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Jan G. De Gooijer

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.

Working papers

  1. Jan G. de Gooijer & Ao Yuan, 2011. "Kernel-Smoothed Conditional Quantiles of Correlated Bivariate Discrete Data," Tinbergen Institute Discussion Papers 11-011/4, Tinbergen Institute.

    Cited by:

    1. Luke B. Smith & Brian J. Reich & Amy H. Herring & Peter H. Langlois & Montserrat Fuentes, 2015. "Multilevel quantile function modeling with application to birth outcomes," Biometrics, The International Biometric Society, vol. 71(2), pages 508-519, June.

  2. Jan G. de Gooijer & Ao Yuan, 2010. "Some Exact Tests for Manifest Properties of Latent Trait Models," Tinbergen Institute Discussion Papers 10-044/4, Tinbergen Institute.

    Cited by:

    1. Jules Ellis, 2014. "An Inequality for Correlations in Unidimensional Monotone Latent Variable Models for Binary Variables," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 303-316, April.
    2. Rudy Ligtvoet, 2022. "Incomplete Tests of Conditional Association for the Assessment of Model Assumptions," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1214-1237, December.
    3. Jules L. Ellis & Klaas Sijtsma, 2023. "A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 387-412, June.

  3. De Gooijer, J. & Diks, C.G.H. & Gatarek, L., 2009. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," CeNDEF Working Papers 09-13, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.

    Cited by:

    1. Katja Ahoniemi & Ana-Maria Fuertes & Jose Olmo, 2016. "Overnight News and Daily Equity Trading Risk Limits," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 525-551.
    2. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    3. Insana, Alessandra, 2022. "Does systematic risk change when markets close? An analysis using stocks’ beta," Economic Modelling, Elsevier, vol. 109(C).
    4. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "US stock market efficiency over weekly, monthly, quarterly and yearly time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 554-564.
    5. Christian L Dunis & Jason Laws & Jozef Rudy, 2011. "Profitable mean reversion after large price drops: A story of day and night in the S&P 500, 400 MidCap and 600 SmallCap Indices," Journal of Asset Management, Palgrave Macmillan, vol. 12(3), pages 185-202, August.
    6. 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.

  4. Cheng, Yebin & De Gooijer, Jan & Zerom, Dawit, 2009. "Efficient Estimation of an Additive Quantile Regression Model," MPRA Paper 14388, University Library of Munich, Germany.

    Cited by:

    1. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    2. Noh, Hohsuk & Lee, Eun, 2012. "Component Selection in Additive Quantile Regression Models," LIDAM Discussion Papers ISBA 2012021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  5. Brännäs, Kurt & G De Gooijer, Jan & Lönnbark, Carl & Soultanaeva, Albina, 2007. "Simultaneity and Asymmetry of Returns and Volatilities in the Emerging Baltic State Stock Exchanges," Umeå Economic Studies 725, Umeå University, Department of Economics.

    Cited by:

    1. Hellström, Jörgen & Soultanaeva, Albina, 2010. "The Impact of Stock Market Jumps on Time-Varying Return Correlations: Empirical Evidence from the Baltic Countries," Umeå Economic Studies 816, Umeå University, Department of Economics.
    2. Luis A. Gil-Alana & Rangan Gupta & Olanrewaju I. Shittu & OlaOluwa S. Yaya, 2016. "Market Efficiency of Baltic Stock Markets: A Fractional Integration Approach," Working Papers 201617, University of Pretoria, Department of Economics.
    3. Humavindu, Michael N, 2008. "Essays on the Namibian Economy," Umeå Economic Studies 745, Umeå University, Department of Economics.
    4. Soultanaeva, Albina, 2008. "Impact of Political News on the Baltic State Stock Markets," Umeå Economic Studies 735, Umeå University, Department of Economics.

  6. Ao Yuan & Jan G. De Gooijer, 2006. "Semiparametric Regression with Kernel Error Model," Tinbergen Institute Discussion Papers 06-058/4, Tinbergen Institute.

    Cited by:

    1. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
    2. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.
    3. Guohua Feng & Chuan Wang & Xibin Zhang, 2019. "Estimation of inefficiency in stochastic frontier models: a Bayesian kernel approach," Journal of Productivity Analysis, Springer, vol. 51(1), pages 1-19, February.
    4. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2013. "A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 20/13, Monash University, Department of Econometrics and Business Statistics.
    5. Jan G. De Gooijer & Ao Yuan, 2008. "MDL Mean Function Selection in Semiparametric Kernel Regression Models," Tinbergen Institute Discussion Papers 08-046/4, Tinbergen Institute.
    6. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2013. "Bayesian bandwidth selection for a nonparametric regession model with mixed types of regressors," Monash Econometrics and Business Statistics Working Papers 13/13, Monash University, Department of Econometrics and Business Statistics.
    7. Wang, Qin & Yao, Weixin, 2012. "An adaptive estimation of MAVE," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 88-100, February.
    8. Yao, Weixin, 2013. "A note on EM algorithm for mixture models," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 519-526.
    9. McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    10. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2011. "Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 10/11, Monash University, Department of Econometrics and Business Statistics.
    11. Chen, Yixin & Wang, Qin & Yao, Weixin, 2015. "Adaptive estimation for varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 17-31.
    12. Zhang, Jun & Lin, Bingqing & Zhou, Yan, 2021. "Kernel density estimation for partial linear multivariate responses models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    13. Chee, Chew-Seng & Seo, Byungtae, 2020. "Semiparametric estimation for linear regression with symmetric errors," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    14. De Gooijer, Jan G. & Reichardt, Hugo, 2021. "A multi-step kernel–based regression estimator that adapts to error distributions of unknown form," LSE Research Online Documents on Economics 115083, London School of Economics and Political Science, LSE Library.

  7. 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.

    Cited by:

    1. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB-Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Filelis - Papadopoulos, Christos K. & Kyziropoulos, Panagiotis E. & Morrison, John P. & O‘Reilly, Philip, 2022. "Modelling and forecasting based on recursive incomplete pseudoinverse matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 358-376.
    3. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
    4. Prestwich, S.D. & Tarim, S.A. & Rossi, R. & Hnich, B., 2014. "Forecasting intermittent demand by hyperbolic-exponential smoothing," International Journal of Forecasting, Elsevier, vol. 30(4), pages 928-933.
    5. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    6. Ahmad Alsharef & Sonia & Karan Kumar & Celestine Iwendi, 2022. "Time Series Data Modeling Using Advanced Machine Learning and AutoML," Sustainability, MDPI, vol. 14(22), pages 1-19, November.

  8. Yebin Cheng & Jan G. de Gooijer, 2004. "On the u-th Geometric Conditional Quantile," Tinbergen Institute Discussion Papers 04-072/4, Tinbergen Institute.

    Cited by:

    1. Matthieu Garcin & Dominique Guegan & Bertrand Hassani, 2018. "A novel multivariate risk measure: the Kendall VaR," Post-Print halshs-01467857, HAL.
    2. Matthieu Garcin & Dominique Guegan & Bertrand Hassani, 2017. "A novel multivariate risk measure: the Kendall VaR," Documents de travail du Centre d'Economie de la Sorbonne 17008, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    3. Matthieu Garcin & Dominique Guegan & Bertrand Hassani, 2017. "A novel multivariate risk measure: the Kendall VaR," Documents de travail du Centre d'Economie de la Sorbonne 17008r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Apr 2018.
    4. Matthieu Garcin & Dominique Guegan & Bertrand Hassani, 2018. "A novel multivariate risk measure: the Kendall VaR," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01467857, HAL.

  9. Jan G. de Gooijer & Dawit Zerom, 2002. "On Conditional Density Estimation," Tinbergen Institute Discussion Papers 02-032/4, Tinbergen Institute.

    Cited by:

    1. Jonas Rothfuss & Fabio Ferreira & Simon Walther & Maxim Ulrich, 2019. "Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks," Papers 1903.00954, arXiv.org, revised Apr 2019.
    2. Ann-Kathrin Bott & Michael Kohler, 2016. "Adaptive Estimation of a Conditional Density," International Statistical Review, International Statistical Institute, vol. 84(2), pages 291-316, August.
    3. A. Delaigle & P. Hall, 2016. "Approximating fragmented functional data by segments of Markov chains," Biometrika, Biometrika Trust, vol. 103(4), pages 779-799.
    4. Wen, Kuangyu & Wu, Ximing, 2017. "Smoothed kernel conditional density estimation," Economics Letters, Elsevier, vol. 152(C), pages 112-116.
    5. Liang, Han-Ying & Liu, Ai-Ai, 2013. "Kernel estimation of conditional density with truncated, censored and dependent data," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 40-58.
    6. Xiong, Xianzhu & Ou, Meijuan & Chen, Ailian, 2021. "Reweighted Nadaraya–Watson estimation of conditional density function in the right-censored model," Statistics & Probability Letters, Elsevier, vol. 168(C).
    7. Wang, Xiao-Feng & Ye, Deping, 2015. "Conditional density estimation in measurement error problems," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 38-50.
    8. Michael Kohler & Adam Krzyżak, 2020. "Estimating quantiles in imperfect simulation models using conditional density estimation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 123-155, February.
    9. Kim Huynh & David Jacho-Chavez, 2007. "Conditional density estimation: an application to the Ecuadorian manufacturing sector," Economics Bulletin, AccessEcon, vol. 3(62), pages 1-6.
    10. Han-Ying Liang & Jong-Il Baek, 2016. "Asymptotic normality of conditional density estimation with left-truncated and dependent data," Statistical Papers, Springer, vol. 57(1), pages 1-20, March.
    11. Ann-Kathrin Bott & Michael Kohler, 2017. "Nonparametric estimation of a conditional density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 189-214, February.
    12. Akkal Fatima & Kadiri Nadia & Rabhi Abbes, 2021. "Asymptotic Normality of Conditional Density and Conditional Mode in the Functional Single Index Model," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 25(1), pages 1-24, March.
    13. Holmes, Michael P. & Gray, Alexander G. & Isbell Jr., Charles Lee, 2010. "Fast kernel conditional density estimation: A dual-tree Monte Carlo approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1707-1718, July.
    14. Kateřina Konečná & Ivanka Horová, 2019. "Maximum likelihood method for bandwidth selection in kernel conditional density estimate," Computational Statistics, Springer, vol. 34(4), pages 1871-1887, December.
    15. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    16. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    17. De Gooijer, Jan G. & Henter, Gustav Eje & Yuan, Ao, 2022. "Kernel-based hidden Markov conditional densities," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    18. Gonçalves Mazzeu, Joao Henrique & Ruiz Ortega, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.

  10. Brännäs, Kurt & de Gooijer, Jan G., 2000. "ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH," Umeå Economic Studies 535, Umeå University, Department of Economics.

    Cited by:

    1. Taştan, Hüseyin, 2011. "Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry," MPRA Paper 34302, University Library of Munich, Germany.
    2. Kurt Brannas & Albina Soultanaeva, 2011. "Influence of news from Moscow and New York on returns and risks of Baltic States’ stock markets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 11(1), pages 109-124, July.
    3. Kurt Brännäs & Niklas Nordman, 2001. "An Alternative Conditional Asymmetry Specification for Stock Returns," CESifo Working Paper Series 448, CESifo.
    4. Brännäs, Kurt & Nordman, Niklas, 2001. "Conditional Skewness Modelling for Stock Returns," Umeå Economic Studies 562, Umeå University, Department of Economics.
    5. Brännäs, Kurt, 2003. "Temporal Aggregation of the Returns of a Stock Index Series," Umeå Economic Studies 614, Umeå University, Department of Economics.
    6. Hua, Zhongsheng & Zhang, Bin, 2008. "Improving density forecast by modeling asymmetric features: An application to S&P500 returns," European Journal of Operational Research, Elsevier, vol. 185(2), pages 716-725, March.
    7. Brännäs Kurt & De Gooijer Jan G. & Lönnbark Carl & Soultanaeva Albina, 2012. "Simultaneity and Asymmetry of Returns and Volatilities: The Emerging Baltic States' Stock Exchanges," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-24, January.
    8. Brännäs, Kurt & Soultanaeva, Albina, 2006. "Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices," Umeå Economic Studies 696, Umeå University, Department of Economics.
    9. María José Rodríguez & Esther Ruiz, 2012. "Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 637-668, September.
    10. Rodríguez, Mª José & Ruiz Ortega, Esther, 2009. "GARCH models with leverage effect : differences and similarities," DES - Working Papers. Statistics and Econometrics. WS ws090302, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Srikanta Kundu & Nityananda Sarkar, 2016. "Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study," International Econometric Review (IER), Econometric Research Association, vol. 8(2), pages 53-71, September.
    12. Jan G. Gooijer, 2021. "Asymmetric vector moving average models: estimation and testing," Computational Statistics, Springer, vol. 36(2), pages 1437-1460, June.
    13. Kulp-Tåg, Sofie, 2007. "Short-Horizon Asymmetric Mean-Reversion and Overreactions: Evidence from the Nordic Stock Markets," Working Papers 524, Hanken School of Economics.
    14. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
    15. Giuseppe Storti & Alessandra Amendola, 2000. "A Non Linear Time Series Approach To Modelling Asymmetry In Stock Market Indexes," Computing in Economics and Finance 2000 97, Society for Computational Economics.

Articles

  1. 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.

    Cited by:

    1. Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.

  2. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.

    Cited by:

    1. Roger M. Cooke, 2023. "Averaging quantiles, variance shrinkage, and overconfidence," Futures & Foresight Science, John Wiley & Sons, vol. 5(1), March.
    2. Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.
    3. 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.

  3. Jan G. De Gooijer & Ao Yuan, 2016. "Non parametric portmanteau tests for detecting non linearities in high dimensions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(2), pages 385-399, January.

    Cited by:

    1. De Gooijer, Jan G., 2023. "On portmanteau-type tests for nonlinear multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 195(C).

  4. Brännäs Kurt & De Gooijer Jan G. & Lönnbark Carl & Soultanaeva Albina, 2012. "Simultaneity and Asymmetry of Returns and Volatilities: The Emerging Baltic States' Stock Exchanges," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-24, January.

    Cited by:

    1. Luis A. Gil-Alana & Rangan Gupta & Olanrewaju I. Shittu & OlaOluwa S. Yaya, 2016. "Market Efficiency of Baltic Stock Markets: A Fractional Integration Approach," Working Papers 201617, University of Pretoria, Department of Economics.
    2. Nikola Radivojević & Nikola V. Ćurčić & Djurdjica Dj. Vukajlović, 2017. "Hull-White’s value at risk model: case study of Baltic equities market," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(5), pages 1023-1041, September.

  5. Jan G. De Gooijer & Cees G. H. Diks & Łukasz T. Gątarek, 2012. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 23-44, March.
    See citations under working paper version above.
  6. De Gooijer, Jan G. & Yuan, Ao, 2011. "Some exact tests for manifest properties of latent trait models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 34-44, January.
    See citations under working paper version above.
  7. Yebin Cheng & Jan G. De Gooijer & Dawit Zerom, 2011. "Efficient Estimation of an Additive Quantile Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 46-62, March.
    See citations under working paper version above.
  8. De Gooijer, Jan G. & Sivarajasingham, Selliah, 2008. "Parametric and nonparametric Granger causality testing: Linkages between international stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2547-2560.

    Cited by:

    1. Abu Bakar, Norhidayah & Masih, Abul Mansur M., 2014. "The Dynamic Linkages between Islamic Index and the Major Stock Markets: New Evidence from Wavelet time-scale decomposition Analysis," MPRA Paper 56977, University Library of Munich, Germany.
    2. 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.
    3. Charakopoulos, A.K. & Katsouli, G.A. & Karakasidis, T.E., 2018. "Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 436-453.
    4. Jan G. De Gooijer & Cees G. H. Diks & Łukasz T. Gątarek, 2012. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 23-44, March.
    5. Ozdemir, Zeynel Abidin & Olgun, Hasan & Saracoglu, Bedriye, 2009. "Dynamic linkages between the center and periphery in international stock markets," Research in International Business and Finance, Elsevier, vol. 23(1), pages 46-53, January.
    6. Ozdemir, Zeynel Abidin, 2009. "Linkages between international stock markets: A multivariate long-memory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2461-2468.
    7. Seema Narayan, 2019. "The Influence of Domestic and Foreign Shocks on Portfolio Diversification Gains and the Associated Risks," JRFM, MDPI, vol. 12(4), pages 1-26, October.
    8. Fowowe, Babajide, 2016. "Do oil prices drive agricultural commodity prices? Evidence from South Africa," Energy, Elsevier, vol. 104(C), pages 149-157.
    9. Anil Sharma & Neha Seth, 2012. "Literature review of stock market integration: a global perspective," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 4(1), pages 84-122, April.
    10. Lahmiri, Salim, 2017. "Cointegration and causal linkages in fertilizer markets across different regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 181-189.

  9. Jan G. De Gooijer, 2007. "Power of the Neyman Smooth Test for Evaluating Multivariate Forecast Densities," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(4), pages 371-381.

    Cited by:

    1. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," VfS Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    2. Jonas Dovern & Hans Manner, 2018. "Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts," CESifo Working Paper Series 7023, CESifo.
    3. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.

  10. Ao Yuan & Jan G. De Gooijer, 2007. "Semiparametric Regression with Kernel Error Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 841-869, December.
    See citations under working paper version above.
  11. De Gooijer, Jan G., 2006. "Detecting change-points in multidimensional stochastic processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1892-1903, December.

    Cited by:

    1. Cécile Bastidon & Antoine Parent & Pablo Jensen & Patrice Abry & Pierre Borgnat, 2020. "Graph-based era segmentation of international financial integration," Post-Print hal-04255796, HAL.
    2. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.
    3. Galeano, Pedro & Wied, Dominik, 2014. "Multiple break detection in the correlation structure of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 262-282.
    4. Cheon, Sooyoung & Kim, Jaehee, 2010. "Multiple change-point detection of multivariate mean vectors with the Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 406-415, February.
    5. David Hallac & Peter Nystrup & Stephen Boyd, 2019. "Greedy Gaussian segmentation of multivariate time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 727-751, September.

  12. 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.

    Cited by:

    1. Zhineng Hu & Jing Ma & Liangwei Yang & Xiaoping Li & Meng Pang, 2019. "Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand," Sustainability, MDPI, vol. 11(5), pages 1-25, February.
    2. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    3. Francisco Salas-Molina & Francisco J. Martin & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Josep Ll. Arcos, 2016. "Empowering cash managers to achieve cost savings by improving predictive accuracy," Papers 1605.04219, arXiv.org.
    4. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 53(6), pages 286-303, January.
    5. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
    6. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
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    124. Daniel Ramos & Pedro Faria & Zita Vale & João Mourinho & Regina Correia, 2020. "Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning," Energies, MDPI, vol. 13(18), pages 1-18, September.
    125. Guidi, Francesco, 2010. "Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models," MPRA Paper 19851, University Library of Munich, Germany.
    126. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    127. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
    128. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
    129. Shagun Srivastava & Madhvendra Misra, 2014. "Developing Evaluation Matrix for Critical Success Factors in Technology Forecasting," Global Business Review, International Management Institute, vol. 15(2), pages 363-380, June.
    130. Lixin Tian & Huan Chen & Zaili Zhen, 2018. "Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    131. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    132. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
    133. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.
    134. 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.
    135. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    136. Fan-Osuala, Onochie & Zantedeschi, Daniel & Jank, Wolfgang, 2018. "Using past contribution patterns to forecast fundraising outcomes in crowdfunding," International Journal of Forecasting, Elsevier, vol. 34(1), pages 30-44.
    137. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.
    138. Delavari, Majid & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," MPRA Paper 45977, University Library of Munich, Germany.
    139. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    140. Hofmeister, Markus & Mosbach, Sebastian & Hammacher, Jörg & Blum, Martin & Röhrig, Gerd & Dörr, Christoph & Flegel, Volker & Bhave, Amit & Kraft, Markus, 2022. "Resource-optimised generation dispatch strategy for district heating systems using dynamic hierarchical optimisation," Applied Energy, Elsevier, vol. 305(C).
    141. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    142. Chew Lian Chua & G. C. Lim & Sarantis Tsiaplias, 2012. "A latent variable approach to forecasting the unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(3), pages 229-244, April.
    143. Appino, Riccardo Remo & González Ordiano, Jorge Ángel & Mikut, Ralf & Faulwasser, Timm & Hagenmeyer, Veit, 2018. "On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages," Applied Energy, Elsevier, vol. 210(C), pages 1207-1218.
    144. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
    145. Abhishek Singh & G. C. Mishra, 2015. "Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 83-96, May.
    146. Sarah Ouédraogo & Ghjuvan Antone Faggianelli & Guillaume Pigelet & Jean Laurent Duchaud & Gilles Notton, 2020. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island," Energies, MDPI, vol. 13(17), pages 1-20, September.
    147. Yingqi Zhu & Ying Wang & Tianxue Liu & Qi Sui, 2018. "Assessing macroeconomic recovery after a natural hazard based on ARIMA—a case study of the 2008 Wenchuan earthquake in China," 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. 91(3), pages 1025-1038, April.
    148. Saeid Mehdizadeh, 2020. "Using AR, MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 263-282, January.
    149. Cheong, Chongcheul & Lee, Hyunchul, 2014. "Forecasting with a parsimonious subset VAR model," Economics Letters, Elsevier, vol. 125(2), pages 167-170.
    150. Roberto Casado-Vara & Angel Martin del Rey & Daniel Pérez-Palau & Luis de-la-Fuente-Valentín & Juan M. Corchado, 2021. "Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
    151. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    152. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2021. "A performance analysis of prediction intervals for count time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 603-625, July.
    153. Savio, Nicolas D. & Nikolopoulos, Konstantinos, 2013. "A strategic forecasting framework for governmental decision-making and planning," International Journal of Forecasting, Elsevier, vol. 29(2), pages 311-321.
    154. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    155. Strunz, Franziska & Gödl, Maximilian, 2023. "An Evaluation of Professional Forecasts for the German Economy," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277707, Verein für Socialpolitik / German Economic Association.
    156. Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.
    157. Ahmadian Behrooz, Hesam & Boozarjomehry, R. Bozorgmehry, 2017. "Dynamic optimization of natural gas networks under customer demand uncertainties," Energy, Elsevier, vol. 134(C), pages 968-983.
    158. Srećko KRILE & Nikolai MAIOROV & Vladimir FETISOV, 2018. "Forecasting The Operational Activities Of The Sea Passenger Terminal Using Intelligent Technologies," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 13(1), pages 27-36, March.
    159. Meade, Nigel & Islam, Towhidul, 2015. "Forecasting in telecommunications and ICT—A review," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1105-1126.
    160. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    161. Christian Hertrich, 2013. "Asset Allocation Considerations for Pension Insurance Funds," Springer Books, Springer, edition 127, number 978-3-658-02167-2, September.
    162. Duwon Choi & Youngkuk An & Nankyu Lee & Jinil Park & Jonghwa Lee, 2020. "Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System," Energies, MDPI, vol. 13(20), pages 1-24, October.

  13. Jan G. De Gooijer & Antoni Vidiella-i-Anguera, 2005. "Estimating threshold cointegrated systems," Economics Bulletin, AccessEcon, vol. 3(8), pages 1-7.

    Cited by:

    1. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.
    2. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.

  14. Garcia-Ferrer, Antonio & De Gooijer, Jan G. & Poncela, Pilar & Ruiz, Esther, 2005. "Introduction to nonlinearities, business cycles, and forecasting," International Journal of Forecasting, Elsevier, vol. 21(4), pages 623-625.

    Cited by:

    1. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.

  15. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.

    Cited by:

    1. Pawel Milobedzki, 2010. "The Term Structure of the Polish Interbank Rates. A Note on the Symmetry of their Reversion to the Mean," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 81-95.
    2. 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.
    3. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.
    4. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    5. 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.
    6. Adusei Jumah & Robert M. Kunst, 2016. "Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging," Applied Economics, Taylor & Francis Journals, vol. 48(45), pages 4366-4378, September.
    7. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2013. "Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes," Empirical Economics, Springer, vol. 44(2), pages 387-417, April.
    8. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.
    9. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    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. Jan G. De Gooijer & Antoni Vidiella-i-Anguera, 2005. "Estimating threshold cointegrated systems," Economics Bulletin, AccessEcon, vol. 3(8), pages 1-7.

  16. Jan G. De Gooijer & Kurt Brännäs, 2004. "Asymmetries in conditional mean and variance: modelling stock returns by asMA-asQGARCH," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 155-171.
    See citations under working paper version above.
  17. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2003. "Nonlinear stochastic inflation modelling using SEASETARs," Insurance: Mathematics and Economics, Elsevier, vol. 32(1), pages 3-18, February.

    Cited by:

    1. Li, Johnny Siu-Hang & Ng, Andrew C.Y. & Chan, Wai-Sum, 2015. "Managing financial risk in Chinese stock markets: Option pricing and modeling under a multivariate threshold autoregression," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 217-230.
    2. Chan, W.S. & Cheung, S.H., 2005. "A bivariate threshold time series model for analyzing Australian interest rates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(5), pages 429-437.

  18. Jan G. De Gooijer & Dawit Zerom, 2003. "On Conditional Density Estimation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(2), pages 159-176, May.
    See citations under working paper version above.
  19. De Gooijer J.G. & Zerom D., 2003. "On Additive Conditional Quantiles With High Dimensional Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 135-146, January.

    Cited by:

    1. Cheng, Yebin & De Gooijer, Jan & Zerom, Dawit, 2009. "Efficient Estimation of an Additive Quantile Regression Model," MPRA Paper 14388, University Library of Munich, Germany.
    2. Tadao Hoshino, 2014. "Quantile regression estimation of partially linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 509-536, September.
    3. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Wu, Tracy Z. & Yu, Keming & Yu, Yan, 2010. "Single-index quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1607-1621, August.
    5. Zhao, Weihua & Lian, Heng, 2017. "Quantile index coefficient model with variable selection," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 40-58.
    6. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    7. Thomas Q. Pedersen, 2015. "Predictable Return Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 114-132, March.
    8. Holger Dette & Regine Scheder, 2011. "Estimation of additive quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(2), pages 245-265, April.
    9. Joel L. Horowitz & Sokbae (Simon) Lee, 2004. "Nonparametric estimation of an additive quantile regression model," CeMMAP working papers CWP07/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Ioannides, D. A., 2004. "Fixed design regression quantiles for time series," Statistics & Probability Letters, Elsevier, vol. 68(3), pages 235-245, July.
    11. Lian, Heng, 2012. "A note on the consistency of Schwarz’s criterion in linear quantile regression with the SCAD penalty," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1224-1228.
    12. Yebin Cheng & Jan G. De Gooijer & Dawit Zerom, 2009. "Efficient Estimation of an Additive Quantile Regression," Tinbergen Institute Discussion Papers 09-104/4, Tinbergen Institute.
    13. Oberhofer, Walter & Haupt, Harry, 2005. "Consistency of nonlinear regression quantiles under Type I censoring weak dependence and general covariate design," University of Regensburg Working Papers in Business, Economics and Management Information Systems 406, University of Regensburg, Department of Economics.
    14. Xiaoping Xu & Zongwu Cai, 2013. "Nonparametric Quantile Estimations For Dynamic Smooth Coefficient Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    15. Zongwu Cai & Zhijie Xiao, 2010. "Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients," Boston College Working Papers in Economics 761, Boston College Department of Economics.
    16. Oberhofer, Walter & Haupt, Harry, 2003. "Nonlinear quantile regression under dependence and heterogeneity," University of Regensburg Working Papers in Business, Economics and Management Information Systems 388, University of Regensburg, Department of Economics.
    17. Cai, Zongwu & Chen, Linna & Fang, Ying, 2018. "A semiparametric quantile panel data model with an application to estimating the growth effect of FDI," Journal of Econometrics, Elsevier, vol. 206(2), pages 531-553.
    18. Oberhofer, Walter & Haupt, Harry, 2005. "The asymptotic distribution of the unconditional quantile estimator under dependence," Statistics & Probability Letters, Elsevier, vol. 73(3), pages 243-250, July.
    19. Christophe Crambes & Ali Gannoun & Yousri Henchiri, 2014. "Modelling functional additive quantile regression using support vector machines approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 639-668, December.
    20. Xie, Shangyu & Wan, Alan T.K. & Zhou, Yong, 2015. "Quantile regression methods with varying-coefficient models for censored data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 154-172.
    21. Taisuke Otsu, 2011. "Empirical Likelihood for Nonparametric Additive Models," Cowles Foundation Discussion Papers 1792, Cowles Foundation for Research in Economics, Yale University.
    22. Holger Dette & Matthias Guhlich & Natalie Neumeyer, 2015. "Testing for additivity in nonparametric quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 437-477, June.
    23. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    24. Joel L. Horowitz & Sokbae (Simon) Lee, 2004. "Nonparametric estimation of an additive quantile regression model," CeMMAP working papers 07/04, Institute for Fiscal Studies.
    25. Sherwood, Ben, 2016. "Variable selection for additive partial linear quantile regression with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 206-223.
    26. Yue, Yu Ryan & Rue, Håvard, 2011. "Bayesian inference for additive mixed quantile regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 84-96, January.
    27. Zongwu Cai & Qi Li, 2013. "Some Recent Develop- ments on Nonparametric Econometrics," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    28. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    29. Lian, Heng & Meng, Jie & Fan, Zengyan, 2015. "Simultaneous estimation of linear conditional quantiles with penalized splines," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 1-21.
    30. Wu, Chaojiang & Yu, Yan, 2014. "Partially linear modeling of conditional quantiles using penalized splines," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 170-187.
    31. Simila, Timo, 2006. "Self-organizing map visualizing conditional quantile functions with multidimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 2097-2110, April.
    32. Holger Dette, 2013. "Comments on: An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 437-441, September.

  20. De Gooijer, Jan G. & Ray, Bonnie K., 2003. "Modeling vector nonlinear time series using POLYMARS," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 73-90, February.

    Cited by:

    1. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
    2. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.

  21. De Gooijer, Jan G. & Gannoun, Ali & Zerom, Dawit, 2002. "Mean squared error properties of the kernel-based multi-stage median predictor for time series," Statistics & Probability Letters, Elsevier, vol. 56(1), pages 51-56, January.

    Cited by:

    1. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    2. Christophe Crambes & Ali Gannoun & Yousri Henchiri, 2014. "Modelling functional additive quantile regression using support vector machines approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 639-668, December.

  22. Gooijer, Jan G. De & Gannoun, Ali, 2000. "Nonparametric conditional predictive regions for time series," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 259-275, May.

    Cited by:

    1. Laïb Naâmane & Lemdani Mohamed & Ould Saïd Elias, 2013. "A functional conditional symmetry test for a GARCH-SM model: Power asymptotic properties," Statistics & Risk Modeling, De Gruyter, vol. 30(1), pages 75-104, March.
    2. Su, Liangjun, 2006. "A simple test for multivariate conditional symmetry," Economics Letters, Elsevier, vol. 93(3), pages 374-378, December.
    3. Di, J. & Kolaczyk, E., 2010. "Complexity-penalized estimation of minimum volume sets for dependent data," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 1910-1926, October.
    4. Ibrahim M. Almanjahie & Zouaoui Chikr Elmezouar & Ali Laksaci & Mustapha Rachdi, 2021. "Smooth k NN Local Linear Estimation of the Conditional Distribution Function," Mathematics, MDPI, vol. 9(10), pages 1-14, May.
    5. Jan G. De Gooijer & Cees G. H. Diks & Łukasz T. Gątarek, 2012. "Information Flows Around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(1), pages 23-44, March.
    6. Tao Huang & Jialiang Li, 2018. "Semiparametric model average prediction in panel data analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 125-144, January.
    7. Tierney, Heather L.R., 2013. "Forecasting and Tracking Real-Time Data Revisions in Inflation Persistence," MPRA Paper 53374, University Library of Munich, Germany, revised Nov 2013.
    8. Kara, Lydia-Zaitri & Laksaci, Ali & Rachdi, Mustapha & Vieu, Philippe, 2017. "Data-driven kNN estimation in nonparametric functional data analysis," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 176-188.
    9. 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.
    10. Hyndman, R.J. & Yao, Q., 1998. "Nonparametric Estimation and Symmetry Tests for Conditional Density Functions," Monash Econometrics and Business Statistics Working Papers 17/98, Monash University, Department of Econometrics and Business Statistics.
    11. 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.
    12. Mohamed Chaouch, 2023. "Probabilistic Wind Speed Forecasting for Wind Turbine Allocation in the Power Grid," Energies, MDPI, vol. 16(22), pages 1-15, November.
    13. Silvano Bordignon & Francesco Lisi, 2001. "Interval prediction for chaotic time series," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 117-140.
    14. Liangjun Su & Sainan Jin, 2005. "A Bootstrap Test for Conditional Symmetry," Annals of Economics and Finance, Society for AEF, vol. 6(2), pages 251-261, November.

  23. De Gooijer, Jan G & MacNeill, Ian B, 1999. "Lagged Regression Residuals and Serial-Correlation Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 236-247, April.

    Cited by:

    1. Jan Gooijer, 2008. "Partial sums of lagged cross-products of AR residuals and a test for white noise," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 567-584, November.

  24. Jan De Gooijer, 1998. "On threshold moving‐average models," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 1-18, January.

    Cited by:

    1. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Xiaobing Zheng & Kun Liang & Qiang Xia & Dabin Zhang, 2022. "Best Subset Selection for Double-Threshold-Variable Autoregressive Moving-Average Models: The Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1175-1201, March.

  25. De Gooijer, Jan G. & Ray, Bonnie K. & Krager, Horst, 1998. "Forecasting exchange rates using TSMARS," Journal of International Money and Finance, Elsevier, vol. 17(3), pages 513-534, June.

    Cited by:

    1. Peter Sephton, 2008. "Critical values of the augmented fractional Dickey–Fuller test," Empirical Economics, Springer, vol. 35(3), pages 437-450, November.
    2. 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.
    3. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    4. Petra M. Kuhnert & Kerrie Mengersen & Peter Tesar, 2003. "Bridging the Gap between Different Statistical Approaches: An Integrated Framework for Modelling," International Statistical Review, International Statistical Institute, vol. 71(2), pages 335-368, August.
    5. Peter Sephton, 2001. "Forecasting recessions: can we do better on MARS?," Review, Federal Reserve Bank of St. Louis, vol. 83(Mar), pages 39-49.
    6. De Gooijer, Jan G. & Ray, Bonnie K., 2003. "Modeling vector nonlinear time series using POLYMARS," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 73-90, February.
    7. Christos Katris, 2020. "Prediction of Unemployment Rates with Time Series and Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 673-706, February.
    8. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.

  26. De Gooijer, Jan G. & De Bruin, Paul T., 1998. "On forecasting SETAR processes," Statistics & Probability Letters, Elsevier, vol. 37(1), pages 7-14, January.

    Cited by:

    1. Ramazan Gencay & Ege Yazgan, 2017. "When Are Wavelets Useful Forecasters?," Working Papers 1704, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    2. 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.
    3. 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.
    4. 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.
    5. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    6. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    7. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    8. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    9. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    10. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
    11. Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.
    12. 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.
    13. 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.
    14. De Gooijer, Jan G. & Ray, Bonnie K., 2003. "Modeling vector nonlinear time series using POLYMARS," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 73-90, February.
    15. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.

  27. De Gooijer, Jan G. & Franses, Philip Hans, 1997. "Forecasting and seasonality," International Journal of Forecasting, Elsevier, vol. 13(3), pages 303-305, September.

    Cited by:

    1. Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
    2. Albertson, Kevin & Aylen, Jonathan, 2003. "Forecasting the behaviour of manufacturing inventory," International Journal of Forecasting, Elsevier, vol. 19(2), pages 299-311.
    3. Albertson, Kevin & Aylen, Jonathan, 1999. "Forecasting using a periodic transfer function: with an application to the UK price of ferrous scrap," International Journal of Forecasting, Elsevier, vol. 15(4), pages 409-419, October.

  28. De Gooijer, Jan G., 1993. "On predictive least squares principles : C.Z. Wei, The Annals of Statistics 20 (1992), 1-42," International Journal of Forecasting, Elsevier, vol. 9(1), pages 138-139, April.

    Cited by:

    1. Christoph Breunig & Stefan Hoderlein, 2016. "Nonparametric Specification Testing in Random Parameter Models," Boston College Working Papers in Economics 897, Boston College Department of Economics.
    2. Christoph Breunig & Stefan Hoderlein, 2018. "Specification testing in random coefficient models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1371-1417, November.
    3. Christoph Breunig, 2017. "Testing Missing at Random using Instrumental Variables," SFB 649 Discussion Papers SFB649DP2017-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  29. Peter Molenaar & Jan Gooijer & Bernhard Schmitz, 1992. "Dynamic factor analysis of nonstationary multivariate time series," Psychometrika, Springer;The Psychometric Society, vol. 57(3), pages 333-349, September.

    Cited by:

    1. Nikolaos Zirogiannis & Yorghos Tripodis, 2018. "Dynamic factor analysis for short panels: estimating performance trajectories for water utilities," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 131-150, March.
    2. Fei Gu & Kristopher J. Preacher & Emilio Ferrer, 2014. "A State Space Modeling Approach to Mediation Analysis," Journal of Educational and Behavioral Statistics, , vol. 39(2), pages 117-143, April.
    3. Lyhagen, Johan, 2005. "The exact covariance matrix of dynamic models with latent variables," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 133-139, November.
    4. Peter Molenaar, 1999. "Comment on fitting MA time series by structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 91-94, March.
    5. Ortega, Jose Antonio & Poncela, Pilar, 2005. "Joint forecasts of Southern European fertility rates with non-stationary dynamic factor models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 539-550.
    6. Carfora, Alfonso & Scandurra, Giuseppe & Thomas, Antonio, 2022. "Forecasting the COVID-19 effects on energy poverty across EU member states," Energy Policy, Elsevier, vol. 161(C).
    7. Eichler, Michael & Motta, Giovanni & von Sachs, Rainer, 2011. "Fitting dynamic factor models to non-stationary time series," Journal of Econometrics, Elsevier, vol. 163(1), pages 51-70, July.
    8. Xiaochun Jiang & Wei Sun & Peng Su & Ting Wang, 2019. "The Synergy of Financial Volatility between China and the United States and the Risk Conduction Paths," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    9. Chang, Lei & Gan, Xiaojun & Mohsin, Muhammad, 2022. "Studying corporate liquidity and regulatory responses for economic recovery in COVID-19 crises," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 211-225.
    10. Galeano, Pedro & Peña, Daniel, 2001. "Multivariate analysis in vector time series," DES - Working Papers. Statistics and Econometrics. WS ws012415, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Peter Molenaar & John Nesselroade, 2001. "Rotation in the dynamic factor modeling of multivariate stationary time series," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 99-107, March.
    12. Stef Buuren, 1997. "Fitting arma time series by structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 62(2), pages 215-236, June.
    13. Montfort, Kees van & Bijleveld, Catrien, 1997. "Dynamic analysis of multivariate panel data with nonlinear transformations," Serie Research Memoranda 0054, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

  30. De Gooijer, Jan G. & Kumar, Kuldeep, 1992. "Some recent developments in non-linear time series modelling, testing, and forecasting," International Journal of Forecasting, Elsevier, vol. 8(2), pages 135-156, October.

    Cited by:

    1. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Buncic, Daniel, 2009. "Understanding forecast failure in ESTAR models of real exchange rates," MPRA Paper 13121, University Library of Munich, Germany.
    4. N. Terui & Herman K. van Dijk, 2000. "Combined Forecasts from Linear and Nonlinear Time Series Models," Tinbergen Institute Discussion Papers 00-003/4, Tinbergen Institute.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," AQR Working Papers 201410, University of Barcelona, Regional Quantitative Analysis Group, revised May 2014.
    6. Rituparna Kar & Nityananda Sarkar, 2006. "Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(1), pages 41-69, March.
    7. Clements, Michael P & Smith, Jeremy, 1999. "A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 123-141, March-Apr.
    8. Jing Li, 2021. "Block bootstrap prediction intervals for parsimonious first‐order vector autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 512-527, April.
    9. Rossen, Anja, 2014. "On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations," HWWI Research Papers 157, Hamburg Institute of International Economics (HWWI).
    10. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    11. 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.
    12. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    13. Filelis - Papadopoulos, Christos K. & Kyziropoulos, Panagiotis E. & Morrison, John P. & O‘Reilly, Philip, 2022. "Modelling and forecasting based on recursive incomplete pseudoinverse matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 358-376.
    14. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    15. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    16. 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.
    17. Gary Madden & Joachim Tan, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," Applied Economics, Taylor & Francis Journals, vol. 40(14), pages 1775-1787.
    18. Clements, M.P. & Franses, Ph.H.B.F. & Smith, J., 1999. "On SETAR non- linearity and forecasting," Econometric Institute Research Papers EI 9914-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Blanco, Aníbal M. & Chantre, Guillermo R. & Lodovichi, Mariela V. & Bandoni, J. Alberto & López, Ricardo L. & Vigna, Mario R. & Gigón, Ramón & Sabbatini, Mario R., 2014. "Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach," Ecological Modelling, Elsevier, vol. 272(C), pages 293-300.
    20. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
    21. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    22. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    23. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    24. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    25. 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.
    26. Fu, Zhonghao & Hong, Yongmiao, 2019. "A model-free consistent test for structural change in regression possibly with endogeneity," Journal of Econometrics, Elsevier, vol. 211(1), pages 206-242.
    27. Nadir Ocal & Denise R. Osborn, 2000. "Business cycle non-linearities in UK consumption and production," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 27-43.
    28. Etaf Alshawarbeh & Alanazi Talal Abdulrahman & Eslam Hussam, 2023. "Statistical Modeling of High Frequency Datasets Using the ARIMA-ANN Hybrid," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
    29. De Gooijer, Jan G. & Ray, Bonnie K. & Krager, Horst, 1998. "Forecasting exchange rates using TSMARS," Journal of International Money and Finance, Elsevier, vol. 17(3), pages 513-534, June.
    30. Guy Melard, 1994. "Modèles linéaires et non linéaires," ULB Institutional Repository 2013/13804, ULB -- Universite Libre de Bruxelles.
    31. Clements, Michael P. & Smith, Jeremy, 1996. "The Performance of Alternative Forecasting Methods for SETAR Models," Economic Research Papers 268737, University of Warwick - Department of Economics.
    32. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    33. Anoop Chaturvedi & Shivam Jaiswal, 2020. "Bayesian Estimation and Unit Root Test for Logistic Smooth Transition Autoregressive Process," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(4), pages 733-745, December.
    34. Michael Štencl & Ondřej Popelka & Jiří Šťastný, 2012. "Forecast of consumer behaviour based on neural networks models comparison," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 60(2), pages 437-442.
    35. Yue Fang & John Zhang, 1999. "Performance of control charts for autoregressive conditional heteroscedastic processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 701-714.
    36. Debasis Mithiya & Kumarjit Mandal & Simanti Bandyopadhyay, 2020. "Time Series Analysis and Forecasting of Rainfall for Agricultural Crops in India: An Application of Artificial Neural Network," Research in Applied Economics, Macrothink Institute, vol. 12(4), pages 1-21, December.
    37. Crespo-Cuaresma, Jesus, 2000. "Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning," Economics Series 79, Institute for Advanced Studies.
    38. King Chi Hung & Siu Hung Cheung & Wai-Sum Chan & Li-Xin Zhang, 2009. "On a robust test for SETAR-type nonlinearity in time series analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 445-464.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
    44. Dilip Nachane & Jose Clavel, 2008. "Forecasting interest rates: a comparative assessment of some second-generation nonlinear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 493-514.
    45. 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.
    46. Martín González-Rozada & Luis Pereiro, 2013. "Forecasting Prices in Regime-Switching Markets," Department of Economics Working Papers 2013_2, Universidad Torcuato Di Tella.
    47. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    48. McMillan, David G., 2007. "Non-linear forecasting of stock returns: Does volume help?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 115-126.
    49. Wu, Berlin, 1995. "Model-free forecasting for nonlinear time series (with application to exchange rates)," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 433-459, April.
    50. Kasai, Ndahiriwe & Naraidoo, Ruthira, 2011. "Evaluating the forecasting performance of linear and nonlinear monetary policy rules for South Africa," MPRA Paper 40699, University Library of Munich, Germany.
    51. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    52. 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.
    53. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
    54. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    55. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean-Marie, 2005. "A global forecasting support system adapted to textile distribution," International Journal of Production Economics, Elsevier, vol. 96(1), pages 81-95, April.
    56. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    57. Mohamed A. Ismail & Husni A. Charif, 2003. "Bayesian inference for threshold moving average models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 119-132.

  31. de Gooijer, Jan G. & Klein, Andre, 1992. "On the cumulated multi-step-ahead predictions of vector autoregressive moving average processes," International Journal of Forecasting, Elsevier, vol. 7(4), pages 501-513, March.

    Cited by:

    1. Kajal Lahiri & Gultekin Isiklar, 2006. "How Far Ahead Can We Forecast? Evidence From Cross-country Surveys," Discussion Papers 06-04, University at Albany, SUNY, Department of Economics.
    2. 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.
    3. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do forecasters inform or reassure?," KOF Working papers 09-215, KOF Swiss Economic Institute, ETH Zurich.

  32. de Gooijer, Jap G., 1990. "The role of time series analysis in forecasting: A personal view," International Journal of Forecasting, Elsevier, vol. 6(4), pages 449-451, December.

    Cited by:

    1. Ord, Keith, 1995. "The future of the International Journal of Forecasting," International Journal of Forecasting, Elsevier, vol. 11(2), pages 197-198, June.
    2. 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.
    3. 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.

  33. De Gooijer, Jan G., 1989. "Testing non-linearities in world stock market prices," Economics Letters, Elsevier, vol. 31(1), pages 31-35.

    Cited by:

    1. Kian-Ping Lim & Melvin J. Hinich, 2005. "Cross-temporal universality of non-linear dependencies in Asian stock markets," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-6.
    2. Philip Rothman, "undated". "Higher-Order Residual Analysis for Simple Bilinear and Threshold Autoregressive Models with the TR Test," Working Papers 9813, East Carolina University, Department of Economics.
    3. Othieno, Ferdinand & Biekpe, Nicholas, 2019. "Estimating the conditional equity risk premium in African frontier markets," Research in International Business and Finance, Elsevier, vol. 47(C), pages 538-551.

  34. De Gooijer, Jan G., 1980. "Exact moments of the sample autocorrelations from series generated by general arima processes of order (p, d, q), d=0 or 1," Journal of Econometrics, Elsevier, vol. 14(3), pages 365-379, December.

    Cited by:

    1. Palm, F.C. & Sneek, J.M., 1981. "Some econometric applications of the exact distribution of the ratio of two quadratic forms in normal variates," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. Genton, Marc G., 1999. "The correlation structure of the sample autocovariance function for a particular class of time series with elliptically contoured distribution," Statistics & Probability Letters, Elsevier, vol. 41(2), pages 131-137, January.
    3. Hillier, Grant & Kan, Raymond & Wang, Xiaolu, 2009. "Computationally Efficient Recursions For Top-Order Invariant Polynomials With Applications," Econometric Theory, Cambridge University Press, vol. 25(1), pages 211-242, February.
    4. Jacob Boudoukh & Ronen Israel & Matthew P. Richardson, 2020. "Biases in Long-Horizon Predictive Regressions," NBER Working Papers 27410, National Bureau of Economic Research, Inc.
    5. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    6. Paolella, Marc S., 2003. "Computing moments of ratios of quadratic forms in normal variables," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 313-331, March.
    7. Sneek, J.M., 1982. "Some approximations to the exact distribution of sample autocorrelations for autoregressive moving average models," Serie Research Memoranda 0002, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

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