2011. "When do stock futures dominate price discovery," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2011-016, Indira Gandhi Institute of Development Research, Mumbai, India.
    5. Ardalankia, Jamshid & Osoolian, Mohammad & Haven, Emmanuel & Jafari, G. Reza, 2020. "Scaling features of price–volume cross correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    6. Zhou, Hao & Elliott, Robert J. & Kalev, Petko S., 2019. "Information or noise: What does algorithmic trading incorporate into the stock prices?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 27-39.
    7. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
    8. Duong, Huu Nhan & Kalev, Petko S. & Tian, Xiao Jason, 2022. "Does the bid–ask spread affect trading in exchange operated dark pools? Evidence from a natural experiment," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    9. Kuck, Konstantin & Schweikert, Karsten, 2023. "Price discovery in equity markets: A state-dependent analysis of spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 149(C).
    10. Gustavo Fruet Dias & Marcelo Fernandes & Cristina M. Scherrer, 2016. "Component shares in continuous time," CREATES Research Papers 2016-25, Department of Economics and Business Economics, Aarhus University.
    11. Hong Li & Yanlin Shi, 2022. "Robust information share measures with an application on the international crude oil markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 555-579, April.
    12. Lien, Donald & Hung, Pi-Hsia & Lin, Zong-Wei, 2020. "Whose trades move stock prices? Evidence from the Taiwan Stock Exchange," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 25-50.
    13. Dimpfl, Thomas & Schweikert, Karsten, 2023. "Information shares for markets with partially overlapping trading hours," Journal of Banking & Finance, Elsevier, vol. 154(C).
    14. Dias, Gustavo Fruet & Fernandes, Marcelo & Scherrer, Cristina Mabel, 2017. "Improving on daily measures of price discovery," Textos para discussão 444, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    15. Donald Lien & Zijun Wang, 2016. "Estimation of Market Information Shares: A Comparison," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(11), pages 1108-1124, November.
    16. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Jamshid Ardalankia & Mohammad Osoolian & Emmanuel Haven & G. Reza Jafari, 2019. "Scaling Features of Price-Volume Cross-Correlation," Papers 1903.01744, arXiv.org, revised Aug 2020.
    18. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    19. Sobti, Neharika & Sehgal, Sanjay & Ilango, Balakrishnan, 2021. "How do macroeconomic news surprises affect round-the-clock price discovery of gold?," International Review of Financial Analysis, Elsevier, vol. 78(C).

  6. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.

    Cited by:

    1. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    2. Felix Haase & Matthias Neuenkirch, 2020. "Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US," Research Papers in Economics 2020-01, University of Trier, Department of Economics.
    3. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    4. Ma, Feng & Lu, Xinjie & Liu, Jia & Huang, Dengshi, 2022. "Macroeconomic attention and stock market return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    5. Hollstein, Fabian & Prokopczuk, Marcel & Tharann, Björn & Wese Simen, Chardin, 2021. "Predictability in commodity markets: Evidence from more than a century," Journal of Commodity Markets, Elsevier, vol. 24(C).
    6. Massimo Guidolin & Erwin Hansen & Gabriel Cabrera, 2023. "Time-Varying Risk Aversion and International Stock Returns," BAFFI CAREFIN Working Papers 23203, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    7. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    8. Oguzhan Cepni & Rangan Gupta & I. Ethem Guney & M. Hasan Yilmaz, 2019. "Forecasting Local Currency Bond Risk Premia of Emerging Markets: The Role of Cross-Country Macro-Financial Linkages," Working Papers 201957, University of Pretoria, Department of Economics.
    9. Fabian Hollstein & Marcel Prokopczuk & Björn Tharann & Chardin Wese Simen, 2019. "Predicting the equity market with option-implied variables," The European Journal of Finance, Taylor & Francis Journals, vol. 25(10), pages 937-965, July.
    10. Li, Zhenxiong & Yao, Xingzhi & Izzeldin, Marwan, 2023. "On the right jump tail inferred from the VIX market," International Review of Financial Analysis, Elsevier, vol. 86(C).
    11. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    12. Cem Cakmakli & Verda Ozturk, 2021. "Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing," Koç University-TUSIAD Economic Research Forum Working Papers 2110, Koc University-TUSIAD Economic Research Forum.
    13. Ahmad, Wasim & Sharma, Sumit Kumar, 2018. "Testing output gap and economic uncertainty as an explicator of stock market returns," Research in International Business and Finance, Elsevier, vol. 45(C), pages 293-306.
    14. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    15. Jonathan A. Batten & Harald Kinateder & Niklas Wagner, 2022. "Beating the Average: Equity Premium Variations, Uncertainty, and Liquidity," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 567-588, September.
    16. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    17. Döpke, Jörg & Müller, Karsten & Tegtmeier, Lars, 2018. "The economic value of business cycle forecasts for potential investors – Evidence from Germany," Research in International Business and Finance, Elsevier, vol. 46(C), pages 445-461.

  7. Dick van Dijk & Robin L. Lumsdaine & Michel van der Wel, 2016. "Market Set‐up in Advance of Federal Reserve Policy Rate Decisions," Economic Journal, Royal Economic Society, vol. 0(592), pages 618-653, May.

    Cited by:

    1. Markus Heckel & Kiyohiko G. Nishimura, 2022. "Unconventional Monetary Policy through Open Market Operations: A Principal Component Analysis," Asian Economic Papers, MIT Press, vol. 21(1), pages 1-28, Winter/Sp.
    2. Markus Heckel & Kiyohiko G. Nishimura, 2020. "Unconventional Monetary Policy through Open Market Operations: A Principal Component Analysis," CARF F-Series CARF-F-501, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

  8. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    See citations under working paper version above.
  9. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    See citations under working paper version above.
  10. Opschoor, Anne & van Dijk, Dick & van der Wel, Michel, 2014. "Predicting volatility and correlations with Financial Conditions Indexes," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 435-447.

    Cited by:

    1. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.
    2. Lubberink, Martien, 2014. "Are banks’ below-par own debt repurchases a cause for prudential concern?," MPRA Paper 59475, University Library of Munich, Germany.
    3. P. Evans & David G. McMillan & Fiona J. McMillan, 2017. "Time-varying correlations and interrelations: Firm-level-based sector evidence," Journal of Asset Management, Palgrave Macmillan, vol. 18(3), pages 209-221, May.
    4. Wang, Bo & Li, Haoran, 2021. "Downside risk, financial conditions and systemic risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    5. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    6. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    7. Afees A. Salisu & Riza Demirer & Rangan Gupta, 2021. "Financial Turbulence, Systemic Risk and the Predictability of Stock Market Volatility," Working Papers 202162, University of Pretoria, Department of Economics.
    8. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    9. Stan Hurn & Peter C. B. Phillips & Shu-Ping Shi, 2016. ""Change Detection and the Causal Impact of the Yield Curve," Cowles Foundation Discussion Papers 2058, Cowles Foundation for Research in Economics, Yale University.
    10. Conrad, Christian & Schienle, Melanie, 2019. "Testing for an omitted multiplicative long-term component in GARCH models," Working Paper Series in Economics 121, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    11. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "On the efficient synthesis of short financial time series: A Dynamic Factor Model approach," Finance Research Letters, Elsevier, vol. 53(C).
    12. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    13. Riza Demirer & Rangan Gupta & He Li & Yu You, 2021. "Financial Vulnerability and Volatility in Emerging Stock Markets: Evidence from GARCH-MIDAS Models," Working Papers 202112, University of Pretoria, Department of Economics.
    14. Hummaira Jabeen, 2023. "US-Financial Conditions and Macro-economy of Emerging Markets," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(1), pages 51-63, March.
    15. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.
    16. Conrad, Christian & Schienle, Melanie, 2015. "Misspecification Testing in GARCH-MIDAS Models," Working Papers 0597, University of Heidelberg, Department of Economics.

  11. Opschoor, Anne & Taylor, Nick & van der Wel, Michel & van Dijk, Dick, 2014. "Order flow and volatility: An empirical investigation," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 185-201.

    Cited by:

    1. Ivan Indriawan & Feng Jiao & Yiuman Tse, 2019. "The impact of the US stock market opening on price discovery of government bond futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 779-802, July.
    2. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 1-14.
    3. van der Wel, M., 2020. "Connecting Silos : On linking macroeconomics and finance, and the role of econometrics therein," ERIM Inaugural Address Series Research in Management 124748, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam..
    4. Füss, Roland & Grabellus, Markus & Mager, Ferdinand & Stein, Michael, 2018. "Something in the air: Information density, news surprises, and price jumps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 50-75.
    5. Hoang, Lai T. & Baur, Dirk G., 2022. "Loaded for bear: Bitcoin private wallets, exchange reserves and prices," Journal of Banking & Finance, Elsevier, vol. 144(C).
    6. Wu, Ming & Ohk, Ki Yool, 2023. "Who benefits more? Shanghai-Hong Kong stock Connect—“Through Train”," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 409-427.
    7. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    8. Glenn Kit Foong Ho & Sirimon Treepongkaruna & Marvin Wee & Chaiyuth Padungsaksawasdi, 2022. "The effect of short selling on volatility and jumps," Australian Journal of Management, Australian School of Business, vol. 47(1), pages 34-52, February.
    9. Adam Clements & Joanne Fuller & Vasilios Papalexiou, 2015. "Public news flow in intraday component models for trading activity and volatility," NCER Working Paper Series 106, National Centre for Econometric Research.
    10. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    11. Dion Bongaerts & Richard Roll & Dominik Rösch & Mathijs van Dijk & Darya Yuferova, 2022. "How Do Shocks Arise and Spread Across Stock Markets? A Microstructure Perspective," Management Science, INFORMS, vol. 68(4), pages 3071-3089, April.
    12. Conrad, Christian & Schienle, Melanie, 2015. "Misspecification Testing in GARCH-MIDAS Models," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112919, Verein für Socialpolitik / German Economic Association.
    13. Firouzi, Shahrokh & Wang, Xiangning, 2021. "The interrelationship between order flow, exchange rate, and the role of American economic news," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    14. Chang, Ya-Ting & Gau, Yin-Feng & Hsu, Chih-Chiang, 2017. "Liquidity Commonality in Foreign Exchange Markets During the Global Financial Crisis and the Sovereign Debt Crisis: Effects of Macroeconomic and Quantitative Easing Announcements," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 172-192.

  12. Diks, Cees & Panchenko, Valentyn & Sokolinskiy, Oleg & van Dijk, Dick, 2014. "Comparing the accuracy of multivariate density forecasts in selected regions of the copula support," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 79-94.

    Cited by:

    1. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    2. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    3. Collins, Sean & Gallagher, Emily, 2016. "Assessing the credit risk of money market funds during the eurozone crisis," Journal of Financial Stability, Elsevier, vol. 25(C), pages 150-165.
    4. Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021. "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 56-73.
    5. Carol Alexander & Michael Coulon & Yang Han & Xiaochun Meng, 2021. "Evaluating the Discrimination Ability of Proper Multivariate Scoring Rules," Papers 2101.12693, arXiv.org.
    6. Xiaochun Meng & James W. Taylor & Souhaib Ben Taieb & Siran Li, 2020. "Scores for Multivariate Distributions and Level Sets," Papers 2002.09578, arXiv.org, revised Jun 2023.
    7. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    8. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.
    9. Oleg Sokolinskiy, 2020. "Conditional dependence in post-crisis markets: dispersion and correlation skew trades," Review of Quantitative Finance and Accounting, Springer, vol. 55(2), pages 389-426, August.
    10. Aslanidis, Nektarios & Martinez, Oscar, 2021. "Correlation regimes in international equity and bond returns," Economic Modelling, Elsevier, vol. 97(C), pages 397-410.

  13. Scholtus, Martin & van Dijk, Dick & Frijns, Bart, 2014. "Speed, algorithmic trading, and market quality around macroeconomic news announcements," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 89-105.
    See citations under working paper version above.
  14. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2014. "Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 360-388, June.
    See citations under working paper version above.
  15. Jana P. Fidrmuc & Alessandro Palandri & Peter Roosenboom & Dick van Dijk, 2013. "When Do Managers Seek Private Equity Backing in Public-to-Private Transactions?," Review of Finance, European Finance Association, vol. 17(3), pages 1099-1139.
    See citations under working paper version above.
  16. van den Hauwe, Sjoerd & Paap, Richard & van Dijk, Dick, 2013. "Bayesian forecasting of federal funds target rate decisions," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 19-40.
    See citations under working paper version above.
  17. Marc B.J. Schauten & Dick van Dijk & Jan†Paul van der Waal, 2013. "Corporate Governance and the Value of Excess Cash Holdings of Large European Firms," European Financial Management, European Financial Management Association, vol. 19(5), pages 991-1016, November.
    See citations under working paper version above.
  18. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2013. "Structural Breaks in the International Dynamics of Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 646-659, May.

    Cited by:

    1. Elena Andreou & Alessandra Pelloni & Marianne Sensier, 2008. "Is Volatility Good for Growth? Evidence from the G7," Centre for Growth and Business Cycle Research Discussion Paper Series 97, Economics, The University of Manchester.
    2. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Discussion Paper 2016-019, Tilburg University, Center for Economic Research.
    3. Erdenebat Bataa & Denise R. Osborn & Marianne Sensier & Dick van Dijk, 2008. "Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation," Centre for Growth and Business Cycle Research Discussion Paper Series 109, Economics, The University of Manchester.
    4. Neil Lawton & Liam A. Gallagher, 2020. "The negative side of inflation targeting: revisiting inflation uncertainty in the EMU," Applied Economics, Taylor & Francis Journals, vol. 52(29), pages 3186-3203, June.
    5. Erdenebat Bataa & Marwan Izzeldin & Denise Osborn, 2015. "Changes in the global oil market," Working Papers 75761696, Lancaster University Management School, Economics Department.
    6. Hailemariam, Abebe & Smyth, Russell, 2019. "What drives volatility in natural gas prices?," Energy Economics, Elsevier, vol. 80(C), pages 731-742.
    7. Bataa, Erdenebat & Wohar, Mark & Vivian, Andrew, 2015. "Changes in the relationship between short-term interest rate, inflation and growth: Evidence from the UK, 1820-2014," MPRA Paper 72422, University Library of Munich, Germany.
    8. Erdenebat Bataa, 2019. "Growth and Inflation Regimes in Greater Tumen Initiative Area," The Northeast Asian Economic Review, ERINA - Economic Research Institute for Northeast Asia, vol. 7(1), pages 15-29, November.
    9. Altansukh, Gantungalag & Becker, Ralf & Bratsiotis, George J. & Osborn, Denise R., 2017. "What is the Globalisation of Inflation?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74, pages 1-27.
    10. S Coleman & K Sirichand, 2015. "Investigating Multiple Changes in Persistence in International Yields," Economic Issues Journal Articles, Economic Issues, vol. 20(1), pages 65-90, March.
    11. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    12. Erdenebat Bataa & Denise R.Osborn & Marianne Sensier, 2016. "China's Increasing Global Influence: Changes in International Growth Spillovers," Centre for Growth and Business Cycle Research Discussion Paper Series 221, Economics, The University of Manchester.
    13. Steffen Henzel & Elisabeth Wieland, 2013. "Synchronization and Changes in International Inflation Uncertainty," CESifo Working Paper Series 4194, CESifo.
    14. Dominik Blatt & Kausik Chaudhuri & Hans Manner, 2021. "Spillover in the UK Housing Market," Graz Economics Papers 2021-13, University of Graz, Department of Economics.
    15. Christina Christou & David Gabauer & Rangan Gupta, 2019. "Time-Varying Impact of Uncertainty Shocks on Macroeconomic Variables of the United Kingdom: Evidence from Over 150 Years of Monthly Data," Working Papers 201962, University of Pretoria, Department of Economics.
    16. Vasilios Plakandaras & Rangan Gupta & Mehmet Balcilar & Qiang Ji, 2021. "Evolving United States Stock Market Volatility: The Role of Conventional and Unconventional Monetary Policies," Working Papers 202113, University of Pretoria, Department of Economics.
    17. Aharon, David Y. & Qadan, Mahmoud, 2022. "Infection, invasion, and inflation: Recent lessons," Finance Research Letters, Elsevier, vol. 50(C).
    18. Bataa, Erdenebat & Osborn, Denise R. & Sensier, Marianne, 2018. "China's increasing global influence: Changes in international growth linkages," Economic Modelling, Elsevier, vol. 74(C), pages 194-206.
    19. Cheolbeom Park & Erdenebat Bataa, 2017. "Is the Recent Low Oil Price Attributable to the Shale Revolution?," Discussion Paper Series 1704, Institute of Economic Research, Korea University.
    20. Marinela Adriana Finta & Bart Frijns & Alireza Tourani-Rad, 2019. "Time-varying contemporaneous spillovers during the European Debt Crisis," Empirical Economics, Springer, vol. 57(2), pages 423-448, August.
    21. Gantungalag Altansukh & Ralf Becker & George Bratsiotis & Denise R. Osborn, 2018. "Structural Breaks in International Inflation Linkages for OECD Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 240, Economics, The University of Manchester.
    22. Inês da Cunha Cabral & João Nicolau, 2022. "Inflation in the G7 and the expected time to reach the reference rate: A nonparametric approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1608-1620, April.
    23. Bataa, Erdenebat, 2012. "The Composite Leading Indicator of Mongolia," MPRA Paper 72415, University Library of Munich, Germany.
    24. Bataa, Erdenebat, 2012. "Macroeconomic risks of Mongolia and ways to mitigate them," MPRA Paper 72386, University Library of Munich, Germany, revised 25 Jun 2013.
    25. Williams Ohemeng & Elvis Kwame Agyapong & Kenneth Ofori-Boateng, 2021. "Exchange rate and inflation dynamics: does the month or quarter of the year matter?," SN Business & Economics, Springer, vol. 1(6), pages 1-24, June.
    26. Blatt, Dominik & Candelon, Bertrand & Manner, Hans, 2015. "Detecting contagion in a multivariate time series system: An application to sovereign bond markets in Europe," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 1-13.

  19. Bannouh, Karim & Martens, Martin & van Dijk, Dick, 2013. "Forecasting volatility with the realized range in the presence of noise and non-trading," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 535-551.
    See citations under working paper version above.
  20. Peter Exterkate & Dick Van Dijk & Christiaan Heij & Patrick J. F. Groenen, 2013. "Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(3), pages 193-214, April.
    See citations under working paper version above.
  21. Çakmaklı, Cem & Paap, Richard & van Dijk, Dick, 2013. "Measuring and predicting heterogeneous recessions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2195-2216.
    See citations under working paper version above.
  22. Nalan Baştürk & Richard Paap & Dick van Dijk, 2012. "Structural differences in economic growth: an endogenous clustering approach," Applied Economics, Taylor & Francis Journals, vol. 44(1), pages 119-134, January.

    Cited by:

    1. Basturk, N. & Paap, R. & van Dijk, D.J.C., 2010. "Financial Development and Convergence Clubs," Econometric Institute Research Papers EI 2010-52, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
    3. Tim Salimans, 2011. "Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions," Tinbergen Institute Discussion Papers 11-012/4, Tinbergen Institute.
    4. Michelle Gilmartin & Dimitris Korobilis, 2012. "On Regional Unemployment: An Empirical Examination of the Determinants of Geographical Differentials in the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 59(2), pages 179-195, May.

  23. Bram van Dijk & Philip Hans Franses & Richard Paap & Dick van Dijk, 2011. "Modelling regional house prices," Applied Economics, Taylor & Francis Journals, vol. 43(17), pages 2097-2110.
    See citations under working paper version above.
  24. Christiaan Heij & Dick van Dijk & Patrick J.F. Groenen, 2011. "Forecasting with Leading Indicators by means of the Principal Covariate Index," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 73-92.

    Cited by:

    1. H. Burcu Gurcihan & Gonul Sengul & Arzu Yavuz, 2013. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Working Papers 1341, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  25. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.

    Cited by:

    1. Knut Lehre Seip & Yunus Yilmaz & Michael Schröder, 2019. "Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?," Economies, MDPI, vol. 7(4), pages 1-18, October.
    2. Lise Pichette & Marie-Noëlle Robitaille, 2017. "Assessing the Business Outlook Survey Indicator Using Real-Time Data," Discussion Papers 17-5, Bank of Canada.
    3. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Philip Hans Franses & Eva Janssens, 2019. "Spurious principal components," Applied Economics Letters, Taylor & Francis Journals, vol. 26(1), pages 37-39, January.
    5. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2012. "Evaluating Macroeconomic Forecasts: A Concise Review of Some Recent Developments," Documentos de Trabajo del ICAE 2012-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    6. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    7. Soh, Ann-Ni, 2020. "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper 103854, University Library of Munich, Germany.
    8. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
    9. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    10. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.

  26. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    See citations under working paper version above.
  27. Roger Lord & Remmert Koekkoek & Dick Van Dijk, 2010. "A comparison of biased simulation schemes for stochastic volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 177-194.
    See citations under working paper version above.
  28. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
    See citations under working paper version above.
  29. Boswijk, H. Peter & Franses, Philip Hans & van Dijk, Dick, 2010. "Cointegration in a historical perspective," Journal of Econometrics, Elsevier, vol. 158(1), pages 156-159, September.
    See citations under working paper version above.
  30. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.

    Cited by:

    1. Manabu Asai & Michael McAleer, 2011. "Dynamic Conditional Correlations for Asymmetric Processes," Documentos de Trabajo del ICAE 2011-30, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Asai, M. & McAleer, M.J. & Medeiros, M.C., 2010. "Asymmetry and Long Memory in Volatility Modelling," Econometric Institute Research Papers EI 2010-60, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Thilo A. Schmitt & Rudi Schafer & Holger Dette & Thomas Guhr, 2015. "Quantile Correlations: Uncovering temporal dependencies in financial time series," Papers 1507.04990, arXiv.org.
    4. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    5. Duan, Yinying & Chen, Wang & Zeng, Qing & Liu, Zhicao, 2018. "Leverage effect, economic policy uncertainty and realized volatility with regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 148-154.
    6. Rossi, Eduardo & Santucci de Magistris, Paolo, 2013. "Long memory and tail dependence in trading volume and volatility," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 94-112.
    7. Robinson Kruse & Christian Leschinski & Michael Will, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," CREATES Research Papers 2016-17, Department of Economics and Business Economics, Aarhus University.
    8. Gonzalez-Perez, Maria T. & Guerrero, David E., 2013. "Day-of-the-week effect on the VIX. A parsimonious representation," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 243-260.
    9. Zhang, Lixia & Luo, Qin & Guo, Xiaozhu & Umar, Muhammad, 2022. "Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices," Resources Policy, Elsevier, vol. 77(C).
    10. Duong T Le, 2015. "Ex-ante Determinants of Volatility in the Crude Oil Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(1), pages 1-13, January.
    11. Massimiliano Caporin & Gabriel G. Velo, 2011. "Modeling and forecasting realized range volatility," "Marco Fanno" Working Papers 0128, Dipartimento di Scienze Economiche "Marco Fanno".
    12. Song, Shijia & Tian, Fei & Li, Handong, 2021. "An intraday-return-based Value-at-Risk model driven by dynamic conditional score with censored generalized Pareto distribution," Journal of Asian Economics, Elsevier, vol. 74(C).
    13. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    14. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    15. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    16. Oleg Sokolinskiy & Dick van Dijk, 2011. "Forecasting Volatility with Copula-Based Time Series Models," Tinbergen Institute Discussion Papers 11-125/4, Tinbergen Institute.
    17. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Tinbergen Institute Discussion Papers 14-037/III, Tinbergen Institute.
    18. Mei, Dexiang & Zeng, Qing & Cao, Xiang & Diao, Xiaohua, 2019. "Uncertainty and oil volatility: New evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 155-163.
    19. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    20. Asai, M. & Chang, C-L. & McAleer, M.J., 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Econometric Institute Research Papers TI 2017-038/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    21. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    22. Lv, Wendai, 2018. "Does the OVX matter for volatility forecasting? Evidence from the crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 916-922.
    23. Davide Delle Monache & Stefano Grassi & Paolo Santucci, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," Studies in Economics 1511, School of Economics, University of Kent.
    24. Heather M. Anderson & Farshid Vahid, 2013. "Common non-linearities in multiple series of stock market volatility," Monash Econometrics and Business Statistics Working Papers 1/13, Monash University, Department of Econometrics and Business Statistics.
    25. Lin, Xiaoqiang & Fei, Fangyu, 2013. "Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis," Economic Modelling, Elsevier, vol. 31(C), pages 265-275.
    26. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
    27. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    28. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    29. J. Eduardo Vera-Vald'es, 2017. "On Long Memory Origins and Forecast Horizons," Papers 1712.08057, arXiv.org.
    30. Li, Xingyi & Zakamulin, Valeriy, 2020. "The term structure of volatility predictability," International Journal of Forecasting, Elsevier, vol. 36(2), pages 723-737.
    31. Liu, Yue & Sun, Huaping & Zhang, Jijian & Taghizadeh-Hesary, Farhad, 2020. "Detection of volatility regime-switching for crude oil price modeling and forecasting," Resources Policy, Elsevier, vol. 69(C).
    32. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    33. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    34. Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.
    35. Manabu Asai & Michael McAleer, 2017. "Forecasting the volatility of Nikkei 225 futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(11), pages 1141-1152, November.
    36. Degiannakis, Stavros & Potamia, Artemis, 2017. "Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: Inter-day versus intra-day data," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 176-190.
    37. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
    38. Francesco Audrino & Simon D. Knaus, 2016. "Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
    39. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers 2014-27, Department of Economics and Business Economics, Aarhus University.
    40. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    41. J. Arteche, 2012. "Semiparametric Inference in Correlated Long Memory Signal Plus Noise Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 440-474.
    42. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
    43. Yu, Miao & Song, Jinguo, 2018. "Volatility forecasting: Global economic policy uncertainty and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 316-323.
    44. Seyfi, Seyed Mohammad Sina & Sharifi, Azin & Arian, Hamidreza, 2021. "Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1056-1079.
    45. Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Estimation of long memory in integrated variance," CREATES Research Papers 2011-11, Department of Economics and Business Economics, Aarhus University.
    46. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    47. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    48. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    49. Chen, Chun-Hung & Yu, Wei-Choun & Zivot, Eric, 2012. "Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks," International Journal of Forecasting, Elsevier, vol. 28(2), pages 366-383.
    50. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    51. Liu, Zhicao & Ye, Yong & Ma, Feng & Liu, Jing, 2017. "Can economic policy uncertainty help to forecast the volatility: A multifractal perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 181-188.
    52. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    53. Vortelinos, Dimitrios I., 2015. "Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets," Review of Financial Economics, Elsevier, vol. 27(C), pages 58-67.
    54. Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
    55. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    56. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    57. Song, Shijia & Li, Handong, 2022. "Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution," International Review of Financial Analysis, Elsevier, vol. 82(C).
    58. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    59. Dimitrios I. Vortelinos, 2015. "Out‐of‐sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini‐futures markets," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 58-67, November.
    60. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    61. Cem Cakmakli & Verda Ozturk, 2021. "Economic Value of Modeling the Joint Distribution of Returns and Volatility: Leverage Timing," Koç University-TUSIAD Economic Research Forum Working Papers 2110, Koc University-TUSIAD Economic Research Forum.
    62. Qianjie Geng & Xianfeng Hao & Yudong Wang, 2024. "Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 309-325, March.
    63. Wang Pu & Yixiang Chen & Feng Ma, 2016. "Forecasting the realized volatility in the Chinese stock market: further evidence," Applied Economics, Taylor & Francis Journals, vol. 48(33), pages 3116-3130, July.
    64. Caporin, Massimiliano & Velo, Gabriel G., 2015. "Realized range volatility forecasting: Dynamic features and predictive variables," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 98-112.
    65. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
    66. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    67. Thilo A. Schmitt & Rudi Schäfer & Holger Dette & Thomas Guhr, 2015. "Quantile Correlations: Uncovering Temporal Dependencies In Financial Time Series," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1-16, November.
    68. Seyed Mohammad Sina Seyfi & Azin Sharifi & Hamidreza Arian, 2020. "Portfolio Risk Measurement Using a Mixture Simulation Approach," Papers 2011.07994, arXiv.org.
    69. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    70. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    71. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    72. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    73. Feng Ma & Yu Wei & Wang Chen & Feng He, 2018. "Forecasting the volatility of crude oil futures using high-frequency data: further evidence," Empirical Economics, Springer, vol. 55(2), pages 653-678, September.
    74. Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.
    75. Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
    76. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    77. Ouandlous, Arav & Barkoulas, John T. & Alhaj-Yaseen, Yaseen, 2018. "Persistence and discontinuity in the VIX dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 333-344.
    78. Ke Yang & Langnan Chen, 2014. "Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect," International Review of Finance, International Review of Finance Ltd., vol. 14(3), pages 345-392, September.
    79. Dimitrios Louzis & Spyros Xanthopoulos-Sisinis & Apostolos Refenes, 2011. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Post-Print hal-00709559, HAL.
    80. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    81. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    82. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    83. Wamg, Jianxin, 2011. "Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors," Asian Development Review, Asian Development Bank, vol. 28(2), pages 32-57.
    84. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.

  31. Alberto Musso & Livio Stracca & Dick van Dijk, 2009. "Instability and Nonlinearity in the Euro-Area Phillips Curve," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 181-212, June.
    See citations under working paper version above.
  32. Karim Bannouh & Dick van Dijk & Martin Martens, 2009. "Range-Based Covariance Estimation Using High-Frequency Data: The Realized Co-Range -super-," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 341-372, Fall.

    Cited by:

    1. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2014. "Precious Metals Under the Microscope: A High-Frequency Analysis," Working Papers on Finance 1409, University of St. Gallen, School of Finance.
    2. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    3. Vander Elst, Harry & Veredas, David, 2014. "Disentangled jump-robust realized covariances and correlations with non-synchronous prices," DES - Working Papers. Statistics and Econometrics. WS ws142416, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Vortelinos, Dimitrios I., 2010. "The properties of realized correlation: Evidence from the French, German and Greek equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 273-290, August.
    5. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    6. Jan Novotný & Giovanni Urga, 2018. "Testing for Co-jumps in Financial Markets," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 118-128.
    7. I‐Ming Jiang & Jui‐Cheng Hung & Chuan‐San Wang, 2014. "Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(11), pages 1077-1094, November.
    8. Wenjing Wang & Minjing Tao, 2020. "Forecasting Realized Volatility Matrix With Copula-Based Models," Papers 2002.08849, arXiv.org.
    9. V. Popov, 2016. "Correlation estimation using components of Japanese candlesticks," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1615-1630, October.
    10. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2013. "Stylized Facts and Dynamic Modeling of High-frequency Data on Precious Metals," Working Papers on Finance 1318, University of St. Gallen, School of Finance.
    11. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    12. Ziegelmann, Flávio Augusto & Borges, Bruna & Caldeira, João F., 2015. "Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    13. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.

  33. Markwat, Thijs & Kole, Erik & van Dijk, Dick, 2009. "Contagion as a domino effect in global stock markets," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 1996-2012, November.
    See citations under working paper version above.
  34. Karen Watkins & Jaap Spronk & Dick Van Dijk, 2009. "Corporate governance and performance during normal and crisis periods: evidence from an emerging market perspective," International Journal of Corporate Governance, Inderscience Enterprises Ltd, vol. 1(4), pages 382-399.

    Cited by:

    1. Karen Watkins Fassler, 2013. "Efecto de la Ley corporativa y la concentración de la propiedad en el desempeño empresarial en Chile y México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-22, November.

  35. de Zwart, Gerben & Markwat, Thijs & Swinkels, Laurens & van Dijk, Dick, 2009. "The economic value of fundamental and technical information in emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 28(4), pages 581-604, June.
    See citations under working paper version above.
  36. Paap, Richard & Segers, Rene & van Dijk, Dick, 2009. "Do Leading Indicators Lead Peaks More Than Troughs?," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 528-543.
    See citations under working paper version above.
  37. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2008. "Macroeconomic forecasting with matched principal components," International Journal of Forecasting, Elsevier, vol. 24(1), pages 87-100.

    Cited by:

    1. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    2. Philip Hans Franses & Rianne Legerstee, 2010. "A Unifying View On Multi‐Step Forecasting Using An Autoregression," Journal of Economic Surveys, Wiley Blackwell, vol. 24(3), pages 389-401, July.
    3. Guidolin, Massimo & Hyde, Stuart, 2012. "Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3546-3566.
    4. Richard G. Anderson & Jane M. Binner & Björn Hagströmer & Birger Nilsson, 2009. "Dynamics in systematic liquidity," Working Papers 2009-025, Federal Reserve Bank of St. Louis.
    5. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    6. Guidolin, Massimo & Hyde, Stuart, 2012. "Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 695-716.

  38. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 199-229.
    See citations under working paper version above.
  39. Heij, Christiaan & Groenen, Patrick J.F. & van Dijk, Dick, 2007. "Forecast comparison of principal component regression and principal covariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3612-3625, April.
    See citations under working paper version above.
  40. van Dijk, Dick & Hans Franses, Philip & Peter Boswijk, H., 2007. "Absorption of shocks in nonlinear autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4206-4226, May.

    Cited by:

    1. Bel, K. & Paap, R., 2013. "Modeling the impact of forecast-based regime switches on macroeconomic time series," Econometric Institute Research Papers EI 2013-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. E Pavlidis & I Paya & D Peel, 2009. "Real Exchange Rates and Time-Varying Trade Costs," Working Papers 600537, Lancaster University Management School, Economics Department.
    3. Till Strohsal & Lars Winkelmann, 2012. "Assessing the Anchoring of Inflation Expectations," SFB 649 Discussion Papers SFB649DP2012-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Franchi, Massimo & Ordóñez, Javier, 2011. "Multiple equilibria in Spanish unemployment," Structural Change and Economic Dynamics, Elsevier, vol. 22(1), pages 71-80, February.
    5. Fanelli, Luca & Paruolo, Paolo, 2007. "Speed of Adjustment in Cointegrated Systems," MPRA Paper 9174, University Library of Munich, Germany.
    6. Efthymios Pavlidis & Nicos Pavlidis, 2012. "Dynamic Estimation of Trade Costs from Real Exchange Rates," Working Papers 21883757, Lancaster University Management School, Economics Department.
    7. Strohsal, Till & Winkelmann, Lars, 2015. "Assessing the anchoring of inflation expectations," Journal of International Money and Finance, Elsevier, vol. 50(C), pages 33-48.
    8. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    9. Pragidis, I.C. & Tsintzos, P. & Plakandaras, B., 2018. "Asymmetric effects of government spending shocks during the financial cycle," Economic Modelling, Elsevier, vol. 68(C), pages 372-387.
    10. M. Berument & Yeliz Yalcin & Julide Yildirim, 2011. "The inflation and inflation uncertainty relationship for Turkey: a dynamic framework," Empirical Economics, Springer, vol. 41(2), pages 293-309, October.
    11. Lo Ming Chien, 2008. "Nonlinear PPP Deviations: A Monte Carlo Investigation of Their Unconditional Half-Life," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(4), pages 1-31, December.
    12. Markku Lanne, 2013. "Noncausality and Inflation Persistence," Discussion Papers of DIW Berlin 1286, DIW Berlin, German Institute for Economic Research.
    13. Ivan Paya & David A. Peel, 2011. "Systematic sampling of nonlinear models: Evidence on speed of adjustment in index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(2), pages 192-203, February.
    14. Dong, Minyi & Chang, Chun-Ping & Gong, Qiang & Chu, Yin, 2019. "Revisiting global economic activity and crude oil prices: A wavelet analysis," Economic Modelling, Elsevier, vol. 78(C), pages 134-149.
    15. Bel, Koen & Paap, Richard, 2016. "Modeling the impact of forecast-based regime switches on US inflation," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1306-1316.
    16. Woo, Kai-Yin & Lee, Shu-Kam & Chan, Alan, 2014. "Non-linear adjustments to intranational PPP," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 360-371.
    17. Rahman, Sajjadur & Serletis, Apostolos, 2010. "The asymmetric effects of oil price and monetary policy shocks: A nonlinear VAR approach," Energy Economics, Elsevier, vol. 32(6), pages 1460-1466, November.
    18. Efthymios Pavlidis & Ivan Paya & David Peel, 2010. "Further empirical evidence on the consumption-real exchange rate anomaly," Working Papers 447022, Lancaster University Management School, Economics Department.

  41. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
    See citations under working paper version above.
  42. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, vol. 138(1), pages 181-207, May.
    See citations under working paper version above.
  43. Swanson, Norman R. & van Dijk, Dick, 2006. "Are Statistical Reporting Agencies Getting It Right? Data Rationality and Business Cycle Asymmetry," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 24-42, January.
    See citations under working paper version above.
  44. Harvey, David I. & van Dijk, Dick, 2006. "Sample size, lag order and critical values of seasonal unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2734-2751, June.

    Cited by:

    1. Tomás Barrio Castro & Andrii Bodnar & Andreu Sansó, 2017. "Numerical distribution functions for seasonal unit root tests with OLS and GLS detrending," Computational Statistics, Springer, vol. 32(4), pages 1533-1568, December.
    2. Díaz-Emparanza Herrero, Ignacio, 2011. "Numerical Distribution Functions for Seasonal Unit Root Tests," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    3. Peter Sephton, 2008. "Critical values of the augmented fractional Dickey–Fuller test," Empirical Economics, Springer, vol. 35(3), pages 437-450, November.
    4. Díaz-Emparanza, Ignacio & Moral, M. Paz, 2014. "Numerical distribution functions for seasonal stability tests," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 44-49.
    5. Jesús Otero & Jeremy Smith, 2012. "Response surface models for the Leybourne unit root tests and lag order dependence," Computational Statistics, Springer, vol. 27(3), pages 473-486, September.
    6. Sebastian Kripfganz & Daniel C. Schneider, 2019. "Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models," Discussion Papers 1901, University of Exeter, Department of Economics.
    7. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707.
    8. Pui Sun Tam, 2013. "Finite-sample distribution of the augmented Dickey--Fuller test with lag optimization," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3495-3511, August.
    9. Jesús Otero & Jeremy Smith, 2013. "Response Surface Estimates of the Cross-Sectionally Augmented IPS Tests for Panel Unit Roots," Computational Economics, Springer;Society for Computational Economics, vol. 41(1), pages 1-9, January.
    10. Gabriel Pons, 2006. "Testing Monthly Seasonal Unit Roots With Monthly and Quarterly Information," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(2), pages 191-209, March.
    11. Nazlioglu, Saban & Lee, Junsoo, 2020. "Response surface estimates of the LM unit root tests," Economics Letters, Elsevier, vol. 192(C).
    12. Díaz-Emparanza Herrero, Ignacio & Moral Zuazo, María Paz, 2013. "Seasonal Stability Tests in gretl. An Application to International Tourism Data," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).

  45. Paap, Richard & Franses, Philip Hans & van Dijk, Dick, 2005. "Does Africa grow slower than Asia, Latin America and the Middle East? Evidence from a new data-based classification method," Journal of Development Economics, Elsevier, vol. 77(2), pages 553-570, August.

    Cited by:

    1. Serge Rey & Florent Deisting, 2012. "GDP per Capita among African Countries over the Period 1950-2008: Highlights of Convergence Clubs," Post-Print hal-01881912, HAL.
    2. Owen, Ann L. & Temesvary, Judit, 2012. "Foreign lending, local lending, and economic growth," MPRA Paper 39978, University Library of Munich, Germany.
    3. Bram van Dijk & Philip Hans Franses & Richard Paap & Dick van Dijk, 2011. "Modelling regional house prices," Applied Economics, Taylor & Francis Journals, vol. 43(17), pages 2097-2110.
    4. Amélie Charles & Olivier Darné & Jean-François Hoarau, 2010. "Does the real GDP per capita convergence hold in the Common Market for Eastern and Southern Africa?," Post-Print hal-00797485, HAL.
    5. Basturk, N. & Paap, R. & van Dijk, D.J.C., 2010. "Financial Development and Convergence Clubs," Econometric Institute Research Papers EI 2010-52, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Tedesco, Ilaria & Pelloni, Alessandra & Trovato, Giovanni, 2015. "Oecd Agricultural Subsidies And Poverty Rates In Lower Income Countries," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(2), pages 1-19, April.
    7. Vidoli, Francesco & Pignataro, Giacomo & Benedetti, Roberto, 2022. "Identification of spatial regimes of the production function of Italian hospitals through spatially constrained cluster-wise regression," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    8. Rodríguez, Francisco & Shelton, Cameron A., 2013. "Cleaning up the kitchen sink: Specification tests and average derivative estimators for growth econometrics," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 260-273.
    9. Owen, Ann L. & Temesvary, Judit, 2014. "Heterogeneity in the growth and finance relationship: How does the impact of bank finance vary by country and type of lending?," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 275-288.
    10. Dick van Dijk & Dennis Fok & Philip Hans Franses, 2005. "A multi-level panel STAR model for US manufacturing sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 811-827.
    11. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    12. Chris Papageorgiou & Winford H. Masanjala, 2006. "Initial Conditions, European Colonialism and Africa's Growth," Departmental Working Papers 2006-01, Department of Economics, Louisiana State University.
    13. Walheer, Barnabé, 2016. "Growth and convergence of the OECD countries: A multi-sector production-frontier approach," European Journal of Operational Research, Elsevier, vol. 252(2), pages 665-675.
    14. Amélie Charles & Olivier Darne & Jean-François Hoarau, 2012. "Convergence of real per capita GDP within COMESA countries: A panel unit root evidence," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(1), pages 53-71, August.
    15. Pérez-Urdiales, María & Baerenklau, Kenneth A., 2019. "Learning to live within your (water) budget: Evidence from allocation-based rates," Resource and Energy Economics, Elsevier, vol. 57(C), pages 205-221.
    16. Castellacci, Fulvio, 2008. "Technology clubs, technology gaps and growth trajectories," MPRA Paper 27595, University Library of Munich, Germany.
    17. Antonio Sianes & Luis A. Fernández-Portillo & Adela Toscano-Valle & Elena Pérez-Velasco, 2023. "Heterogeneity in financing for development strategies as a hindering factor to achieve a global agreement on the 2030 Agenda," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    18. Franses, Ph.H.B.F., 2004. "Forecasting in marketing," Econometric Institute Research Papers EI 2004-40, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Phillips, Kerk L. & Chen, Baizhu, 2011. "Regional growth in China: An empirical investigation using multiple imputation and province-level panel data," Research in Economics, Elsevier, vol. 65(3), pages 243-253, September.
    20. Gianfranco Di Vaio & Kerstin Enflo, 2009. "Did Globalization Lead to Segmentation? Identifying Cross-Country Growth Regimes in the Long-Run," Discussion Papers 09-08, University of Copenhagen. Department of Economics.
    21. Yuki, Kazuhiro, 2012. "Education, inequality, and development in a dual economy," MPRA Paper 39062, University Library of Munich, Germany.
    22. Davis, Lewis & Owen, Ann L. & Videras, Julio, 2007. "Do all countries follow the same growth process?," MPRA Paper 11589, University Library of Munich, Germany, revised Sep 2008.
    23. Owen, Ann L. & Videras, Julio, 2012. "The quality of growth," MPRA Paper 38342, University Library of Munich, Germany.
    24. Nalan Basturk & Richard Paap & Dick van Dijk, 2008. "Structural Differences in Economic Growth," Tinbergen Institute Discussion Papers 08-085/4, Tinbergen Institute.
    25. Michele Battisti & Christopher F. Parmeter, 2011. "Income Polarization, Convergence Tools and Mixture Analysis," Working Papers 2011-17, University of Miami, Department of Economics.
    26. Barnabé Walheer, 2016. "Multi-Sector Nonparametric Production-Frontier Analysis of the Economic Growth and the Convergence of the European Countries," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 498-524, October.
    27. Leonardo Becchetti & Luisa Corrado & Fiammetta Rossetti, 2008. "Easterlin-types and Frustrated Achievers: the Heterogeneous E¤ects of Income Changes on Life Satisfaction," CEIS Research Paper 127, Tor Vergata University, CEIS, revised 09 Sep 2008.
    28. Tim Salimans, 2011. "Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions," Tinbergen Institute Discussion Papers 11-012/4, Tinbergen Institute.
    29. Kerekes, Monika, 2012. "Growth miracles and failures in a Markov switching classification model of growth," Journal of Development Economics, Elsevier, vol. 98(2), pages 167-177.
    30. David Audretsch & Mark Sanders & Lu Zhang, 2021. "International product life cycles, trade and development stages," The Journal of Technology Transfer, Springer, vol. 46(5), pages 1630-1673, October.
    31. Jerzmanowski, Michal, 2006. "Empirics of hills, plateaus, mountains and plains: A Markov-switching approach to growth," Journal of Development Economics, Elsevier, vol. 81(2), pages 357-385, December.
    32. Di Vaio, Gianfranco & Enflo, Kerstin, 2011. "Did globalization drive convergence? Identifying cross-country growth regimes in the long run," European Economic Review, Elsevier, vol. 55(6), pages 832-844, August.
    33. Michele Battisti & Christopher F. Parmeter, 2010. "Convergence Tools and Mixture Analysis," Working Papers CELEG 1007, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    34. Ann L. Owen & Julio Videras, 2016. "Classifying Human Development with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 959-981, July.
    35. Deng, Wen-Shuenn & Lin, Yi-Chen & Gong, Jinguo, 2012. "A smooth coefficient quantile regression approach to the social capital–economic growth nexus," Economic Modelling, Elsevier, vol. 29(2), pages 185-197.
    36. Michelle Gilmartin & Dimitris Korobilis, 2012. "On Regional Unemployment: An Empirical Examination of the Determinants of Geographical Differentials in the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 59(2), pages 179-195, May.
    37. Zheng Ying & Chang-Rui Dong & Hsu-Ling Chang & Chi-Wei Su, 2014. "Are Real GDP Levels Stationary in African Countries?," South African Journal of Economics, Economic Society of South Africa, vol. 82(3), pages 392-401, September.
    38. Morier, Bruno & Teles, Vladimir Kühl, 2016. "A Time-Varying Markov-Switching Model For Economic Growth," Macroeconomic Dynamics, Cambridge University Press, vol. 20(6), pages 1550-1580, September.
    39. Marco Alfo & Giovanni Trovato & Robert J. Waldmann, 2008. "Testing for country heterogeneity in growth models using a finite mixture approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 487-514.
    40. Rosa Bernardini Papalia & Silvia Bertarelli, 2013. "Nonlinearities in economic growth and club convergence," Empirical Economics, Springer, vol. 44(3), pages 1171-1202, June.
    41. Guanchun Liu & Shichang Ma & Chien‐Chiang Lee & Ming Xu, 2020. "Growth decomposition bias when accounting for heterogeneous regimes: Evidence from China," Review of Development Economics, Wiley Blackwell, vol. 24(2), pages 691-711, May.
    42. Ntombiyesibini Matonana & Andrew Phiri, 2020. "Convergence Dynamics between South Africa and Her Main Trading Partners," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 18(1 (Spring), pages 25-44.
    43. Nalan Baştürk & Richard Paap & Dick van Dijk, 2012. "Structural differences in economic growth: an endogenous clustering approach," Applied Economics, Taylor & Francis Journals, vol. 44(1), pages 119-134, January.
    44. Guanchun Liu & Chien-Chiang Lee & Yuanyuan Liu, 2020. "Growth path heterogeneity across provincial economies in China: the role of geography versus institutions," Empirical Economics, Springer, vol. 59(2), pages 503-546, August.
    45. Arye Hillman, 2007. "Economic and security consequences of supreme values," Public Choice, Springer, vol. 131(3), pages 259-280, June.
    46. Richard Startz, 2020. "The next hundred years of growth and convergence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 99-113, January.
    47. Bos, J.W.B. & Economidou, C. & Koetter, M. & Kolari, J.W., 2010. "Do all countries grow alike?," Journal of Development Economics, Elsevier, vol. 91(1), pages 113-127, January.
    48. Ferreira Paulo & Dionísio Andreia, 2016. "GDP growth and convergence determinants in the European Union: a crisp-set analysis," Review of Economic Perspectives, Sciendo, vol. 16(4), pages 279-296, December.
    49. Laura Serrano & Antonio Sianes & Antonio Ariza-Montes, 2020. "Understanding the Implementation of Airbnb in Urban Contexts: Towards a Categorization of European Cities," Land, MDPI, vol. 9(12), pages 1-21, December.
    50. Kerekes, Monika, 2009. "Growth miracles and failures in a Markov switching classification model of growth," Discussion Papers 2009/11, Free University Berlin, School of Business & Economics.

  46. van der Hart, Jaap & de Zwart, Gerben & van Dijk, Dick, 2005. "The success of stock selection strategies in emerging markets: Is it risk or behavioral bias?," Emerging Markets Review, Elsevier, vol. 6(3), pages 238-262, September.
    See citations under working paper version above.
  47. Franses, Philip Hans & van Dijk, Dick, 2005. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," International Journal of Forecasting, Elsevier, vol. 21(1), pages 87-102.
    See citations under working paper version above.
  48. Fok, Dennis & van Dijk, Dick & Franses, Philip Hans, 2005. "Forecasting aggregates using panels of nonlinear time series," International Journal of Forecasting, Elsevier, vol. 21(4), pages 785-794.
    See citations under working paper version above.
  49. Philip Hans Franses & Herman K. van Dijk & Dick van Dijk, 2005. "On the dynamics of business cycle analysis: editors' introduction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 147-150.

    Cited by:

    1. Ooms, M., 2008. "Trends in Applied Econometrics Software Development 1985-2008, an analysis of Journal of Applied Econometrics research articles, software reviews, data and code," Serie Research Memoranda 0021, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. João Paulo Martin Faleiros & Denisard Cnéio de Oliveira Alves, 2006. "Não Linearidade Nos Ciclos De Negócios: Modelo Auto-Regressivo “Smooth Transition” Para O Índice Geral De Produção Industrial Brasileiro E Bens De Capital," Anais do XXXIV Encontro Nacional de Economia [Proceedings of the 34th Brazilian Economics Meeting] 10, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    3. Kevin Lee & Kalvinder Shields, 2010. "Decision-Making in Hard Times: What is a Recession, Why Do We Care and When Do We Know We Are in One?," Discussion Papers 10/08, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    4. Garratt, Anthony & Lee, Kevin & Shields, Kalvinder, 2016. "Forecasting global recessions in a GVAR model of actual and expected output," International Journal of Forecasting, Elsevier, vol. 32(2), pages 374-390.
    5. Lee, Kevin & Shields, Kalvinder K., 2011. "Decision-making in hard times: What is a recession, why do we care and how do we know when we are in one?," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 43-60, January.
    6. Anthony Garratt & Kevin Lee & Kalvinder Shields, 2014. "Forecasting Global Recessions in a GVAR Model of Actual and Expected Output in the G7," Discussion Papers 2014/06, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).

  50. Dick van Dijk & Dennis Fok & Philip Hans Franses, 2005. "A multi-level panel STAR model for US manufacturing sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 811-827.

    Cited by:

    1. Hermann Ndoya hegueu & Aristophane Djeufack dongmo, 2021. "Urbanization, Governance and Informal Economy: an African Tale," Economics Bulletin, AccessEcon, vol. 41(3), pages 1525-1540.
    2. Cristina Jude & Gregory Levieuge, 2017. "Growth Effect of Foreign Direct Investment in Developing Economies: The Role of Institutional Quality," The World Economy, Wiley Blackwell, vol. 40(4), pages 715-742, April.
    3. JUDE, Cristina & LEVIEUGE, Gregory, 2013. "Growth effect of FDI in developing economies: The role of institutional quality," MPRA Paper 49321, University Library of Munich, Germany.
    4. Davide Furceri & Mr. Prakash Loungani, 2015. "Capital Account Liberalization and Inequality," IMF Working Papers 2015/243, International Monetary Fund.
    5. Jude Eggoh, 2012. "Inflation Effects on Finance-Growth Link: A Panel Smooth Threshold Approach," International Economic Journal, Taylor & Francis Journals, vol. 26(4), pages 711-725, June.
    6. Fok, D. & van Dijk, D.J.C. & Franses, Ph.H.B.F., 2004. "Forecasting aggregates using panels of nonlinear time series," Econometric Institute Research Papers EI 2004-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Juan Carlos Cuestas & Estefania Mourelle & Paulo José Regis, 2019. "Real exchange rate misalignments in CEECs: have they hindered growth?," Bank of Estonia Working Papers wp2018-05, Bank of Estonia, revised 23 Jan 2019.
    8. Damette, Olivier & Seghir, Majda, 2018. "Natural resource curse in oil exporting countries: A nonlinear approach," International Economics, Elsevier, vol. 156(C), pages 231-246.
    9. Yongsung Chang & Sunoong Hwang, 2011. "Asymmetric Phase Shifts in the U.S. Industrial Production Cycles," RCER Working Papers 564, University of Rochester - Center for Economic Research (RCER).
    10. Shiao-Yen Liu & Po-Chin Wu & Tsai-Yuan Huang, 2018. "Nonlinear Causality between Education and Health: the Role of Human Development Index," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 13(3), pages 761-777, September.
    11. Olivier Damette & Mathilde Maurel & Michael A. Stemmer, 2016. "What does it take to grow out of recession? An error-correction approach towards growth convergence of European and transition countries," Post-Print halshs-01318131, HAL.
    12. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "International R&D spillovers, absorptive capacity and relative backwardness: a panel smooth transition regression model," Department of Economics Working Papers 1203, Department of Economics, University of Trento, Italia.
    13. Antonia López‐Villavicencio & José Ignacio Silva, 2011. "Employment Protection And The Non‐Linear Relationship Between The Wage‐Productivity Gap And Unemployment," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(2), pages 200-220, May.
    14. Jean‐Pierre Allegret & Audrey Allegret, 2019. "Did foreign exchange holding influence growth performance during the global financial crisis?," The World Economy, Wiley Blackwell, vol. 42(3), pages 680-710, March.
    15. Saia, Artjom, 2023. "Digitalization and CO2 emissions: Dynamics under R&D and technology innovation regimes," Technology in Society, Elsevier, vol. 74(C).
    16. Gabriella Deborah Legrenzi & Costas Milas, 2011. "Debt Sustainability and Financial Crises: Evidence from the GIIPS," CESifo Working Paper Series 3594, CESifo.
    17. Djedje Hermann YOHOU & Michaël GOUJON & Bertrand LAPORTE & Samuel GUERINEAU, 2016. "Is Aid Unfriendly to Tax? African Evidence of Heterogeneous Direct and Indirect Effects," Working Papers 201608, CERDI.
    18. Djedje Hermann Yohou & Michaël Goujon & Wautabouna Ouattara, 2015. "Heterogeneous Aid Effects on Tax Revenues: Accounting for Government Stability in WAEMU Countries," CERDI Working papers halshs-01138159, HAL.
    19. Béreau, Sophie & Villavicencio, Antonia López & Mignon, Valérie, 2010. "Nonlinear adjustment of the real exchange rate towards its equilibrium value: A panel smooth transition error correction modelling," Economic Modelling, Elsevier, vol. 27(1), pages 404-416, January.
    20. Magloire LANHA, 2022. "Les institutions influencent-elles la relation," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 55, pages 5-22.
    21. Gabriel Gomes & Emmanuel Hache & Valérie Mignon & Anthony Paris, 2017. "On the current account - biofuels link in emerging and developing countries: do oil price fluctuations matter?," EconomiX Working Papers 2017-30, University of Paris Nanterre, EconomiX.
    22. Ben Hmiden, Oussama & Ben Cheikh, Nidhaleddine, 2016. "Debt-threshold effect in sovereign credit ratings: New evidence from nonlinear panel smooth transition models," Finance Research Letters, Elsevier, vol. 19(C), pages 273-278.
    23. Tue Gørgens & Christopher L. Skeels & Allan H. Würtz, 2009. "Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models," CREATES Research Papers 2009-51, Department of Economics and Business Economics, Aarhus University.
    24. Majid Moayyed & Mehdi Shiva, 2023. "The impact of oil price changes on industrial production: a panel smooth-transition approach on G7 countries," International Economics and Economic Policy, Springer, vol. 20(4), pages 595-612, October.
    25. Wafa Ghardallou, 2022. "Financial System Development and Democracy: a Panel Smooth Transition Regression Approach for Developing Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(2), pages 1714-1735, June.
    26. Mahmut Zortuk & Sinan Çeken, 2016. "Testing Environmental Kuznets Curve in the Selected Transition Economies with Panel Smooth Transition Regression Analysis," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 18(43), pages 537-537, August.
    27. Dennis Fok & Philip Hans Franses, 2013. "Testing earnings management," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 281-292, August.
    28. António Afonso & João Jalles, 2017. "Do Fiscal Rules Lower Government Financing Costs?," Working Papers REM 2017/15, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    29. Zhang, Wenwen & Chiu, Yi-Bin, 2020. "Do country risks influence carbon dioxide emissions? A non-linear perspective," Energy, Elsevier, vol. 206(C).
    30. Muhammad Khan, 2016. "Evidence on the functional form of inflation and output growth variability relationship in European economies," International Economics, CEPII research center, issue 146, pages 1-11.
    31. Seydou Coulibaly, 2019. "Impact of natural resource wealth on non-resource tax revenue mobilization in Africa: Do institutions and economic diversification matter?," CERDI Working papers halshs-02108128, HAL.
    32. Gabriella Legrenzi & Costas Milas, 2012. "Long-Run Debt Sustainability and Threshold Adjustments: Non-Linear Empirical Evidence from the GIIPS," Economics Bulletin, AccessEcon, vol. 32(3), pages 2586-2593.
    33. Lena Dräger & Jan-Oliver Menz & Ulrich Fritsche, 2011. "Perceived Inflation under Loss Aversion," Macroeconomics and Finance Series 201105, University of Hamburg, Department of Socioeconomics.
    34. Giulio Cainelli & Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "Spatial agglomeration and productivity in Italy: a panel smooth transition regression approach," Openloc Working Papers 1204, Public policies and local development.
    35. Mustapha JOBARTEH & Huseyin KAYA, 2019. "Non-linear finance-growth nexus for African countries: A panel smooth transition regression approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(620), A), pages 205-222, Autumn.
    36. Peltonen, Tuomas A. & Sager, Michael & Popescu, Adina, 2009. "Can non-linear real shocks explain the persistence of PPP exchange rate disequilibria?," Working Paper Series 1073, European Central Bank.
    37. Hideaki Matsuoka, 2020. "Debt intolerance: Threshold level and composition," Working Papers e147, Tokyo Center for Economic Research.
    38. Yi-Chi Chen & Chang-Ching Lin, 2010. "Threshold Effects in Cigarette Addiction: An Application of the Threshold Model in Dynamic Panels," Economics Bulletin, AccessEcon, vol. 30(4), pages 3128-3142.
    39. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
    40. Duygu Yolcu Karadam & Jülide Yildirim & Nadir Öcal, 2017. "Military expenditure and economic growth in Middle Eastern countries and Turkey: a non-linear panel data approach," Defence and Peace Economics, Taylor & Francis Journals, vol. 28(6), pages 719-730, November.
    41. Jude Eggoh & Chrysost Bangake & Gervasio Semedo, 2018. "Do remittances spur economic growth? Evidence from developing countries," Post-Print hal-02107277, HAL.
    42. Chi-Hui Wang & Prasad Padmanabhan & Chia-Hsing Huang, 2021. "The Impact of Renewable Energy, Urbanization, and Environmental Sustainability Ratings on the Environmental Kuznets Curve and the Pollution Haven Hypothesis," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    43. Henry Aray & Luis Pedagua, 2021. "Decentralization and economic growth: Evidence across states of some relevant macroeconomic variables," ThE Papers 21/08, Department of Economic Theory and Economic History of the University of Granada..
    44. Myung Hwan Seo & Yongcheol Shin, 2014. "Dynamic Panels with Threshold Effect and Endogeneity," STICERD - Econometrics Paper Series 577, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    45. Jude C. Eggoh & Patrick Villieu, 2013. "Un réexamen de la non-linéarité entre le développement financier et la croissance économique," Revue d'économie politique, Dalloz, vol. 123(2), pages 211-236.
    46. António Afonso & João Tovar Jalles, 2019. "Fiscal Rules and Government Financing Costs," Fiscal Studies, John Wiley & Sons, vol. 40(1), pages 71-90, March.
    47. Julien Fouquau & Philippe K. Spieser, 2015. "Statistical evidence about LIBOR manipulation: A "Sherlock Holmes" investigation," Post-Print hal-01160060, HAL.
    48. Seydou Coulibaly, 2019. "Impact of natural resource wealth on non-resource tax revenue mobilization in Africa: Do institutions and economic diversification matter?," Working Papers halshs-02108128, HAL.
    49. Wafa Ghardallou & Abdelkader Boudriga, 2014. "Financial Development and Democracy: is the Relationship Non-Linear?," Working Papers 886, Economic Research Forum, revised Dec 2014.
    50. Wafa Ghardallou & Noha Alessa, 2022. "Corporate Social Responsibility and Firm Performance in GCC Countries: A Panel Smooth Transition Regression Model," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    51. Eggoh, Jude C. & Khan, Muhammad, 2014. "On the nonlinear relationship between inflation and economic growth," Research in Economics, Elsevier, vol. 68(2), pages 133-143.
    52. Li, Wenying & Dorfman, Jeffrey H., 2019. "The implications of heterogeneous habit in consumer beverage purchases on soda and sin taxes," Food Policy, Elsevier, vol. 84(C), pages 111-120.
    53. Xiaoqing Ye & Xiangjun Wu, 2017. "Estimating three-dimensional nonlinear panel data models with interactive effects," Applied Economics Letters, Taylor & Francis Journals, vol. 24(10), pages 708-712, June.
    54. Ketenci, Natalya, 2010. "The Feldstein –Horioka Puzzle and structural breaks: evidence from EU members," MPRA Paper 26010, University Library of Munich, Germany.
    55. Derek Stimel, 2010. "Choice of Aggregate Demand Proxy and its Affect on Phillips Curve Nonlinearity: U.S. Evidence," Economics Bulletin, AccessEcon, vol. 30(1), pages 543-557.
    56. Furceri, Davide & Loungani, Prakash, 2018. "The distributional effects of capital account liberalization," Journal of Development Economics, Elsevier, vol. 130(C), pages 127-144.
    57. Hermann D. Yohou, 2023. "Corruption, tax reform and fiscal space in emerging and developing economies," The World Economy, Wiley Blackwell, vol. 46(4), pages 1082-1118, April.
    58. Xiaosheng Li & Xia Yan & Qingxian An & Ke Chen & Zhen Shen, 2016. "The coordination between China’s economic growth and environmental emission from the Environmental Kuznets Curve viewpoint," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 233-252, August.
    59. Chakroun, Mohamed, 2009. "Health care expenditure and GDP: An international panel smooth transition approach," MPRA Paper 14322, University Library of Munich, Germany.
    60. Maha Kalai & Nahed Zghidi, 2019. "Foreign Direct Investment, Trade, and Economic Growth in MENA Countries: Empirical Analysis Using ARDL Bounds Testing Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 397-421, March.
    61. Artur Tarassow, 2015. "Financial Investment Constraints. A Panel Threshold Application to German Firm Level Data," Macroeconomics and Finance Series 201405, University of Hamburg, Department of Socioeconomics.
    62. Nicholas Apergis, 2015. "Economic freedom and income inequality: further evidence from 58 countries in the long-run," Financial Theory and Practice, Institute of Public Finance, vol. 39(4), pages 349-370.
    63. Nicholas Apergis & Arusha Cooray, 2017. "Economic Freedom and Income Inequality: Evidence from a Panel of Global Economies— A Linear and a Non-Linear Long-Run Analysis," Manchester School, University of Manchester, vol. 85(1), pages 88-105, January.
    64. López-Villavicencio, Antonia & Mignon, Valérie, 2011. "On the impact of inflation on output growth: Does the level of inflation matter?," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 455-464, September.
    65. Silvia Haan-Rietdijk & John Gottman & Cindy Bergeman & Ellen Hamaker, 2016. "Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 217-241, March.
    66. Tzu-Yi Yang & Chieh Liu & Yu-Tai Yang & Ssu-Han Chen, 2023. "The dynamic effect of trading between China and Taiwan under exchange rate fluctuations," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.

  51. van Dijk, Dick & Osborn, Denise R. & Sensier, Marianne, 2005. "Testing for causality in variance in the presence of breaks," Economics Letters, Elsevier, vol. 89(2), pages 193-199, November.
    See citations under working paper version above.
  52. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    See citations under working paper version above.
  53. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
    See citations under working paper version above.
  54. van der Hart, Jaap & Slagter, Erica & van Dijk, Dick, 2003. "Stock selection strategies in emerging markets," Journal of Empirical Finance, Elsevier, vol. 10(1-2), pages 105-132, February.
    See citations under working paper version above.
  55. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.

    Cited by:

    1. GIOT, Pierre & PETITJEAN, Mikael, 2005. "Dynamic asset allocation between stocks and bonds using the Bond-Equity Yield Ratio," LIDAM Discussion Papers CORE 2005010, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Paper series 42_09, Rimini Centre for Economic Analysis.
    3. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    4. Frédérique Bec & Annabelle de Gaye, 2019. "Le modèle autorégressif autorégressif à seuil avec effet rebond : Une application aux rendements boursiers français et américains ," Working Papers hal-02014663, HAL.
    5. Asmara Jamaleh, 2002. "Explaining and forecasting the euro/dollar exchange rate through a non-linear threshold model," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 422-448.
    6. Baik, Hyeoncheol & Han, Sumin & Joo, Sunghoon & Lee, Kangbok, 2022. "A bank's optimal capital ratio: A time-varying parameter model to the partial adjustment framework," Journal of Banking & Finance, Elsevier, vol. 142(C).
    7. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    8. Ramazan Gencay & Ege Yazgan, 2017. "When Are Wavelets Useful Forecasters?," Working Papers 1704, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    9. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    10. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an Optimal Forecast Combination? A Stochastic Dominance Approach to Forecast Combination Puzzle," Working Paper series 17_12, Rimini Centre for Economic Analysis.
    11. GIOT, Pierre & PETITJEAN, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," LIDAM Reprints CORE 1982, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. Bermejo Mancera, Miguel Ángel & Peña, Daniel & Sánchez, Ismael, 2009. "Graphical identification of TAR models," DES - Working Papers. Statistics and Econometrics. WS ws097723, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2013. "The European Way out of Recession," Post-Print hal-02980626, HAL.
    15. de Morais, Igor Alexandre C. & Portugal, Marcelo Savino, 2005. "A Markov Switching Model for the Brazilian Demand for Imports: Analyzing the Import Substitution Process in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(2), November.
    16. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    17. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    18. Bec, Frédérique & Bouabdallah, Othman & Ferrara, Laurent, 2014. "The way out of recessions: A forecasting analysis for some Euro area countries," International Journal of Forecasting, Elsevier, vol. 30(3), pages 539-549.
    19. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    20. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    21. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.
    22. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    23. Hui Feng & Jia Liu, 2002. "A SETAR Model for Canadian GDP: Non-Linearities and Forecast Comparisons," Econometrics Working Papers 0206, Department of Economics, University of Victoria.
    24. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    25. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    26. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    27. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    28. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    29. Rodriguez, Nestor & Eales, James S., 2015. "Structural Change via Threshold Effects: Estimating U.S. Meat Demand Using Smooth Transition Functions and the Effects of More Women in the Labor Force," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206522, Agricultural and Applied Economics Association.
    30. Pedro M.D.C.B. Gouveia & Denise R. Osborn & Paulo M.M. Rodrigues, 2008. "Comparing Seasonal Forecasts of Industrial Production," Centre for Growth and Business Cycle Research Discussion Paper Series 102, Economics, The University of Manchester.
    31. Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.

  56. Dick van Dijk 1 & Birgit Strikholm & Timo Teräsvirta, 2003. "The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 79-98, June.
    See citations under working paper version above.
  57. Lundbergh, Stefan & Terasvirta, Timo & van Dijk, Dick, 2003. "Time-Varying Smooth Transition Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 104-121, January.
    See citations under working paper version above.
  58. Dick van Dijk & Philip Hans Franses, 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 727-744, December.
    See citations under working paper version above.
  59. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    See citations under working paper version above.
  60. A. M. Robert Taylor & Dick van Dijk, 2002. "Can Tests for Stochastic Unit Roots Provide Useful Portmanteau Tests for Persistence?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 381-397, September.

    Cited by:

    1. Gawon Yoon, 2010. "Nonlinear mean-reversion to purchasing power parity: exponential smooth transition autoregressive models and stochastic unit root processes," Applied Economics, Taylor & Francis Journals, vol. 42(4), pages 489-496.
    2. Gawon Yoon, 2010. "Nonlinear mean reversion in real exchange rates: threshold autoregressive models and stochastic unit root processes," Applied Economics Letters, Taylor & Francis Journals, vol. 17(8), pages 797-804.
    3. Hyginus Leon & Serineh Najarian, 2005. "Asymmetric adjustment and nonlinear dynamics in real exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 15-39.
    4. Yoon, Gawon, 2005. "Has the U.S. economy really become less correlated with that of the rest of the world?," Economic Modelling, Elsevier, vol. 22(1), pages 147-158, January.
    5. Gawon Yoon, 2005. "Stochastic Unit Roots in the Capital Asset Pricing Model?," Bulletin of Economic Research, Wiley Blackwell, vol. 57(4), pages 369-389, October.
    6. Yoon, Gawon, 2016. "Stochastic unit root processes: Maximum likelihood estimation, and new Lagrange multiplier and likelihood ratio tests," Economic Modelling, Elsevier, vol. 52(PB), pages 725-732.

  61. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.

    Cited by:

    1. Laurent Ferrara & Dominique Guégan, 2006. "Detection of the Industrial Business Cycle using SETAR Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 353-371.
    2. David Ubilava & Matt Holt, 2013. "El Niño southern oscillation and its effects on world vegetable oil prices: assessing asymmetries using smooth transition models," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(2), pages 273-297, April.
    3. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    4. Gilles Dufrenot & Dominique Guegan & Anne Peguin-Feissolle, 2008. "Changing-regime volatility: A fractionally integrated SETAR model," Post-Print halshs-00185369, HAL.
    5. Imene Mootamri & Mohamed Boutahar & Anne Peguin-Feissolle, 2008. "A fractionally integrated exponential STAR model applied to the US real effective exchange rate," Post-Print halshs-00390134, HAL.
    6. Gilles Dufrenot & Elisabeth Grimaud & Eugénie Latil & Valerie Mignon, 2008. "Modelling The Slow Mean‐Reversion Of The Central And Eastern European Countries' Real Exchange Rates," Manchester School, University of Manchester, vol. 76(1), pages 21-43, January.
    7. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    8. Thomaidis, Nikolaos S. & Biskas, Pandelis N., 2021. "Fundamental pricing laws and long memory effects in the day-ahead power market," Energy Economics, Elsevier, vol. 100(C).
    9. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    10. James D. Hamilton, 2005. "What's real about the business cycle?," Review, Federal Reserve Bank of St. Louis, vol. 87(Jul), pages 435-452.
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    2. Ubilava, David, 2014. "On the Relationship between Financial Instability and Economic Performance: Stressing the Business of Nonlinear Modelling," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170222, Agricultural and Applied Economics Association.
    3. David Ubilava & Matt Holt, 2013. "El Niño southern oscillation and its effects on world vegetable oil prices: assessing asymmetries using smooth transition models," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(2), pages 273-297, April.
    4. Bazán, Walter, 2011. "No-linealidades y asimetrías en el crédito peruano," Working Papers 2011-015, Banco Central de Reserva del Perú.
    5. Wang, Xia & Zheng, Tingguo & Zhu, Yanli, 2014. "Money–output Granger causal dynamics in China," Economic Modelling, Elsevier, vol. 43(C), pages 192-200.
    6. Fredj Jawadi & Catherine Bruneau & Nadia Sghaier, 2009. "Nonlinear Cointegration Relationships Between Non‐Life Insurance Premiums and Financial Markets," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 753-783, September.
    7. Maki, Daiki, 2015. "Wild bootstrap testing for cointegration in an ESTAR error correction model," Economic Modelling, Elsevier, vol. 47(C), pages 292-298.
    8. Goodwin, Barry K. & Holt, Matthew T. & Prestemon, Jeffrey P., 2019. "Nonlinear exchange rate pass-through in timber products: The case of oriented strand board in Canada and the United States," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    9. Dimitris K. Christopoulos & Miguel A. León-Ledesma, 2008. "Testing for Granger (non-)causality in a time-varying coefficient VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 293-303.
    10. Michael J. Dueker & Laura E. Jackson & Michael T. Owyang & Martin Sola, 2010. "A Time-Varying Threshold STAR Model with Applications," Working Papers 2010-029, Federal Reserve Bank of St. Louis, revised 10 Aug 2022.
    11. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    12. Chollete, Loran & Ning, Cathy, 2009. "The Dependence Structure of Macroeconomic Variables in the US," UiS Working Papers in Economics and Finance 2009/31, University of Stavanger.
    13. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2011. "Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes," Working Papers 1103, University of Nevada, Las Vegas , Department of Economics.
    14. Cerqueira, Vinícius Dos Santos & Ribeiro, Márcio Bruno & Martinez, Thiago Sevilhano, 2014. "Propagação Assimétrica de Choques Monetários na Economia Brasileira: Evidências com base em um modelo vetorial não-linear de transição suave," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(1), April.
    15. Roula Inglesi-Lotz & Mehmet Balcilar & Rangan Gupta, 2013. "Time-Varying Causality between Research Output and Economic Growth in the US," Working Papers 201350, University of Pretoria, Department of Economics.
    16. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    17. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben, 2023. "Investigating the dynamics of crude oil and clean energy markets in times of geopolitical tensions," Energy Economics, Elsevier, vol. 124(C).
    18. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    19. João Paulo Martin Faleiros & Denisard Cnéio de Oliveira Alves, 2008. "Modelo de Crescimento Baseado nas Exportações: Evidências empíricas para Chile, Brasil e México, em uma perspectiva Não Linear," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807170923500, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    20. Jonathan B. Hill, 2004. "Causation Delays and Causal Neutralization for General Horizons: The Money-Output Relationship Revisited," Econometrics 0402002, University Library of Munich, Germany, revised 23 Mar 2005.
    21. Ubilava, David, 2014. "The ENSO Effect on World Wheat Market Dynamics: Smooth Transitions in Asymmetric Price Transmission," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170223, Agricultural and Applied Economics Association.
    22. D R Osborn & P J Perez & M Sensier, 2005. "Business Cycle Linkages for the G7 Countries:Does the US Lead the World?," Centre for Growth and Business Cycle Research Discussion Paper Series 50, Economics, The University of Manchester.
    23. Hasanov, Mübariz & Araç, Aysen & Telatar, Funda, 2010. "Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey," Economic Modelling, Elsevier, vol. 27(5), pages 1103-1115, September.
    24. Araç, Ayşen & Hasanov, Mübariz, 2014. "Asymmetries in the dynamic interrelationship between energy consumption and economic growth: Evidence from Turkey," Energy Economics, Elsevier, vol. 44(C), pages 259-269.
    25. Deborah Gefang, 2012. "Money‐output Causality Revisited – A Bayesian Logistic Smooth Transition VECM Perspective," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(1), pages 131-151, February.

  63. Van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for Smooth Transition Nonlinearity in the Presence of Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 217-235, April.
    See citations under working paper version above.
  64. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.
    See citations under working paper version above.

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