IDEAS home Printed from https://ideas.repec.org/f/c/pca513.html

Carlos Capistrán
(Carlos Capistran)

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. Capistrán Carlos & Chiquiar Daniel & Hernández Juan R., 2017. "Identifying Dornbusch's Exchange Rate Overshooting with Structural VECs: Evidence from Mexico," Working Papers 2017-11, Banco de México.

    Cited by:

    1. Hernández, Juan R. & Ventosa-Santaulària, Daniel & Valencia, J. Eduardo, 2024. "Global supply chain inflationary pressures and monetary policy in Mexico," Emerging Markets Review, Elsevier, vol. 58(C).
    2. I Made Suidarma & I Gede Sanica & Putu Cita Ayu & I Gusti Nengah Darma Diatmika, 2018. "Overshooting Indonesian Rupiah's Exchange Rate towards US Dollar: Dornbusch Model Hypotheses Test," International Journal of Economics and Financial Issues, Econjournals, vol. 8(5), pages 52-58.

  2. Ramos Francia Manuel & Cuadra Gabriel & Capistrán Carlos, 2011. "Policy Response to External Shocks: Lessons from the Crisis," Working Papers 2011-14, Banco de México.

    Cited by:

    1. Patrick Ologbenla, 2019. "Fiscal Policy and External Shocks in Nigeria," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 129-138.
    2. Baranov, Igor N. & Castro, P. C. & Micic, D. & Salgado, G. B., 2013. "Fiscal Impulse during the 2008 Crisis," Working Papers 809, Graduate School of Management, St. Petersburg State University.
    3. Juda Agung & Solikin M Juhro & Harmanta & Tarsidin, 2016. "Managing monetary and financial stability in a dynamic global environment: Bank Indonesia s policy perspectives," BIS Papers chapters, in: Bank for International Settlements (ed.), Expanding the boundaries of monetary policy in Asia and the Pacific, volume 88, pages 157-188, Bank for International Settlements.

  3. Capistrán Carlos & Ibarra-Ramírez Raúl & Ramos Francia Manuel, 2011. "Exchange Rate Pass-Through to Prices: Evidence from Mexico," Working Papers 2011-12, Banco de México.

    Cited by:

    1. Hern�n Rinc�n-Castro & Norberto Rodr�guez-Ni�o, 2016. "Nonlinear Pass-Through of Exchange Rate Shocks on Inflation: A Bayesian Smooth Transition VAR Approach," Borradores de Economia 14299, Banco de la Republica.
    2. Garcés Díaz Daniel, 2016. "Changes in Inflation Predictability in Major Latin American Countries," Working Papers 2016-20, Banco de México.
    3. Chavarín, Ricardo & Gómez, Ricardo & Salgado, Alfredo, 2023. "Sectoral supply and demand shocks during COVID-19: Evidence from Mexico," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(1).
    4. López-Martín Bernabé & Ramírez de Aguilar Alberto & Sámano Daniel, 2018. "Fiscal Policy and Inflation: Understanding the Role of Expectations in Mexico," Working Papers 2018-18, Banco de México.
    5. Hernán Rincón-Castro & Norberto Rodríguez-Niño, 2018. "Nonlinear state and shock dependence of exchange rate pass through on prices," BIS Working Papers 690, Bank for International Settlements.

  4. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    2. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    3. Tu, Yundong & Yi, Yanping, 2017. "Forecasting cointegrated nonstationary time series with time-varying variance," Journal of Econometrics, Elsevier, vol. 196(1), pages 83-98.
    4. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    5. Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
    6. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    7. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    8. Ercio Muñoz & Miguel Ricaurte & Mariel Siravegna, 2012. "Combinación de Proyecciones para el Precio del Petróleo: Aplicación y Evaluación de Metodologías," Working Papers Central Bank of Chile 660, Central Bank of Chile.
    9. George Papadopoulos & Savas Papadopoulos & Thomas Sager, 2016. "Credit risk stress testing for EU15 banks: a model combination approach," Working Papers 203, Bank of Greece.
    10. Mr. Emil Stavrev, 2006. "Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy," IMF Working Papers 2006/197, International Monetary Fund.
    11. Dan Zhu & Qingwei Wang & John Goddard, 2022. "A new hedging hypothesis regarding prediction interval formation in stock price forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 697-717, July.
    12. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    13. Hansen, Bruce E., 2010. "Averaging estimators for autoregressions with a near unit root," Journal of Econometrics, Elsevier, vol. 158(1), pages 142-155, September.
    14. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    15. Wolters, Maik H., 2013. "Evaluating point and density forecasts of DSGE models," Economics Working Papers 2013-03, Christian-Albrechts-University of Kiel, Department of Economics.
    16. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    17. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joëlle, 2019. "A comprehensive appraisal of style-integration methods," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 134-150.
    18. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    19. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    20. Yu-chin Chen & Kwok Ping Tsang & Wen Jen Tsay, 2010. "Home Bias in Currency Forecasts," Working Papers 272010, Hong Kong Institute for Monetary Research.
    21. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2009. "Do Composite Procedures Really Improve the Accuracy of Outlook Forecasts?," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53052, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    22. Wang, Cindy S.H. & Fan, Rui & Xie, Yiqiang, 2023. "Market systemic risk, predictability and macroeconomics news," Finance Research Letters, Elsevier, vol. 56(C).
    23. Scott A. Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019. "Predicting Benchmarked US State Employment Data in Real Time," Working Papers 2019-037, Federal Reserve Bank of St. Louis, revised 11 Mar 2021.
    24. 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.
    25. 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.
    26. Canova, Fabio & Matthes, Christian, 2018. "A composite likelihood approach for dynamic structural models," CEPR Discussion Papers 13245, C.E.P.R. Discussion Papers.
    27. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    28. Ma, Feng & Guo, Yangli & Luo, Qin & Zhong, Juandan, 2025. "Forecasting corporate bond returns amid climate change risk: A dynamic forecast combination approach," Journal of International Money and Finance, Elsevier, vol. 154(C).
    29. Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, Department of Economics and Business Economics, Aarhus University.
    30. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    31. Jinjun Liu & Ming-Yen Cheng, 2026. "Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach," Papers 2601.04608, arXiv.org.
    32. Laura Carabotta & Peter Claeys, 2024. "Combine to compete: Improving fiscal forecast accuracy over time," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 948-982, July.
    33. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    34. Eckert, Florian & Hyndman, Rob J. & Panagiotelis, Anastasios, 2021. "Forecasting Swiss exports using Bayesian forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 693-710.
    35. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    36. Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," International Finance 0503006, University Library of Munich, Germany.
    37. Robin C. Sickles & Jiaqi Hao & Chenjun Shang, 2014. "Panel data and productivity measurement: an analysis of Asian productivity trends," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 12(3), pages 211-231, August.
    38. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    39. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    40. Blanc, Sebastian M. & Setzer, Thomas, 2016. "When to choose the simple average in forecast combination," Journal of Business Research, Elsevier, vol. 69(10), pages 3951-3962.
    41. Shi, Chunpei & Wei, Yu & Li, Xiafei & Liu, Yuntong, 2023. "Combination forecasts of China's oil futures returns based on multiple uncertainties and their connectedness with oil," Energy Economics, Elsevier, vol. 126(C).
    42. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    43. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    44. Sermpinis, Georgios & Stasinakis, Charalampos & Hassanniakalager, Arman, 2017. "Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds," European Journal of Operational Research, Elsevier, vol. 263(2), pages 540-558.
    45. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    46. Philippe Andrade & Valère Fourel & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    47. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    48. Ma, Feng & Lu, Fei & Tao, Ying, 2022. "Geopolitical risk and excess stock returns predictability: New evidence from a century of data," Finance Research Letters, Elsevier, vol. 50(C).
    49. Faias, José Afonso, 2023. "Predicting the equity risk premium using the smooth cross-sectional tail risk: The importance of correlation," Journal of Financial Markets, Elsevier, vol. 63(C).
    50. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    51. Keijsers, Bart & van Dijk, Dick, 2025. "Does economic uncertainty predict real activity in real time?," International Journal of Forecasting, Elsevier, vol. 41(2), pages 748-762.
    52. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29.
    53. Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," Série Textos para Discussão (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.
    54. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    55. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    56. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    57. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    58. Svec, Jiri & Katrak, Xerxis, 2017. "Forecasting volatility with interacting multiple models," Finance Research Letters, Elsevier, vol. 20(C), pages 245-252.
    59. Zanetti Chini, Emilio, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 34(4), pages 711-732.
    60. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
    61. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    62. Lavancier, F. & Rochet, P., 2016. "A general procedure to combine estimators," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 175-192.
    63. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Paper 2020/14, Norges Bank.
    64. Liao, Cunfei & Luo, Qianlin & Tang, Guohao, 2021. "Aggregate liquidity premium and cross-sectional returns: Evidence from China," Economic Modelling, Elsevier, vol. 104(C).
    65. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
    66. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    67. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    68. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
    69. Zhu, Min & Chen, Rui & Du, Ke & Wang, You-Gan, 2018. "Dividend growth and equity premium predictability," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 125-137.
    70. Shu, Lei & Hao, Yifan & Chen, Yu & Yang, Qing, 2025. "SFQRA: Scaled factor-augmented quantile regression with aggregation in conditional mean forecasting," Journal of Multivariate Analysis, Elsevier, vol. 207(C).
    71. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    72. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    73. Davide De Gaetano, 2016. "Forecast Combinations For Realized Volatility In Presence Of Structural Breaks," Departmental Working Papers of Economics - University 'Roma Tre' 0208, Department of Economics - University Roma Tre.
    74. Carlos Segura-Rodriguez, 2025. "Inflation Forecasting in Costa Rica: The Contribution of Exogenous Variables in Item-Level Disaggregated Models," Documentos de Trabajo 2509, Banco Central de Costa Rica.
    75. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 642-675, October.
    76. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    77. Lv, Wendai & Qi, Jipeng, 2022. "Stock market return predictability: A combination forecast perspective," International Review of Financial Analysis, Elsevier, vol. 84(C).
    78. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    79. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    80. Adolfo Rodríguez-Vargas, 2019. "Univariate Forecasts for Costa Rican Inflation With Stochastic Volatility and GARCH Effects," Documentos de Trabajo 1604, Banco Central de Costa Rica.
    81. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2008. "How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37620, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    82. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    83. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    84. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.
    85. Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers 80, Brandeis University, Department of Economics and International Business School.
    86. Cheng, Gang & Yang, Yuhong, 2015. "Forecast combination with outlier protection," International Journal of Forecasting, Elsevier, vol. 31(2), pages 223-237.
    87. 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.
    88. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Fotios Petropoulos, 2015. "Forecasting with Temporal Hierarchies," Monash Econometrics and Business Statistics Working Papers 16/15, Monash University, Department of Econometrics and Business Statistics.
    89. 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.
    90. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
    91. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    92. 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.
    93. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    94. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    95. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    96. 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.
    97. 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.
    98. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    99. Jan Capek & Jesus Crespo Cuaresma & Niko Hauzenberger & Vlastimil Reichel, 2020. "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," Department of Economics Working Papers wuwp305, Vienna University of Economics and Business, Department of Economics.
    100. Costantini, Mauro & Gunter, Ulrich & Kunst, Robert M., 2014. "Forecast combinations in a DSGE-VAR lab," Economics Series 309, Institute for Advanced Studies.
    101. 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.
    102. Di Fonzo, Tommaso & Girolimetto, Daniele, 2024. "Forecast combination-based forecast reconciliation: Insights and extensions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 490-514.
    103. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
    104. 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.
    105. Huck, Nicolas, 2009. "Pairs selection and outranking: An application to the S&P 100 index," European Journal of Operational Research, Elsevier, vol. 196(2), pages 819-825, July.
    106. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    107. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    108. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    109. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    110. 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.
    111. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "How to Estimate Beta?," Hannover Economic Papers (HEP) dp-617, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    112. Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
    113. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "Estimating beta: Forecast adjustments and the impact of stock characteristics for a broad cross-section," Journal of Financial Markets, Elsevier, vol. 44(C), pages 91-118.
    114. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    115. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    116. Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, vol. 26(3), pages 498-510, July.
    117. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    118. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    119. Issler, João Victor & Rodrigues, Claudia & Burjack, Rafael, 2014. "Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 310-335.
    120. Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019. "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series 28, Bank of Lithuania.
    121. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
    122. Lin, Hai & Tao, Xinyuan & Wu, Chunchi, 2022. "Forecasting earnings with combination of analyst forecasts," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 133-159.
    123. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    124. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    125. Joseph P & Romano & Azeem M. Shaikh & Michael Wolf, 2005. "Formalized Data Snooping Based on Generalized Error Rates," IEW - Working Papers 259, Institute for Empirical Research in Economics - University of Zurich.
    126. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.
    127. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    128. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
    129. Mihaela Bratu (Simionescu), 2012. "Improving the accuracy of consensus forecasts for the EURO area," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 4(2), pages 11-15, Decembre.
    130. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving forecasting performance by window and model averaging," CAMA Working Papers 2011-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    131. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    132. Till Weigt & Bernd Wilfling, 2016. "A new combination approach to reducing forecast errors with an application to volatility forecasting," CQE Working Papers 4616, Center for Quantitative Economics (CQE), University of Muenster.
    133. Wang, Chengyang & Nishiyama, Yoshihiko, 2015. "Volatility forecast of stock indices by model averaging using high-frequency data," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 324-337.
    134. 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.
    135. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    136. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    137. Christopher G. Gibbs, 2017. "Forecast combination, non-linear dynamics, and the macroeconomy," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(3), pages 653-686, March.
    138. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    139. Jiang, Yu & Guo, Yongji & Zhang, Yihao, 2017. "Forecasting China's GDP growth using dynamic factors and mixed-frequency data," Economic Modelling, Elsevier, vol. 66(C), pages 132-138.
    140. Park, Jin Suk & Newaz, Mohammad Khaleq, 2021. "Liquidity and short-run predictability: Evidence from international stock markets," Global Finance Journal, Elsevier, vol. 50(C).
    141. Ana Sofia Monteiro & Helder Sebastião & Nuno Silva, 2025. "Prediction and Allocation of Stocks, Bonds, and REITs in the US Market," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1191-1230, March.
    142. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    143. Antonios K. Alexandridis & Ekaterini Panopoulou & Ioannis Souropanis, 2024. "Forecasting exchange rates: An iterated combination constrained predictor approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 983-1017, July.
    144. Likun Lei & Yaojie Zhang & Yu Wei & Yi Zhang, 2021. "Forecasting the volatility of Chinese stock market: An international volatility index," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1336-1350, January.
    145. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    146. Hong, Han & Preston, Bruce, 2012. "Bayesian averaging, prediction and nonnested model selection," Journal of Econometrics, Elsevier, vol. 167(2), pages 358-369.
    147. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    148. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    149. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    150. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
    151. 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.
    152. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    153. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    154. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    155. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    156. 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.
    157. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    158. 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.
    159. Matsypura, Dmytro & Thompson, Ryan & Vasnev, Andrey L., 2018. "Optimal selection of expert forecasts with integer programming," Omega, Elsevier, vol. 78(C), pages 165-175.
    160. Stein, Tobias, 2024. "Forecasting the equity premium with frequency-decomposed technical indicators," International Journal of Forecasting, Elsevier, vol. 40(1), pages 6-28.
    161. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2018. "Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods," MPRA Paper 85523, University Library of Munich, Germany.
    162. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
    163. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    164. Magnus, Jan R. & Vasnev, Andrey L., 2023. "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1895-1908.
    165. Wei, Wei & Zhu, Dan, 2022. "Generic improvements to least squares monte carlo methods with applications to optimal stopping problems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1132-1144.
    166. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    167. Haase, Felix & Neuenkirch, Matthias, 2023. "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, vol. 39(2), pages 587-605.
    168. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    169. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    170. Reichlin, Lucrezia & Andreini, Paolo & Hasenzagl, Thomas & Senftleben-König, Charlotte & Strohsal, Till, 2020. "Nowcasting German GDP," CEPR Discussion Papers 14323, C.E.P.R. Discussion Papers.
    171. Timmermann, Allan & Guidolin, Massimo, 2007. "Forecasts of US Short-term Interest Rates: A Flexible Forecast Combination Approach," CEPR Discussion Papers 6188, C.E.P.R. Discussion Papers.
    172. 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.
    173. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
    174. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
    175. Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022. "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, vol. 106(C).
    176. Chao, Shih-Wei, 2016. "Do economic variables improve bond return volatility forecasts?," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 10-26.
    177. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    178. Robert Lehmann & Klaus Wohlrabe, 2013. "Sektorale Prognosen und deren Machbarkeit auf regionaler Ebene – Das Beispiel Sachsen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 20(04), pages 22-29, August.
    179. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    180. D’Amico, Stefania & King, Thomas B., 2023. "What does anticipated monetary policy do?," Journal of Monetary Economics, Elsevier, vol. 138(C), pages 123-139.
    181. Canova, Fabio & Matthes, Christian, 2019. "Dealing with misspecification in structural macroeconometric models," CEPR Discussion Papers 13511, C.E.P.R. Discussion Papers.
    182. Ren, Yu & Liang, Xuanxuan & Wang, Qin, 2021. "Short-term exchange rate forecasting: A panel combination approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    183. 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.
    184. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2021. "Systemic risk measures and distribution forecasting of macroeconomic shocks," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 178-196.
    185. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    186. Daniel Detzer & Christian R. Proaño & Katja Rietzler & Sven Schreiber & Thomas Theobald & Sabine Stephan, 2012. "Verfahren der konjunkturellen Wendepunktbestimmung unter Berücksichtigung der Echtzeit-Problematik," IMK Studies 27-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    187. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    188. Kawakami, Kei, 2013. "Conditional forecast selection from many forecasts: An application to the Yen/Dollar exchange rate," Journal of the Japanese and International Economies, Elsevier, vol. 28(C), pages 1-18.
    189. Alexandridis, Antonios K. & Panopoulou, Ekaterini & Souropanis, Ioannis, 2024. "Forecasting exchange rate volatility: An amalgamation approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 97(C).
    190. Li, Kaixin & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil returns with oil-related industry ESG indices," Journal of Commodity Markets, Elsevier, vol. 36(C).
    191. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    192. 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.
    193. Kajal Lahiri & Liu Yang, 2015. "A Non-linear Forecast Combination Procedure for Binary Outcomes," CESifo Working Paper Series 5175, CESifo.
    194. Fang, Debin & Hao, Peng & Hao, Jian, 2019. "Study of the influence mechanism of China's electricity consumption based on multi-period ST-LMDI model," Energy, Elsevier, vol. 170(C), pages 730-743.
    195. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    196. 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.
    197. 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.
    198. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.
    199. Bingzi Jin & Xiaojie Xu, 2025. "Forecasts of coking coal futures price indices through Gaussian process regressions," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 203-217, March.
    200. Monticini, Andrea & Ravazzolo, Francesco, 2014. "Forecasting the intraday market price of money," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 304-315.
    201. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    202. 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.
    203. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    204. Till Weigt & Bernd Wilfling, 2021. "An approach to increasing forecast‐combination accuracy through VAR error modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 686-699, July.
    205. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank, Research and Statistics Department.
    206. Catherine L. Kling & Raymond W. Arritt & Gray Calhoun & David A. Keiser, 2017. "Integrated Assessment Models of the Food, Energy, and Water Nexus: A Review and an Outline of Research Needs," Annual Review of Resource Economics, Annual Reviews, vol. 9(1), pages 143-163, October.
    207. 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.
    208. Jon D. Samuels & Rodrigo Sekkel, 2013. "Forecasting with Many Models: Model Confidence Sets and Forecast Combination," Staff Working Papers 13-11, Bank of Canada.
    209. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    210. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    211. 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.
    212. Hubert Dichtl & Wolfgang Drobetz & Viktoria‐Sophie Wendt, 2021. "How to build a factor portfolio: Does the allocation strategy matter?," European Financial Management, European Financial Management Association, vol. 27(1), pages 20-58, January.
    213. Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023. "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, vol. 121(C).
    214. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    215. A. Ford Ramsey & Yong Liu, 2023. "Linear pooling of potentially related density forecasts in crop insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(3), pages 769-788, September.
    216. Todd E. Clark & Taisuke Nakata, 2006. "The trend growth rate of employment : past, present, and future," Economic Review, Federal Reserve Bank of Kansas City, vol. 91(Q I), pages 43-85.
    217. Sancetta, A., 2007. "Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains," Cambridge Working Papers in Economics 0735, Faculty of Economics, University of Cambridge.
    218. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
    219. Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, C.E.P.R. Discussion Papers.
    220. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    221. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    222. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    223. Cai, Michael & Del Negro, Marco & Giannoni, Marc P. & Gupta, Abhi & Li, Pearl & Moszkowski, Erica, 2019. "DSGE forecasts of the lost recovery," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1770-1789.
    224. Adam Elbourne & Henk Kranendonk & Rob Luginbuhl & Bert Smid & Martin Vromans, 2008. "Evaluating CPB's published GDP growth forecasts; a comparison with individual and pooled VAR based forecasts," CPB Document 172, CPB Netherlands Bureau for Economic Policy Analysis.
    225. Aparicio, Diego & Bertolotto, Manuel I., 2020. "Forecasting inflation with online prices," International Journal of Forecasting, Elsevier, vol. 36(2), pages 232-247.
    226. Wichard, Jörg D., 2011. "Forecasting the NN5 time series with hybrid models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 700-707, July.
    227. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2021. "ExpectHill estimation, extreme risk and heavy tails," Journal of Econometrics, Elsevier, vol. 221(1), pages 97-117.
    228. Ma, Yao & Yang, Baochen & Su, Yunpeng, 2020. "Technical trading index, return predictability and idiosyncratic volatility," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 879-900.
    229. Garratt, Anthony & Mitchell, James & Vahey, Shaun P. & Wakerly, Elizabeth C., 2011. "Real-time inflation forecast densities from ensemble Phillips curves," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 77-87, January.
    230. 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.
    231. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    232. Dean Croushore, 2012. "Forecast bias in two dimensions," Working Papers 12-9, Federal Reserve Bank of Philadelphia.
    233. Viossat, Yannick & Zapechelnyuk, Andriy, 2013. "No-regret dynamics and fictitious play," Journal of Economic Theory, Elsevier, vol. 148(2), pages 825-842.
    234. Li Liu & Yudong Wang, 2021. "Forecasting aggregate market volatility: The role of good and bad uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 40-61, January.
    235. Sancetta, Alessio, 2007. "Online forecast combinations of distributions: Worst case bounds," Journal of Econometrics, Elsevier, vol. 141(2), pages 621-651, December.
    236. Fei, Tianlun & Liu, Xiaoquan & Wen, Conghua, 2019. "Cross-sectional return dispersion and volatility prediction," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    237. H.M. Anderson & H. Chan & R. Faff & Y.K. Ho, 2007. "Reported Earnings and Analyst Forecasts as Competing Sources of Information: A New Approach," ANU Working Papers in Economics and Econometrics 2007-488, Australian National University, College of Business and Economics, School of Economics.
    238. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    239. 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.
    240. Kakuho Furukawa & Ryohei Hisano & Yukio Minoura & Tomoyuki Yagi, 2022. "A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches," Bank of Japan Working Paper Series 22-E-16, Bank of Japan.
    241. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    242. Nucera, Federico & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2016. "The information in systemic risk rankings," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 461-475.
    243. Carlo Altavilla & Matteo Ciccarelli, 2006. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro Area," Discussion Papers 7_2006, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy.
    244. Narayan Kundan Kishor, 2021. "Forecasting real‐time economic activity using house prices and credit conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 213-227, March.
    245. Menkhoff, Lukas & Sakha, Sahra, 2017. "Estimating risky behavior with multiple-item risk measures," Journal of Economic Psychology, Elsevier, vol. 59(C), pages 59-86.
    246. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    247. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    248. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    249. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    250. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    251. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
    252. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    253. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    254. Konstantin Kholodilin, 2014. "Business confidence and forecasting of housing prices and rents in large German cities," ERSA conference papers ersa14p9, European Regional Science Association.
    255. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    256. Pedersen, Michael, 2019. "Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1100-1107.
    257. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    258. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    259. Álvarez, Luis J. & Sánchez, Isabel, 2019. "Inflation projections for monetary policy decision making," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 568-585.
    260. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Papers 2006.11265, arXiv.org, revised Sep 2020.
    261. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    262. Costantini, Mauro & Gunter, Ulrich & Kunst, Robert M., 2012. "Forecast Combination Based on Multiple Encompassing Tests in a Macroeconomic DSGE-VAR System," Economics Series 292, Institute for Advanced Studies.
    263. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
    264. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    265. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    266. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    267. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    268. Maria Lycheva & Alexey Mironenkov & Alexey Kurbatskii & Dean Fantazzini, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 28-49.
    269. Lars Lien Ankile & Kjartan Krange, 2022. "Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation," Papers 2201.00426, arXiv.org, revised Nov 2022.
    270. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    271. Schrimpf, Andreas & Wang, Qingwei, 2010. "A reappraisal of the leading indicator properties of the yield curve under structural instability," International Journal of Forecasting, Elsevier, vol. 26(4), pages 836-857, October.
    272. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    273. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    274. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
    275. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
    276. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    277. Roman S. Leukhin, 2019. "Short-Term Fiscal Projections Using Forecast Combination Approach," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 9-21, June.
    278. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    279. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    280. Sancetta, Alessio, 2013. "Weak conditions for shrinking multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 285-300.
    281. Sánchez, Ismael, 2008. "Adaptive combination of forecasts with application to wind energy," International Journal of Forecasting, Elsevier, vol. 24(4), pages 679-693.
    282. David Bolder & Yuliya Romanyuk, 2008. "Combining Canadian Interest-Rate Forecasts," Staff Working Papers 08-34, Bank of Canada.
    283. 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.
    284. Aiolfi Marco & Capistrán Carlos & Timmermann Allan, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
    285. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    286. Zhu, Min, 2013. "Return distribution predictability and its implications for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 209-223.
    287. Markiewicz, Agnieszka & Pick, Andreas, 2014. "Adaptive learning and survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 685-707.
    288. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
    289. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021. "Smooth Robust Multi-Horizon Forecasts," Economics Papers 2021-W01, Economics Group, Nuffield College, University of Oxford.
    290. Baumann, Ursel & Darracq Pariès, Matthieu & Westermann, Thomas & Riggi, Marianna & Bobeica, Elena & Meyler, Aidan & Böninghausen, Benjamin & Fritzer, Friedrich & Trezzi, Riccardo & Jonckheere, Jana & , 2021. "Inflation expectations and their role in Eurosystem forecasting," Occasional Paper Series 264, European Central Bank.
    291. Melo, Luis F. & Loaiza, Rubén A. & Villamizar-Villegas, Mauricio, 2016. "Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates," Economic Systems, Elsevier, vol. 40(3), pages 387-397.
    292. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    293. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    294. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2021. "FX market volatility modelling: Can we use low-frequency data?," Finance Research Letters, Elsevier, vol. 40(C).
    295. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    296. Afees A. Salisu & Ibrahim D. Raheem & Umar B. Ndako, 2017. "A sectoral analysis of asymmetric nexus between oil and stock," Working Papers 033, Centre for Econometric and Allied Research, University of Ibadan.
    297. Halbleib, Roxana & Pohlmeier, Winfried, 2012. "Improving the value at risk forecasts: Theory and evidence from the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1212-1228.
    298. Schrimpf, Andreas, 2010. "International stock return predictability under model uncertainty," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1256-1282, November.
    299. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    300. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    301. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    302. 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.
    303. Massimiliano Giacalone, 2022. "Optimal forecasting accuracy using Lp-norm combination," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 187-230, August.
    304. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    305. Dress, Korbinian & Lessmann, Stefan & von Mettenheim, Hans-Jörg, 2018. "Residual value forecasting using asymmetric cost functions," International Journal of Forecasting, Elsevier, vol. 34(4), pages 551-565.
    306. Furukawa, Kakuho & Hisano, Ryohei & Minoura, Yukio & Yagi, Tomoyuki, 2024. "A nowcasting model of industrial production using alternative data and machine learning approaches," Japan and the World Economy, Elsevier, vol. 71(C).
    307. 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.
    308. Aristidou, Chrystalleni & Lee, Kevin & Shields, Kalvinder, 2022. "Fundamentals, regimes and exchange rate forecasts: Insights from a meta exchange rate model," Journal of International Money and Finance, Elsevier, vol. 123(C).
    309. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2010. "Evaluating Macroeconomic Forecasts: A Review of Some Recent Developments," Working Papers in Economics 10/09, University of Canterbury, Department of Economics and Finance.
    310. Andreini, Paolo & Hasenzagl, Thomas & Reichlin, Lucrezia & Senftleben-König, Charlotte & Strohsal, Till, 2023. "Nowcasting German GDP: Foreign factors, financial markets, and model averaging," International Journal of Forecasting, Elsevier, vol. 39(1), pages 298-313.
    311. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    312. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. van Dijk & Marno Verbeek, 2009. "Forecast accuracy and economic gains from Bayesian model averaging using time varying weight," Working Paper 2009/10, Norges Bank.
    313. Chan, Felix & Pauwels, Laurent L., 2018. "Some theoretical results on forecast combinations," International Journal of Forecasting, Elsevier, vol. 34(1), pages 64-74.
    314. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    315. Filipa Fernandes & Charalampos Stasinakis & Zivile Zekaite, 2019. "Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery," Annals of Operations Research, Springer, vol. 282(1), pages 87-118, November.
    316. 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.
    317. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    318. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    319. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.
    320. Karsten R. Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2009. "Evaluating ensemble density combination - forecasting GDP and inflation," Working Paper 2009/19, Norges Bank.
    321. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    322. Elena Andreou & Eric Ghysels & Constantinos Kourouyiannis, 2012. "Robust volatility forecasts in the presence of structural breaks," University of Cyprus Working Papers in Economics 08-2012, University of Cyprus Department of Economics.
    323. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
    324. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
    325. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    326. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    327. 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.
    328. Dahlquist, Magnus & Hasseltoft, Henrik, 2020. "Economic momentum and currency returns," Journal of Financial Economics, Elsevier, vol. 136(1), pages 152-167.
    329. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    330. Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024. "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 123-135.
    331. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
    332. Darvas, Zsolt & Schepp, Zoltán, 2024. "Exchange rates and fundamentals: Forecasting with long maturity forward rates," Journal of International Money and Finance, Elsevier, vol. 143(C).
    333. Gao, Huicai & Li, Hengyun & Zhang, Chen Jason, 2025. "Time and feature varying tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 112(C).
    334. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    335. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    336. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    337. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    338. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    339. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    340. Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
    341. David E. Rapach & Jack K. Strauss, 2007. "Forecasting real housing price growth in the Eighth District states," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 33-42.
    342. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
    343. Rapach, David E. & Strauss, Jack K., 2009. "Differences in housing price forecastability across US states," International Journal of Forecasting, Elsevier, vol. 25(2), pages 351-372.
    344. 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.
    345. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    346. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    347. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    348. Anwen Yin, 2024. "Predictive model averaging with parameter instability and heteroskedasticity," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 418-442, April.
    349. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
    350. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
    351. Hsiao, Cheng & Wan, Shui Ki, 2011. "Comparison of forecasting methods with an application to predicting excess equity premium," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1235-1246.
    352. Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
    353. Khoo, Zhi De & Ng, Kok Haur & Koh, You Beng & Ng, Kooi Huat, 2024. "Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    354. Chiang, Wen-Chyuan & Russell, Robert A. & Urban, Timothy L., 2011. "Forecasting ridership for a metropolitan transit authority," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 696-705, August.
    355. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2024. "Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1275-1301.
    356. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    357. Valentina Corradi & Sainan Jin & Norman R. Swanson, 2023. "Robust forecast superiority testing with an application to assessing pools of expert forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 596-622, June.
    358. Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    359. Li, Jiahan & Tsiakas, Ilias, 2017. "Equity premium prediction: The role of economic and statistical constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 56-75.
    360. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    361. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    362. 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.
    363. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    364. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    365. Raval, Devesh & Rosenbaum, Ted & Wilson, Nathan E., 2021. "How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments," Journal of Health Economics, Elsevier, vol. 78(C).
    366. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).
    367. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    368. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    369. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    370. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    371. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
    372. 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.
    373. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    374. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    375. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    376. Anthony Garratt & James Mitchell & Shaun P. Vahey, 2009. "Measuring output gap uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2009/15, Reserve Bank of New Zealand.
    377. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    378. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    379. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    380. Michiel de Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," Working Paper 2010/01, Norges Bank.
    381. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    382. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    383. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    384. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    385. Li, Gang & Wu, Doris Chenguang & Zhou, Menglin & Liu, Anyu, 2019. "The combination of interval forecasts in tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 363-378.
    386. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    387. Dominik Wolff & Ulrich Neugebauer, 2019. "Tree-based machine learning approaches for equity market predictions," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 273-288, July.
    388. 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.
    389. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    390. Konstantin A. Kholodilin & Boriss Siliverstovs, 2015. "Think national, forecast local: A case study of 71 German urban housing markets," KOF Working papers 15-372, KOF Swiss Economic Institute, ETH Zurich.
    391. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    392. Travis J. Berge, 2013. "Predicting recessions with leading indicators: model averaging and selection over the business cycle," Research Working Paper RWP 13-05, Federal Reserve Bank of Kansas City.
    393. Møller, Jan Kloppenborg & Nystrup, Peter & Madsen, Henrik, 2024. "Likelihood-based inference in temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 40(2), pages 515-531.
    394. Jardet, Caroline & Monfort, Alain & Pegoraro, Fulvio, 2013. "No-arbitrage Near-Cointegrated VAR(p) term structure models, term premia and GDP growth," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 389-402.
    395. Tsiakas, Ilias & Li, Jiahan & Zhang, Haibin, 2020. "Equity premium prediction and the state of the economy," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 75-95.
    396. António Rua & Paulo Esteves, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    397. Martinsen, Kjetil & Ravazzolo, Francesco & Wulfsberg, Fredrik, 2014. "Forecasting macroeconomic variables using disaggregate survey data," International Journal of Forecasting, Elsevier, vol. 30(1), pages 65-77.
    398. Lansing, Kevin J., 2021. "Endogenous forecast switching near the zero lower bound," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 153-169.
    399. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
    400. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    401. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    402. Kolasa, Marcin & Rubaszek, Michał, 2015. "Forecasting using DSGE models with financial frictions," International Journal of Forecasting, Elsevier, vol. 31(1), pages 1-19.
    403. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
    404. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    405. De Rezende, Rafael B., 2017. "The interest rate effects of government bond purchases away from the lower bound," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 165-186.
    406. Andrew B. Martinez & Alexander D. Schibuola & David Beckworth, 2025. "The Reliability of the Nominal GDP Expectations Gap," Working Papers 2025-004, The George Washington University, The Center for Economic Research.
    407. Huurman, Christian & Ravazzolo, Francesco & Zhou, Chen, 2012. "The power of weather," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3793-3807.
    408. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, EconSciences Journals, vol. 3(3), pages 536-559, September.

  5. López Moctezuma Gabriel & Capistrán Carlos, 2010. "Forecast Revisions of Mexican Inflation and GDP Growth," Working Papers 2010-11, Banco de México.

    Cited by:

    1. Etienne Gagnon & David López-Salido & Nicolas Vincent, 2012. "Individual Price Adjustment along the Extensive Margin," NBER Working Papers 18213, National Bureau of Economic Research, Inc.
    2. Mehrotra, Aaron & Yetman, James, 2018. "Are inflation targets credible? A novel test," Economics Letters, Elsevier, vol. 167(C), pages 67-70.
    3. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    4. 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.
    5. de Mendonça, Helder Ferreira & de Deus, Joseph David Barroso Vasconcelos, 2019. "Central bank forecasts and private expectations: An empirical assessment from three emerging economies," Economic Modelling, Elsevier, vol. 83(C), pages 234-244.

  6. Capistrán Carlos & Constandse Christian & Ramos Francia Manuel, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.

    Cited by:

    1. Guerrero Santiago & Juárez-Torres Miriam & Sámano Daniel & Kochen Federico & Puigvert Jonathan, 2016. "Price Transmission in Food and Non-Food Product Markets: Evidence from Mexico," Working Papers 2016-18, Banco de México.
    2. Jaramillo Rodríguez Jorge & Pech Moreno Luis Alberto & Ramírez Claudia & Sanchez-Amador David, 2019. "Nonlinear Exchange Rate Pass-Through in Mexico," Working Papers 2019-16, Banco de México.
    3. Sidaoui José Julián & Capistrán Carlos & Chiquiar Daniel & Ramos Francia Manuel, 2009. "A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico," Working Papers 2009-14, Banco de México.
    4. Ibarra-Ramírez Raúl, 2010. "Forecasting Inflation in Mexico Using Factor Models: Do Disaggregated CPI Data Improve Forecast Accuracy?," Working Papers 2010-01, Banco de México.

  7. Sidaoui José Julián & Capistrán Carlos & Chiquiar Daniel & Ramos Francia Manuel, 2009. "A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico," Working Papers 2009-14, Banco de México.

    Cited by:

    1. Guerrero Santiago & Juárez-Torres Miriam & Sámano Daniel & Kochen Federico & Puigvert Jonathan, 2016. "Price Transmission in Food and Non-Food Product Markets: Evidence from Mexico," Working Papers 2016-18, Banco de México.
    2. Muhammad, Shahbaz & Kumar, A.T.K. & Mohammad, Iqbal Tahir, 2012. "Does CPI Granger-Cause WPI? New Extensions from Frequency Domain Approach in Pakistan," MPRA Paper 38816, University Library of Munich, Germany, revised 14 May 2012.
    3. Tiwari, Aviral & Shahbaz, Muhammad, 2010. "Modelling the Relationship between Whole Sale Price and Consumer Price Indices: Cointegration and Causality Analysis for India," MPRA Paper 27333, University Library of Munich, Germany.
    4. Ramon Moreno, 2010. "Some issues in measuring and tracking prices in emerging market exonomies," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 13-51, Bank for International Settlements.
    5. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
    6. Ülke, Volkan & Ergun, Ugur, 2013. "The Relationship between Consumer Price and Producer Price Indices in Turkey," MPRA Paper 59437, University Library of Munich, Germany.
    7. Jing Sun & Jinhui Xu & Xin Cheng & Jichao Miao & Hairong Mu, 2023. "Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1279-1289, April.
    8. Tiwari, Aviral Kumar & Suresh K.G., & Arouri, Mohamed & Teulon, Frédéric, 2014. "Causality between consumer price and producer price: Evidence from Mexico," Economic Modelling, Elsevier, vol. 36(C), pages 432-440.
    9. Yusuf V. Topuz & Hassan Yazdifar & Sunil Sahadev, 2018. "The relation between the producer and consumer price indices: a two-country study," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 122-130, June.
    10. Ivo da Rocha Lima Filho, Roberto, 2019. "Does PPI lead CPI IN Brazil?," International Journal of Production Economics, Elsevier, vol. 214(C), pages 73-79.

  8. 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.

    Cited by:

    1. 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.
    2. Hernandez, Manuel A. & Ibarra, Raul & Trupkin, Danilo R., 2011. "How far do shocks move across borders?: Examining volatility transmission in major agricultural futures markets," IFPRI discussion papers 1109, International Food Policy Research Institute (IFPRI).
    3. Haixia, Wu & Shiping, Li, 2013. "Volatility spillovers in China’s crude oil, corn and fuel ethanol markets," Energy Policy, Elsevier, vol. 62(C), pages 878-886.
    4. Capraro Rodríguez Santiago & Perrotini Hernández Ignacio, 2012. "Intervenciones cambiarias esterilizadas, teoría y evidencia:el caso de México," Contaduría y Administración, Accounting and Management, vol. 57(2), pages 11-44, abril-jun.

  9. Benavides Guillermo & Capistrán Carlos, 2009. "Forecasting Exchange Rate Volatility: The Superior Performance of Conditional Combinations of Time Series and Option Implied Forecasts," Working Papers 2009-01, Banco de México.

    Cited by:

    1. Luo, Xingguo & Ye, Zinan, 2015. "Predicting volatility of the Shanghai silver futures market: What is the role of the U.S. options market?," Finance Research Letters, Elsevier, vol. 15(C), pages 68-77.
    2. López Noria Gabriela & Bush Georgia, 2019. "Uncertainty and Exchange Rate Volatility: the Case of Mexico," Working Papers 2019-12, Banco de México.
    3. Holger Fink & Andreas Fuest & Henry Port, 2018. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates," Risks, MDPI, vol. 6(3), pages 1-19, August.
    4. Abarca Gustavo & Rangel José Gonzalo & Benavides Guillermo, 2010. "Exchange Rate Market Expectations and Central Bank Policy: The case of the Mexican Peso-US Dollar from 2005-2009," Working Papers 2010-17, Banco de México.
    5. Gozgor, Giray & Nokay, Pinar, 2011. "Comparing forecast performances among volatility estimation methods in the pricing of european type currency options of USD-TL and Euro-TL," MPRA Paper 34369, University Library of Munich, Germany.
    6. 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.
    7. 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.
    8. Slim, Skander & Dahmene, Meriam & Boughrara, Adel, 2020. "How informative are variance risk premium and implied volatility for Value-at-Risk prediction? International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 22-37.
    9. Bush, Georgia & López Noria, Gabriela, 2021. "Uncertainty and exchange rate volatility: Evidence from Mexico," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 704-722.
    10. 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.
    11. Gustavo Cabrera González, 2019. "Modeling and Projection of the Mexican Exchange Rate (Peso/Dollar): a Bayesian Approach for Model Selection," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 203-219, Abril-Jun.
    12. Salisu, Afees A. & Cuñado, Juncal & Gupta, Rangan, 2022. "Geopolitical risks and historical exchange rate volatility of the BRICS," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 179-190.
    13. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    14. Guillermo Benavides, 2011. "Central Bank Exchange Rate Interventions and Market Expectations: The Case of México During the Financial Crisis 2008-2009," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 6(1), pages 5-27, Julio-Dic.

  10. Carlos Capistrán & Allan Timmermann, 2008. "Disagreement and Biases in Inflation Expectations," CREATES Research Papers 2008-56, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Bernd Hayo & Florian Neumeier, 2018. "Households’ Inflation Perceptions and Expectations: Survey Evidence from New Zealand," ifo Working Paper Series 255, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    3. Chia-Lin Chang & Bert de Bruijn & Philip Hans Franses & Michael McAleer, 2013. "Analyzing Fixed-event Forecast Revisions," Documentos de Trabajo del ICAE 2013-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Apr 2013.
    4. Ahrens, Steffen & Lustenhouwer, Joep & Tettamanzi, Michele, 2017. "The Stabilizing Role of Forward Guidance: A Macro Experiment," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168063, Verein für Socialpolitik / German Economic Association.
    5. Mankiw, N. Gregory & Reis, Ricardo, 2002. "Sticky Information Versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," Scholarly Articles 3415324, Harvard University Department of Economics.
    6. Carlos Capistrán & Manuel Ramos‐Francia, 2010. "Does Inflation Targeting Affect the Dispersion of Inflation Expectations?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 113-134, February.
    7. Pedersen, Michael, 2015. "What affects the predictions of private forecasters? The role of central bank forecasts in Chile," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1043-1055.
    8. Olivier Coibion & Yuriy Gorodnichenko & Rupal Kamdar, 2017. "The Formation of Expectations, Inflation and the Phillips Curve," NBER Working Papers 23304, National Bureau of Economic Research, Inc.
    9. Ehrmann, Michael, 2021. "Point targets, tolerance bands or target ranges? Inflation target types and the anchoring of inflation expectations," Journal of International Economics, Elsevier, vol. 132(C).
    10. James M. Nason & Gregor W. Smith, 2013. "Measuring The Slowly Evolving Trend In Us Inflation With Professional Forecasts," Working Paper 1316, Economics Department, Queen's University.
    11. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    12. Gross, Marco, 2009. "Nonparametric Hybrid Phillips Curves Based on Subjective Expectations: Estimates for the Euro Area," Working Paper Series 1119, European Central Bank.
    13. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric loss functions and the rationality of expected stock returns," International Journal of Forecasting, Elsevier, vol. 27(2), pages 413-437.
    14. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    15. Yoshiyuki Nakazono, 2016. "Inflation expectations and monetary policy under disagreements," Bank of Japan Working Paper Series 16-E-1, Bank of Japan.
    16. Andrey Duván Rincón-Torres & Andrés Felipe Salas-Avila & Juan Manuel Julio-Román, 2023. "Inflation Expectations: Rationality, Disagreement and the Role of the Loss Function in Colombia," Borradores de Economia 1262, Banco de la Republica de Colombia.
    17. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    18. Carrera, César, 2012. "Estimating Information Rigidity using Firms’ Survey Data," Working Papers 2012-004, Banco Central de Reserva del Perú.
    19. Clements, Michael P., 2019. "Do forecasters target first or later releases of national accounts data?," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1240-1249.
    20. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    21. Franses, Ph.H.B.F. & Welz, M., 2020. "Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?," Econometric Institute Research Papers EI-1687, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    22. Lena Dräger & Michael J. Lamla, 2024. "Consumers' macroeconomic expectations," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 427-451, April.
    23. Wändi Bruine de Bruin & Michael F. Bryan & Simon M. Potter & Giorgio Topa & Wilbert Van der Klaauw, 2008. "Rethinking the measurement of household inflation expectations: preliminary findings," Staff Reports 359, Federal Reserve Bank of New York.
    24. William A. Branch, 2014. "Nowcasting and the Taylor Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(5), pages 1035-1055, August.
    25. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    26. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    27. Jonathan Benchimol & Makram El-Shagi & Yossi Saadon, 2022. "Do expert experience and characteristics affect inflation forecasts?," Post-Print emse-04624966, HAL.
    28. Wilbert van der Klaauw & Wandi Bruine de Bruin & Giorgio Topa & Basit Zafar & Olivier Armantier, 2012. "Inflation Expectations and Behavior: Do Survey Respondents Act on their Beliefs?," 2012 Meeting Papers 121, Society for Economic Dynamics.
    29. Sebastiano Manzan, 2011. "Differential Interpretation in the Survey of Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(5), pages 993-1017, August.
    30. Ehrmann, Michael & Hubert, Paul, 2023. "Information acquisition ahead of monetary policy announcements," Working Paper Series 2770, European Central Bank.
    31. Luis Gil-Alana & Antonio Moreno & Fernando Pérez de Gracia, 2011. "Exploring Survey-Based Inflation Forecasts," Faculty Working Papers 05/11, School of Economics and Business Administration, University of Navarra.
    32. Monique Reid & Pierre Siklos, 2024. "Firm level expectations and macroeconomic conditions underpinnings and disagreement," Working Papers 11058, South African Reserve Bank.
    33. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    34. Maxime Phillot & Rina Rosenblatt-Wisch, 2018. "Inflation Expectations: The Effect of Question Ordering on Forecast Inconsistencies," Working Papers 2018-11, Swiss National Bank.
    35. Cooper, Michael & Gulen, Huseyin & Ion, Mihai, 2024. "The use of asset growth in empirical asset pricing models," Journal of Financial Economics, Elsevier, vol. 151(C).
    36. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    37. Montes, Gabriel Caldas & Nicolay, Rodolfo Tomás da Fonseca & Acar, Tatiana, 2019. "Do fiscal communication and clarity of fiscal announcements affect public debt uncertainty? Evidence from Brazil," Journal of Economics and Business, Elsevier, vol. 103(C), pages 38-60.
    38. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    39. Pierre L. Siklos, 2016. "Forecast Disagreement and the Inflation Outlook: New International Evidence," IMES Discussion Paper Series 16-E-03, Institute for Monetary and Economic Studies, Bank of Japan.
    40. Byeongdeuk Jang & Young Se Kim, 2017. "Driving Forces of Inflation Expectations," Korean Economic Review, Korean Economic Association, vol. 33, pages 207-237.
    41. Conrad, Christian, 2017. "When does information on forecast variance improve the performance of a combined forecast?," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168200, Verein für Socialpolitik / German Economic Association.
    42. Fabiana Gomez & David Pacini, 2015. "Counting Biased Forecasters: An Application of Multiple Testing Techniques," Bristol Economics Discussion Papers 15/661, School of Economics, University of Bristol, UK.
    43. Nathan Goldstein & Ben‐Zion Zilberfarb, 2023. "The closer we get, the better we are?," Economic Inquiry, Western Economic Association International, vol. 61(2), pages 364-376, April.
    44. Volker Wieland, 2012. "Model comparison and robustness: a proposal for policy analysis after the financial crisis," Chapters, in: Robert M. Solow & Jean-Philippe Touffut (ed.), What’s Right with Macroeconomics?, chapter 2, pages 33-67, Edward Elgar Publishing.
    45. Bodo Herzog, 2015. "Anchoring of expectations: The role of credible targets in a game experiment," Journal of Economic and Financial Studies (JEFS), LAR Center Press, vol. 3(6), pages 1-15, December.
    46. Fernandes, Cecilia Melo, 2021. "ECB communication as a stabilization and coordination device: evidence from ex-ante inflation uncertainty," Working Paper Series 2582, European Central Bank.
    47. Hartwell, Christopher A & Szybisz, Martin Andres, 2021. "Corralling Expectations: The Role of Institutions in (Hyper)Inflation," MPRA Paper 105612, University Library of Munich, Germany.
    48. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, The Center for Economic Research.
    49. Imane El Ouadghiri & Remzi Uctum, 2020. "Macroeconomic expectations and time varying heterogeneity: Evidence from individual survey data," Post-Print hal-03319091, HAL.
    50. Jia, Pengfei & Shen, Haopeng & Zheng, Shikun, 2023. "Monetary policy rules and opinionated markets," Economics Letters, Elsevier, vol. 223(C).
    51. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    52. Siklos, Pierre, 2017. "What Has Publishing Inflation Forecasts Accomplished? Central Banks And Their Competitors," LCERPA Working Papers 0098, Laurier Centre for Economic Research and Policy Analysis, revised 01 Apr 2017.
    53. Acedański, Jan, 2017. "Heterogeneous expectations and the distribution of wealth," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 162-175.
    54. Doina Chichernea & Kershen Huang & Alex Petkevich, 2019. "Does maturity matter? The case of treasury futures volume," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(10), pages 1301-1321, October.
    55. Taro Ikeda, 2012. "Three Essays on Robustness and Asymmetries in Central Bank Forecasting," Discussion Papers 1216, Graduate School of Economics, Kobe University.
    56. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    57. Montes, Gabriel Caldas & Luna, Paulo Henrique, 2018. "Discretionary fiscal policy and disagreement in expectations about fiscal variables empirical evidence from Brazil," Economic Modelling, Elsevier, vol. 73(C), pages 100-116.
    58. Alexandros Botsis & Christoph Gortz & Plutarchos Sakellaris, 2023. "Quantifying Qualitative Survey Data: New Insights on the (Ir)Rationality of Firms' Forecasts," Discussion Papers 23-06, Department of Economics, University of Birmingham.
    59. Siklos, Pierre L., 2013. "Sources of disagreement in inflation forecasts: An international empirical investigation," Journal of International Economics, Elsevier, vol. 90(1), pages 218-231.
    60. G. C. Montes & L. V. Oliveira & A. Curi & R. T. F. Nicolay, 2016. "Effects of transparency, monetary policy signalling and clarity of central bank communication on disagreement about inflation expectations," Applied Economics, Taylor & Francis Journals, vol. 48(7), pages 590-607, February.
    61. Hartmann, Matthias & Conrad, Christian, 2014. "Cross sectional evidence on the relation between monetary policy, macroeconomic conditions and low-frequency inflation uncertainty," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100477, Verein für Socialpolitik / German Economic Association.
    62. Devereux, Michael B. & Smith, Gregor W. & Yetman, James, 2012. "Consumption and real exchange rates in professional forecasts," Journal of International Economics, Elsevier, vol. 86(1), pages 33-42.
    63. Ikeda, Taro, 2014. "Asymmetric preferences in real-time learning and the Taylor rule," Economics Letters, Elsevier, vol. 124(3), pages 487-489.
    64. Ruttachai Seelajaroen & Pornanong Budsaratragoon & Boonlert Jitmaneeroj, 2020. "Do monetary policy transparency and central bank communication reduce interest rate disagreement?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 368-393, April.
    65. Menz, Jan-Oliver & Poppitz, Philipp, 2013. "Households' disagreement on inflation expectations and socioeconomic media exposure in Germany," Discussion Papers 27/2013, Deutsche Bundesbank.
    66. Tomasz Łyziak & Xuguang Simon Sheng, 2023. "Disagreement in Consumer Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2215-2241, December.
    67. Jaylson Jair da Silveira & Gilberto Tadeu Lima, 2014. "Heterogeneity in Inflation Expectations and Macroeconomic Stability under Satisficing Learning," Working Papers, Department of Economics 2014_28, University of São Paulo (FEA-USP).
    68. Ahrens, Steffen & Lustenhouwer, Joep & Tettamanzi, Michele, 2023. "The Stabilizing Effects of Publishing Strategic Central Bank Projections," Macroeconomic Dynamics, Cambridge University Press, vol. 27(3), pages 826-868, April.
    69. Chen, Heng & Li, Xu & Pei, Guangyu & Xin, Qian, 2024. "Heterogeneous overreaction in expectation formation: Evidence and theory," Journal of Economic Theory, Elsevier, vol. 218(C).
    70. Maurizio Bovi & Roy Cerqueti, 2016. "Forecasting macroeconomic fundamentals in economic crises," Annals of Operations Research, Springer, vol. 247(2), pages 451-469, December.
    71. Wieland, Volker & Wolters, Maik H., 2010. "The diversity of forecasts from macroeconomic models of the U.S. economy," CFS Working Paper Series 2010/08, Center for Financial Studies (CFS).
    72. Volha Audzei, 2016. "Confidence Cycles and Liquidity Hoarding," Working Papers 2016/07, Czech National Bank, Research and Statistics Department.
    73. Capistrán Carlos, 2006. "Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious?," Working Papers 2006-14, Banco de México.
    74. Sheng, Xuguang (Simon), 2015. "Evaluating the economic forecasts of FOMC members," International Journal of Forecasting, Elsevier, vol. 31(1), pages 165-175.
    75. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
    76. Conrad, Christian & Lahiri, Kajal, 2024. "Heterogeneous Expectations among Professional Forecasters," Working Papers 0754, University of Heidelberg, Department of Economics.
    77. Maurizio Bovi, 2014. "Shocks and the Expectations Formation Process. A Tale of Two Expectations," Natural Field Experiments 00390, The Field Experiments Website.
    78. 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.
    79. Philip Hans Franses, 2020. "Correcting the January optimism effect," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 927-933, September.
    80. Andrea Fracasso & Rocco Probo, 2016. "When did inflation expectations in the euro area de-anchor?," DEM Working Papers 2016/05, Department of Economics and Management.
    81. Man-Keung Tang & Mr. Xiangrong Yu, 2011. "Communication of Central Bank Thinking and Inflation Dynamics," IMF Working Papers 2011/209, International Monetary Fund.
    82. Anmol Bhandari & Jaroslav Borovicka & Paul Ho, 2019. "Survey Data and Subjective Beliefs in Business Cycle Models," Working Paper 19-14, Federal Reserve Bank of Richmond.
    83. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465, April.
    84. Gregor W. Smith & James Yetman, 2013. "Estimating Dynamic Euler Equations With Multivariate Professional Forecasts," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 445-458, January.
    85. Clements, Michael P, 2006. "Internal consistency of survey respondents.forecasts : Evidence based on the Survey of Professional Forecasters," The Warwick Economics Research Paper Series (TWERPS) 772, University of Warwick, Department of Economics.
    86. Gabriele Galati & Peter Heemeijer & Richhild Moessner, 2011. "How do inflation expectations form? New insights from a high-frequency survey," BIS Working Papers 349, Bank for International Settlements.
    87. Gobbi, Lucio & Mazzocchi, Ronny & Tamborini, Roberto, 2025. "Inflation shocks and the New Keynesian model: When should central banks fear inflation expectations?," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
    88. Fildes, Robert, 2015. "Forecasters and rationality—A comment on Fritsche et al., Forecasting the Brazilian Real and Mexican Peso: Asymmetric loss, forecast rationality and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 140-143.
    89. Sinha, Rajesh Kumar, 2021. "Macro disagreement and analyst forecast properties," Journal of Contemporary Accounting and Economics, Elsevier, vol. 17(1).
    90. Ivana Komunjer & Michael T. Owyang, 2012. "Multivariate Forecast Evaluation and Rationality Testing," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1066-1080, November.
    91. 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.
    92. Drakos, Konstantinos & Konstantinou, Panagiotis Th. & Thoma, Foteini-Anna, 2020. "Inflation uncertainty and inflation expectations: Micro-level evidence from the eurozone," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    93. Michael P. Clements, 2018. "Do Macroforecasters Herd?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 265-292, March.
    94. Oleksiy Kryvtsov & Luba Petersen, 2019. "Central Bank Communication That Works: Lessons from Lab Experiments," Staff Working Papers 19-21, Bank of Canada.
    95. Goldfayn-Frank, Olga & Wohlfart, Johannes, 2020. "Expectation formation in a new environment: Evidence from the German reunification," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 301-320.
    96. Paul Hubert & Becky Maule, 2016. "Policy and macro signals as inputs to inflation expectation formation," Bank of England working papers 581, Bank of England.
    97. Robert G Murphy & Adam Rohde, 2018. "Rational Bias in Inflation Expectations," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 44(1), pages 153-171, January.
    98. Golden, Brian & Monks, Allen, 2009. "Measuring Inflation Expectations in the Euro Area," Quarterly Bulletin Articles, Central Bank of Ireland, pages 67-84, January.
    99. Hubert Paul, 2017. "Qualitative and quantitative central bank communication and inflation expectations," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(1), pages 1-41, January.
    100. Grothe, Magdalena & Meyler, Aidan, 2015. "Inflation forecasts: Are market-based and survey-based measures informative?," MPRA Paper 66982, University Library of Munich, Germany.
    101. Juan Camilo Galvis-Ciro & Juan Camilo Anzoátegui-Zapata & Cristina Isabel Ramos-Barroso, 2022. "The Effect of Communication and Credibility on Fiscal Disagreement: Empirical Evidence from Colombia," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(3), pages 215-238, November.
    102. Kim, Insu & Kim, Young Se, 2019. "Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach," Journal of Macroeconomics, Elsevier, vol. 62(C).
    103. Reitz, Stefan & Stadtmann, Georg & Taylor, Mark P., 2010. "The effects of Japanese interventions on FX-forecast heterogeneity," Economics Letters, Elsevier, vol. 108(1), pages 62-64, July.
    104. Juan Camilo Anzoátegui Zapata & Juan Camilo Galvis Ciro, 2019. "Efectos de la comunicación sobre los errores de pronóstico de inflación: evidencia para Colombia para el período 2008-2016," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(4), pages 617-631, Octubre -.
    105. Junichi Kikuchi & Yoshiyuki Nakazono, 2023. "The Formation of Inflation Expectations: Microdata Evidence from Japan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(6), pages 1609-1632, September.
    106. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
    107. James M. Nason & Gregor W. Smith, 2013. "Reverse Kalman filtering U.S. inflation with sticky professional forecasts," Working Papers 13-34, Federal Reserve Bank of Philadelphia.
    108. Taro Ikeda, 2013. "Asymmetric forecasting and commitment policy in a robust control problem," Discussion Papers 1306, Graduate School of Economics, Kobe University.
    109. David A. Comerford, 2024. "Response Bias in Survey Measures of Expectations: Evidence from the Survey of Consumer Expectations’ Inflation Module," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(4), pages 933-953, June.
    110. Katharina Allinger & Fabio Rumler, 2025. "Inflation Expectations in Central, Eastern and Southeastern Europe: The Role of Sentiment and Experiences," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 67(2), pages 426-451, June.
    111. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    112. Gilberto Tadeu Lima & Mark Setterfield, Jaylson Jair da Silveira, 2013. "Inflation Targeting and Macroeconomic Stability with Heterogeneous Inflation Expectations," Working Papers, Department of Economics 2013_11, University of São Paulo (FEA-USP), revised 25 Nov 2016.
    113. Dovern, Jonas & Weisser, Johannes, 2008. "Are they really rational? Assessing professional macro-economic forecasts from the G7-countries," Kiel Working Papers 1447, Kiel Institute for the World Economy.
    114. Rianne Legerstee & Philip Hans Franses, 2010. "Does Disagreement amongst Forecasters have Predictive Value?," Tinbergen Institute Discussion Papers 10-088/4, Tinbergen Institute.
    115. Thomas Maag & Michael J. Lamla, 2009. "The role of media for inflation forecast disagreement of households and professional forecasters," KOF Working papers 09-223, KOF Swiss Economic Institute, ETH Zurich.
    116. Carola Binder & Wesley Janson & Randal Verbrugge, 2023. "Out of Bounds: Do SPF Respondents Have Anchored Inflation Expectations?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 559-576, March.
    117. Boris Radovanov & Aleksandra Marcikic, 2011. "Uncertainty And Disagreement In Inflation Forecasting," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 20(1), pages 3-18, june.
    118. Taro Ikeda, 2017. "Asymmetric Preferences and the Stability Problem for Optimal Monetary Policy Rules," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(8), pages 1831-1838, December.
    119. Gaurav Kumar Singh & Tathagata Bandyopadhyay, 2024. "Determinants of disagreement: Learning from inflation expectations survey of households," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 326-343, March.
    120. Young Se Kim & Byeongdeuk Jang, 2015. "Dispersion of Inflation Expectations: Stylized Facts, Puzzles, and Macroeconomic Implications," Korean Economic Review, Korean Economic Association, vol. 31, pages 89-119.
    121. Patrick Hirsch & Lars P. Feld & Ekkehard A. Köhler, 2023. "Breaking Monetary Policy News: The Role of Mass Media Coverage of ECB Announcements for Public Inflation Expectations," CESifo Working Paper Series 10285, CESifo.
    122. Philip Hans Franses & Max Welz, 2022. "Evaluating heterogeneous forecasts for vintages of macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 829-839, July.
    123. Li, You & Tay, Anthony, 2021. "The role of macroeconomic and policy uncertainty in density forecast dispersion," Journal of Macroeconomics, Elsevier, vol. 67(C).
    124. Paul Hubert, 2014. "Disentangling qualitative and quantitative central bank influence," Sciences Po Economics Publications (main) hal-01098464, HAL.
    125. Bovi, Maurizio, 2013. "Are the representative agent’s beliefs based on efficient econometric models?," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 633-648.
    126. Bovi, Maurizio, 2019. "A Time-Varying Expectations Formation Mechanism," MPRA Paper 97624, University Library of Munich, Germany.
    127. Jitmaneeroj, Boonlert & Lamla, Michael J. & Wood, Andrew, 2019. "The implications of central bank transparency for uncertainty and disagreement," Journal of International Money and Finance, Elsevier, vol. 90(C), pages 222-240.
    128. Bürgi, Constantin, 2017. "Bias, rationality and asymmetric loss functions," Economics Letters, Elsevier, vol. 154(C), pages 113-116.
    129. Gerotto, Luca & Paradiso, Antonio & Pellizzari, Paolo, 2025. "A tale of inattentiveness and the loss function: A model for household-level macroeconomic expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).
    130. Franses, Ph.H.B.F., 2019. "Professional Forecasters and January," Econometric Institute Research Papers EI2019-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    131. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    132. Paul Hubert, 2010. "Monetary policy, imperfect information and the expectations channel [Politique monétaire,information imparfaite et canal des anticipations]," Sciences Po Economics Publications (main) tel-04095385, HAL.
    133. Adam, Klaus & Matveev, Dmitry & Nagel, Stefan, 2021. "Do survey expectations of stock returns reflect risk adjustments?," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 723-740.
    134. Tae-Hwy Lee & Yiyao Wang, 2019. "Evaluation of the Survey of Professional Forecasters in the Greenbook’s Loss Function," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 345-360, June.
    135. Katharina Allinger & Fabio Rumler, 2023. "Inflation Expectations in CESEE: The Role of Sentiment and Experiences (Katharina Allinger, Fabio Rumler)," Working Papers 247, Oesterreichische Nationalbank (Austrian Central Bank).
    136. Ehrmann, M. & Eijffinger, S.C.W. & Fratzcher, M., 2010. "The Role of Central Bank Transparency for Guiding Private Sector Forecasts," Other publications TiSEM 25125044-98fc-44b3-8698-3, Tilburg University, School of Economics and Management.
    137. Henning Fischer & Marta García-Bárzana & Peter Tillmann & Peter Winker, 2012. "Evaluating FOMC forecast ranges: an interval data approach," MAGKS Papers on Economics 201213, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    138. Félix, Luiz & Kräussl, Roman & Stork, Philip, 2018. "Predictable biases in macroeconomic forecasts and their impact across asset classes," CFS Working Paper Series 596, Center for Financial Studies (CFS).
    139. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2013. "A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(3), pages 294-301.
    140. David Gbaguidi, 2012. "La courbe de Phillips : temps d’arbitrage et/ou arbitrage de temps," L'Actualité Economique, Société Canadienne de Science Economique, vol. 88(1), pages 87-119.
    141. Luiz Félix & Roman Kräussl & Philip Stork, 2021. "Strategic bias and popularity effect in the prediction of economic surprises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1095-1117, September.
    142. Ryan Banerjee & Aaron Mehrotra, 2021. "Disagreeing during Deflations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(7), pages 1867-1885, October.
    143. Gabriel Caldas Montes & Paulo Henrique Lourenço Luna, 2022. "Do fiscal opacity, fiscal impulse, and fiscal credibility affect disagreement about economic growth forecasts? Empirical evidence from Brazil considering the period of political instability and presidential impeachment," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 2356-2393, November.
    144. Michael Ehrmann, 2014. "Targeting Inflation from Below - How Do Inflation Expectations Behave?," Staff Working Papers 14-52, Bank of Canada.
    145. Rülke, Jan-Christoph & Silgoner, Maria & Wörz, Julia, 2016. "Herding behavior of business cycle forecasters," International Journal of Forecasting, Elsevier, vol. 32(1), pages 23-33.
    146. Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
    147. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    148. Anmol Bhandari & Jaroslav Borovička & Paul Ho, 2016. "Identifying Ambiguity Shocks in Business Cycle Models Using Survey Data," NBER Working Papers 22225, National Bureau of Economic Research, Inc.
    149. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.

  11. Capistrán Carlos, 2007. "Optimality Tests for Multi-Horizon Forecasts," Working Papers 2007-14, Banco de México.

    Cited by:

    1. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    2. Timmermann, Allan & Patton, Andrew, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," CEPR Discussion Papers 8194, C.E.P.R. Discussion Papers.

  12. Capistrán Carlos & Ramos Francia Manuel, 2007. "Does Inflation Targeting Affect the Dispersion of Inflation Expectations?," Working Papers 2007-11, Banco de México.

    Cited by:

    1. Masazumi Hattori & Steven Kong & Frank Packer & Toshitaka Sekine, 2016. "The effects of a central bank's inflation forecasts on private sector forecasts: Recent evidence from Japan," BIS Working Papers 585, Bank for International Settlements.
    2. Steve Brito & Mr. Yan Carriere-Swallow & Bertrand Gruss, 2018. "Disagreement about Future Inflation: Understanding the Benefits of Inflation Targeting and Transparency," IMF Working Papers 2018/024, International Monetary Fund.
    3. Pierdzioch, Christian & Rülke, Jan-Christoph, 2013. "Do inflation targets anchor inflation expectations?," Economic Modelling, Elsevier, vol. 35(C), pages 214-223.
    4. Carlos Capistrán & Manuel Ramos‐Francia, 2010. "Does Inflation Targeting Affect the Dispersion of Inflation Expectations?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 113-134, February.
    5. Huang, Ho-Chuan (River) & Yeh, Chih-Chuan, 2017. "Level, structure, and volatility of financial development and inflation targeting," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 108-124.
    6. Ehrmann, Michael, 2021. "Point targets, tolerance bands or target ranges? Inflation target types and the anchoring of inflation expectations," Journal of International Economics, Elsevier, vol. 132(C).
    7. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    8. Paul Hubert, 2011. "Central Bank Forecasts as an Instrument of Monetary Policy," Documents de Travail de l'OFCE 2011-23, Observatoire Francais des Conjonctures Economiques (OFCE).
    9. Schmidt, Sandra & Nautz, Dieter, 2010. "Why do financial market experts misperceive future monetary policy decisions?," SFB 649 Discussion Papers 2010-036, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Carrasco, Carlos A., 2013. "El Nuevo Consenso Macroeconómico y la mediocridad del crecimiento económico en México [New Consensus Macroeconomics and the mediocrity of economic growth in Mexico]," MPRA Paper 53391, University Library of Munich, Germany.
    11. Rebeca I. Muñoz Torres & David Shepherd, 2014. "Inflation Targeting and the Consistency of Monetary Policy Decisions in Mexico: an Empirical Analysis with Discrete Choice Models," Manchester School, University of Manchester, vol. 82, pages 21-46, December.
    12. Pierre-Richard Agénor & Luiz A. Pereira da Silva, 2013. "Inflation Targeting and Financial Stability: A Perspective from the Developing World," Working Papers Series 324, Central Bank of Brazil, Research Department.
    13. Ana Aguilar & Carlo Alcaraz Pribaz & Victoria Nuguer & Jessica Roldán-Peña, 2022. "Monetary policy announcements and expectations: the case of Mexico," BIS Working Papers 1026, Bank for International Settlements.
    14. Shu Lin & Haichun Ye, 2012. "What to Target? Inflation or Exchange Rate," Southern Economic Journal, John Wiley & Sons, vol. 78(4), pages 1202-1221, April.
    15. Alexander Ballantyne & Christian Gillitzer & David Jacobs & Ewan Rankin, 2016. "Disagreement about Inflation Expectations," RBA Research Discussion Papers rdp2016-02, Reserve Bank of Australia.
    16. Hartmann, Matthias & Herwartz, Helmut & Ulm, Maren, 2017. "A comparative assessment of alternative ex ante measures of inflation uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 76-89.
    17. Carrera, César, 2012. "Estimating Information Rigidity using Firms’ Survey Data," Working Papers 2012-004, Banco Central de Reserva del Perú.
    18. Monica Jain & Christopher S. Sutherland, 2018. "How Do Central Bank Projections and Forward Guidance Influence Private-Sector Forecasts?," Staff Working Papers 18-2, Bank of Canada.
    19. Paul Hubert, 2013. "ECB projections as a tool for understanding policy decisions," Documents de Travail de l'OFCE 2013-04, Observatoire Francais des Conjonctures Economiques (OFCE).
    20. Sylvia Beatriz Guillermo Peón & Mart�n Alberto Rodr�guez Brindis, 2014. "Analyzing the Exchange Rate Pass-through in Mexico: Evidence Post Inflation Targeting Implementation," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 32(74), pages 18-35.
    21. Valera, Harold Glenn A. & Holmes, Mark J. & Hassan, Gazi M., 2017. "How credible is inflation targeting in Asia? A quantile unit root perspective," Economic Modelling, Elsevier, vol. 60(C), pages 194-210.
    22. Grégory Levieuge & Yannick Lucotte & Sébastien Ringuedé, 2015. "Central bank credibility and the expectations channel: Evidence based on a new credibility index," NBP Working Papers 209, Narodowy Bank Polski.
    23. Guido Schultefrankenfeld, 2020. "Appropriate monetary policy and forecast disagreement at the FOMC," Empirical Economics, Springer, vol. 58(1), pages 223-255, January.
    24. Goran Petrevski, 2023. "Macroeconomic Effects of Inflation Targeting: A Survey of the Empirical Literature," Papers 2305.17474, arXiv.org.
    25. Sohei Kaihatsu & Noriyuki Shiraki, 2016. "Firms' Inflation Expectations and Wage-setting Behaviors," Bank of Japan Working Paper Series 16-E-10, Bank of Japan.
    26. Aguilar-Argaez Ana María & Cuadra Gabriel & Ramírez Claudia & Sámano Daniel, 2014. "Anchoring of Inflation Expectations in Light of Adverse Supply Shocks," Working Papers 2014-20, Banco de México.
    27. Ramos-Francia, Manuel & Torres, Alberto, 2008. "Inflation dynamics in Mexico: A characterization using the New Phillips curve," The North American Journal of Economics and Finance, Elsevier, vol. 19(3), pages 274-289, December.
    28. Nora Abu Asab & Juan Carlos Cuestas & Alberto Montagnoli, 2015. "Inflation targeting or Exchange Rate Targeting: Which Framework Supports The Goal of Price Stability in Emerging Market Economics?," Working Papers 2015025, The University of Sheffield, Department of Economics.
    29. Mr. Yan Carriere-Swallow & Bertrand Gruss & Mr. Nicolas E Magud & Mr. Fabian Valencia, 2016. "Monetary Policy Credibility and Exchange Rate Pass-Through," IMF Working Papers 2016/240, International Monetary Fund.
    30. Sascha Möhrle, 2020. "New Evidence on the Anchoring of Inflation Expectations in the Euro Area," ifo Working Paper Series 337, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    31. Abdelkader Aguir, 2015. "Efficiency of monetary policy under inflation targeting," Post-Print hal-03791251, HAL.
    32. MBASSI, Christophe Martial & HYOBA, Suzanne Edwige Clarisse & SHAHBAZ, Muhammad, 2023. "Does monetary policy really matter for environmental protection? The case of inflation targeting," Research in Economics, Elsevier, vol. 77(3), pages 427-452.
    33. Hartmann, Matthias & Conrad, Christian, 2014. "Cross sectional evidence on the relation between monetary policy, macroeconomic conditions and low-frequency inflation uncertainty," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100477, Verein für Socialpolitik / German Economic Association.
    34. Nicolas End, 2020. "Rousseau's social contract or Machiavelli's virtue? A measure of fiscal credibility," Working Papers halshs-03078704, HAL.
    35. Mbassi, Christophe Martial & Samba, Cyrille Michel & Elomo Zogo, Thérèse, 2025. "Does monetary policy fuel energy consumption across the world? Focus on inflation targeting," Energy Economics, Elsevier, vol. 145(C).
    36. Abdelkader Aguir & Mounir Smida, 2015. "Efficiency of monetary policy under inflation targeting," Economics Bulletin, AccessEcon, vol. 35(1), pages 788-813.
    37. Carrillo Julio A. & Elizondo Rocío, 2015. "How Robust Are SVARs at Measuring Monetary Policy in Small Open Economies?," Working Papers 2015-18, Banco de México.
    38. Carlos Caceres & Mr. Yan Carriere-Swallow & Ishak Demir & Bertrand Gruss, 2016. "U.S. Monetary Policy Normalization and Global Interest Rates," IMF Working Papers 2016/195, International Monetary Fund.
    39. Ernest Gnan & Johannes Langthaler & Maria Teresa Valderrama, 2011. "Heterogeneity in Euro Area Consumers’ Inflation Expectations: Some Stylized Facts and Implications," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 43-66.
    40. Paul Hubert, 2011. "Do central banks forecast influence private agents ? Forecasting performance vs. signals," Documents de Travail de l'OFCE 2011-20, Observatoire Francais des Conjonctures Economiques (OFCE).
    41. McKnight, Stephen & Mihailov, Alexander & Pompa Rangel, Antonio, 2020. "What do Latin American inflation targeters care about? A comparative Bayesian estimation of central bank preferences," Journal of Macroeconomics, Elsevier, vol. 63(C).
    42. Jan Acedanski & Julia Wlodarczyk, 2016. "Dispersion Of Inflation Expectations In The European Union During The Global Financial Crisis," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 737-749, December.
    43. Pooja Kapoor & Sujata Kar, 2024. "Do Central Bank Communications Influence Survey of Professional Forecasters? An Empirical Investigation," Business Perspectives and Research, , vol. 12(1), pages 100-112, January.
    44. Mariscal, Rodrigo & Powell, Andrew & Tavella, Pilar, 2018. "On the credibility of inflation-targeting regimes in Latin America," LSE Research Online Documents on Economics 123156, London School of Economics and Political Science, LSE Library.
    45. Paulie, Charlotte, 2019. "Does Inflation Targeting Reduce the Dispersion of Price Setters’ Inflation Expectations?," Working Paper Series 2018:16, Uppsala University, Department of Economics.
    46. Man-Keung Tang & Mr. Xiangrong Yu, 2011. "Communication of Central Bank Thinking and Inflation Dynamics," IMF Working Papers 2011/209, International Monetary Fund.
    47. Huang, Ho-Chuan & Yeh, Chih-Chuan & Wang, Xiuhua, 2019. "Inflation targeting and output-inflation tradeoffs," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 102-120.
    48. Abdelkader AGUIR & Mounir Smida, 2015. "The macroeconomic performance of the inflation targeting policy: An approach based on the Efficient Frontier," Post-Print hal-03825930, HAL.
    49. Mr. Rudolfs Bems & Francesca Caselli & Mr. Francesco Grigoli & Bertrand Gruss & Weicheng Lian, 2018. "Expectations' Anchoring and Inflation Persistence," IMF Working Papers 2018/280, International Monetary Fund.
    50. Suh, Sangwon & Kim, Daehwan, 2021. "Inflation targeting and expectation anchoring: Evidence from developed and emerging market economies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    51. Kanas, Angelos, 2014. "Bond futures, inflation-indexed bonds, and inflation risk premium," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 82-99.
    52. M. Ayhan Kose & Hideaki Matsuoka & Ugo Panizza & Dana Vorisek, 2019. "Inflation Expectations: Review and Evidence," Koç University-TUSIAD Economic Research Forum Working Papers 1904, Koc University-TUSIAD Economic Research Forum.
    53. James Yetman, 2018. "The perils of approximating fixed-horizon inflation forecasts with fixed-event forecasts," BIS Working Papers 700, Bank for International Settlements.
    54. José J. Sidaoui & Manuel Ramos-Francia, 2008. "The monetary transmission mechanism in Mexico: recent developments," BIS Papers chapters, in: Bank for International Settlements (ed.), Transmission mechanisms for monetary policy in emerging market economies, volume 35, pages 363-394, Bank for International Settlements.
    55. Ricardo Reis, 2020. "Comment on "Imperfect Expectations: Theory and Evidence"," NBER Chapters, in: NBER Macroeconomics Annual 2020, volume 35, pages 99-111, National Bureau of Economic Research, Inc.
    56. Ikechukwu Kelikume & Olaniyi Evans, 2015. "Inflation Targeting As A Possible Monetary Framework For Nigeria," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 9(5), pages 71-81.
    57. Baharumshah, Ahmad Zubaidi & Sirag, Abdalla & Soon, Siew-Voon, 2017. "Asymmetric exchange rate pass-through in an emerging market economy: The case of Mexico," Research in International Business and Finance, Elsevier, vol. 41(C), pages 247-259.
    58. Trabelsi, Emna, 2016. "Central bank transparency and the consensus forecast: What does The Economist poll of forecasters tell us?," Research in International Business and Finance, Elsevier, vol. 38(C), pages 338-359.
    59. Reyna Vergara González & Elías Eduardo Gutiérrez Alva, 2014. "Evaluación del cumplimiento de los objetivos de inflación y el papel de las expectativas: evidencia para México, 1995-2012," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-32, November.
    60. Young Bin Ahn & Yoichi Tsuchiya, 2016. "Directional analysis of consumers’ forecasts of inflation in a small open economy: evidence from South Korea," Applied Economics, Taylor & Francis Journals, vol. 48(10), pages 854-864, February.
    61. Michiel De Pooter & Patrice T. Robitaille & Ian Walker & Michael Zdinak, 2014. "Are Long-Term Inflation Expectations Well Anchored in Brazil, Chile and Mexico?," International Finance Discussion Papers 1098, Board of Governors of the Federal Reserve System (U.S.).
    62. Adina Ionela Străchinaru & Bogdan Andrei Dumitrescu, 2019. "Assessing the Sustainability of Inflation Targeting: Evidence from EU Countries with Non-EURO Currencies," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    63. Lena Cleanthous, 2020. "Conceptual note on inflation targeting types and their performance in anchoring inflation expectations," Working Papers 2020-01, Central Bank of Cyprus.
    64. Gayaker, Savas & Ağaslan, Erkan & Alkan, Buket & Çiçek, Serkan, 2021. "The deterioration in credibility, destabilization of exchange rate and the rise in exchange rate pass-through in Turkey," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 571-587.
    65. Paul Hubert, 2013. "FOMC forecasts as a focal point for private expectations," Documents de Travail de l'OFCE 2013-12, Observatoire Francais des Conjonctures Economiques (OFCE).
    66. Bonomo, Marco & Carvalho, Carlos & Eusepi, Stefano & Perrupato, Marina & Abib, Daniel & Ayres, João & Matos, Silvia, 2024. "Abrupt monetary policy change and unanchoring of inflation expectations," Journal of Monetary Economics, Elsevier, vol. 145(S).
    67. Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso, 2024. "A theory-based method to evaluate the impact of central bank inflation forecasts on private inflation expectations," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1069-1084.
    68. Bems, Rudolfs & Caselli, Francesca & Grigoli, Francesco & Gruss, Bertrand, 2020. "Gains from anchoring inflation expectations: Evidence from the taper tantrum shock," Economics Letters, Elsevier, vol. 188(C).
    69. Choudary Ihtasham Ali & Sami Ullah & Umar Ijaz Ahmed & Abd Ur Rehman & Hafiz Zahid Mehmood & Mudassar Yasin & Mohsin Raza, 2023. "Short and Long Run Effects of Monetary Policy on Food Inflation: A Study of Pakistan," Journal of Economic Impact, Science Impact Publishers, vol. 5(2), pages 146-154.
    70. Olivier Coibion & Yuriy Gorodnichenko, 2010. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," Working Papers 102, Economics Department, William & Mary.
    71. Abdelkader Aguir & Mounir Smida, 2014. "The Effects of Inflation Targeting on Macroeconomics Performance," Post-Print hal-03791288, HAL.
    72. Abdelkader Aguir, 2014. "The Impact of Central Bank Independence on The Performance of Inflation Targeting Regimes : Emerging Economies," Post-Print hal-03825933, HAL.
    73. Grégory Levieuge & Yannick Lucotte & Florian Pradines-Jobet, 2019. "The Cost of Banking Crises: Does the Policy Framework Matter?," Working papers 712, Banque de France.
    74. Stefano Neri & Tiziano Ropele, 2019. "Disinflationary shocks and inflation target uncertainty," Temi di discussione (Economic working papers) 1230, Bank of Italy, Economic Research and International Relations Area.
    75. Dooyeon Cho & Husang Kim, 2024. "Inflation target adjustments: Does an improvement in institutional or economic preconditions matter?," International Finance, Wiley Blackwell, vol. 27(2), pages 129-179, August.
    76. Paul Hubert, 2010. "Monetary policy, imperfect information and the expectations channel [Politique monétaire,information imparfaite et canal des anticipations]," Sciences Po Economics Publications (main) tel-04095385, HAL.
    77. John Thornton & Chrysovalantis Vasilakis, 2025. "Further evidence on inflation targeting and income distribution," Southern Economic Journal, John Wiley & Sons, vol. 91(4), pages 1474-1493, April.
    78. Sangyong Joo & Daehwan Kim & Jeffrey Nilsen, 2021. "Monetary Policy and Long-Term Interest Rates in Korea: A Decomposition Analysis," Korean Economic Review, Korean Economic Association, vol. 37, pages 327-366.
    79. Carrière-Swallow, Yan & Deb, Pragyan & Furceri, Davide & Jiménez, Daniel & Ostry, Jonathan D., 2023. "Shipping costs and inflation," Journal of International Money and Finance, Elsevier, vol. 130(C).
    80. Christoph S. Weber, 2018. "Central bank transparency and inflation (volatility) – new evidence," International Economics and Economic Policy, Springer, vol. 15(1), pages 21-67, January.
    81. Akosah, Nana & Alagidede, Paul & Schaling, Eric, 2019. "Monetary Policy Transparency in Ghana: Recent Evidence," MPRA Paper 96998, University Library of Munich, Germany.
    82. Lu, You-Xun, 2022. "The stabilizing effect of the zero lower bound: A perspective of interest rate target zones," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 61-67.
    83. Toshitaka Sekine & Frank Packer & Shunichi Yoneyama, 2022. "Individual Trend Inflation," IMES Discussion Paper Series 22-E-14, Institute for Monetary and Economic Studies, Bank of Japan.
    84. Kenny, Geoff & Dovern, Jonas, 2017. "The long-term distribution of expected inflation in the euro area: what has changed since the great recession?," Working Paper Series 1999, European Central Bank.
    85. Pierdzioch, Christian & Rülke, Jan-Christoph, 2014. "Central banks’ interest rate projections and forecast coordination," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 130-137.
    86. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2013. "A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(3), pages 294-301.
    87. Conrad, Christian & Hartmann, Matthias, 2019. "On the determinants of long-run inflation uncertainty: Evidence from a panel of 17 developed economies," European Journal of Political Economy, Elsevier, vol. 56(C), pages 233-250.
    88. Michael Ehrmann, 2014. "Targeting Inflation from Below - How Do Inflation Expectations Behave?," Staff Working Papers 14-52, Bank of Canada.
    89. Petra Gerlach & Richhild Moessner & Rina Rosenblatt-Wisch, 2017. "Computing long‐term market inflation expectations for countries without inflation expectation markets," Working Papers 2017-09, Swiss National Bank.
    90. Große Steffen, Christoph, 2021. "Anchoring of long-term inflation expectations: Do inflation target formulations matter?," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242466, Verein für Socialpolitik / German Economic Association.
    91. Mr. Yan Carriere-Swallow & Mr. Luis Ignacio Jácome & Mr. Nicolas E Magud & Alejandro M. Werner, 2016. "Central Banking in Latin America: The Way Forward," IMF Working Papers 2016/197, International Monetary Fund.
    92. Zafar Hayat & Saher Masood, 2022. "Inflation Targeting Skepticism: Myth or Reality? A Way Forward for Pakistan (Article)," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 61(1), pages 1-27.
    93. Enrique A. López-Enciso & Hernando Vargas-Herrera & Norberto Rodríguez-Niño, 2016. "La estrategia de inflación objetivo en Colombia. Una visión histórica," Borradores de Economia 952, Banco de la Republica de Colombia.

  13. Ramos Francia Manuel & Capistrán Carlos, 2006. "Inflation Dynamics in Latin America," Working Papers 2006-11, Banco de México.

    Cited by:

    1. Carrasco, Carlos A., 2013. "El Nuevo Consenso Macroeconómico y la mediocridad del crecimiento económico en México [New Consensus Macroeconomics and the mediocrity of economic growth in Mexico]," MPRA Paper 53391, University Library of Munich, Germany.
    2. Benavides Guillermo, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Working Papers 2010-12, Banco de México.
    3. Ramos Francia Manuel & Noriega Antonio E. & Rodríguez-Pérez Cid Alonso, 2015. "The Use of Monetary Aggregates as Indicators of the Future Evolution of Consumer Prices: Monetary Growth and Inflation Target," Working Papers 2015-14, Banco de México.
    4. Valera, Harold Glenn A. & Holmes, Mark J. & Hassan, Gazi M., 2017. "How credible is inflation targeting in Asia? A quantile unit root perspective," Economic Modelling, Elsevier, vol. 60(C), pages 194-210.
    5. Mateo Isoardi & Luis A. Gil-Alana, 2019. "Inflation in Argentina: Analysis of Persistence Using Fractional Integration," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 45(2), pages 204-223, April.
    6. Chiquiar Daniel & Noriega Antonio E. & Ramos Francia Manuel, 2007. "A Time Series Approach to Test a Change in Inflation Persistence: The Mexican Experience," Working Papers 2007-01, Banco de México.
    7. Melik Kamisli & Serap Kamisli & Fatih Temizel & Ethem Esen, 2017. "What Affects the Relationships between Oil and Industrial Sector? Case of Eurozone," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(9), pages 52-59, September.
    8. Ramon Moreno, 2010. "Some issues in measuring and tracking prices in emerging market exonomies," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 13-51, Bank for International Settlements.
    9. Ramos-Francia, Manuel & Torres, Alberto, 2008. "Inflation dynamics in Mexico: A characterization using the New Phillips curve," The North American Journal of Economics and Finance, Elsevier, vol. 19(3), pages 274-289, December.
    10. Andrés Gonzalez & Franz Hamann, 2011. "Lack of Credibility, Inflation Persistence and Disinflation in Colombia," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE.
    11. Alan Finkelstein Shapiro & Andres Gonzalez Gomez & Jessica Roldan-Pena & Victoria Nuguer, 2018. "Price Dynamics and the Financing Structure of Firms in Emerging Economies," 2018 Meeting Papers 339, Society for Economic Dynamics.
    12. Georgios P. Kouretas & Mark E. Wohar, 2012. "The dynamics of inflation: a study of a large number of countries," Applied Economics, Taylor & Francis Journals, vol. 44(16), pages 2001-2026, June.
    13. Antonio Noriega & Carlos Capistrán & Manuel Ramos-Francia, 2013. "On the dynamics of inflation persistence around the world," Empirical Economics, Springer, vol. 44(3), pages 1243-1265, June.
    14. Lenin Arango-Castillo & Francisco J. Martínez-Ramírez & María José Orraca, 2024. "Univariate Measures of Persistence: A Comparative Analysis," Working Papers 2024-11, Banco de México.
    15. Vaughan Daniel, 2013. "An Analysis of the Process of Disinflationary Structural Change: The Case of Mexico," Working Papers 2013-12, Banco de México.
    16. Mr. Shaun K. Roache, 2014. "Inflation Persistence in Brazil - A Cross Country Comparison," IMF Working Papers 2014/055, International Monetary Fund.
    17. Mr. Kenji Moriyama, 2011. "Inflation Inertia in Egypt and its Policy Implications," IMF Working Papers 2011/160, International Monetary Fund.
    18. Guglielmo Maria Caporale & Luis A. Gil-Alana & Carlos Poza, 2020. "Inflation in the G7 Countries: Persistence and Structural Breaks," CESifo Working Paper Series 8349, CESifo.
    19. Sidaoui José Julián & Capistrán Carlos & Chiquiar Daniel & Ramos Francia Manuel, 2009. "A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico," Working Papers 2009-14, Banco de México.
    20. Hernando Vargas, 2007. "The Transmission Mechanism of Monetary Policy in Colombia Major Changes and Current Features," Borradores de Economia 431, Banco de la Republica de Colombia.
    21. Capistran, Carlos & Chiquiar, Daniel & Hernandez, Juan R., 2017. "Identifying Dornbusch's Exchange Rate Overshooting with Structural VECs: Evidence from Mexico," MPRA Paper 100745, University Library of Munich, Germany.
    22. André Marine Charlotte & Medina Espidio Sebastián, 2022. "Optimal Robust Monetary Policy in a Small Open Economy," Working Papers 2022-17, Banco de México.
    23. Ibarra, Raul & Trupkin, Danilo R., 2016. "Reexamining the relationship between inflation and growth: Do institutions matter in developing countries?," Economic Modelling, Elsevier, vol. 52(PB), pages 332-351.
    24. Cortés Espada Josué Fernando & Sámano Daniel & Gutiérrez Villanueva Rubí, 2019. "Dynamics of Mexican Inflation: A Wavelet Analysis," Working Papers 2019-17, Banco de México.
    25. Jonathan Fortun Vargas, 2012. "Monetary dynamics in post inflation Bolivia," Revista Latinoamericana de Desarrollo Economico, Carrera de Economía de la Universidad Católica Boliviana (UCB), issue 18, pages 65-104.
    26. Gabriel Rodríguez & Luis Surco, 2024. "Modeling the trend, persistence, and volatility of inflation in Pacific Alliance countries: an empirical application using a model with inflation bands," Documentos de Trabajo / Working Papers 2024-533, Departamento de Economía - Pontificia Universidad Católica del Perú.
    27. Laura Inés D’Amato & María Lorena Garegnani, 2013. "¿Cuán persistente es la inflación en Argentina?: regímenes inflacionarios y dinámica de precios en los últimos 50 años," Investigación Conjunta-Joint Research, in: Laura Inés D'Amato & Enrique López Enciso & María Teresa Ramírez Giraldo (ed.), Dinámica inflacionaria, persistencia y formación de precios y salarios, edition 1, chapter 4, pages 91-115, Centro de Estudios Monetarios Latinoamericanos, CEMLA.
    28. Carlos Medel, 2016. "Un Análisis de la Capacidad Predictiva del Precio del Cobre sobre la Inflación Global," Working Papers Central Bank of Chile 786, Central Bank of Chile.
    29. Ysusi Carla, 2009. "Analysis of the Dynamics of Mexican Inflation Using Wavelets," Working Papers 2009-09, Banco de México.
    30. André, Marine Charlotte & Espidio, Sebastián Medina, 2024. "Optimal robust monetary policy in a small open emerging-market economy," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    31. Harold Glenn A. Valera & Mark J. Holmes & Gazi M. Hassan, 2018. "Is inflation targeting credible in Asia? A panel GARCH approach," Empirical Economics, Springer, vol. 54(2), pages 523-546, March.
    32. Broto, Carmen, 2011. "Inflation targeting in Latin America: Empirical analysis using GARCH models," Economic Modelling, Elsevier, vol. 28(3), pages 1424-1434, May.
    33. Shesadri Banerjee, 2017. "Empirical Regularities of Inflation Volatility: Evidence from Advanced and Developing Countries," South Asian Journal of Macroeconomics and Public Finance, , vol. 6(1), pages 133-156, June.
    34. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.
    35. Enrique A. López-Enciso & Hernando Vargas-Herrera & Norberto Rodríguez-Niño, 2016. "La estrategia de inflación objetivo en Colombia. Una visión histórica," Borradores de Economia 952, Banco de la Republica de Colombia.

  14. Capistrán Carlos, 2006. "Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious?," Working Papers 2006-14, Banco de México.

    Cited by:

    1. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    2. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    3. Bertrand Candelon & Francesco Roccazzella, 2025. "Evaluating Inflation Forecasts in the Euro Area and the Role of the ECB," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 978-1008, April.
    4. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Central banks’ inflation forecasts under asymmetric loss: Evidence from four Latin-American countries," Economics Letters, Elsevier, vol. 129(C), pages 66-70.
    5. Paul Hubert, 2011. "Central Bank Forecasts as an Instrument of Monetary Policy," Documents de Travail de l'OFCE 2011-23, Observatoire Francais des Conjonctures Economiques (OFCE).
    6. Pablo Pincheira B. & Nicolás Fernández, 2011. "Jaque Mate a las Proyecciones de Consenso," Working Papers Central Bank of Chile 630, Central Bank of Chile.
    7. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    8. Sargent, Thomas & Ellison, Martin, 2009. "A defence of the FOMC," CEPR Discussion Papers 7510, C.E.P.R. Discussion Papers.
    9. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    10. Timmermann, Allan & Elliott, Graham, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
    11. Robert Krol, 2014. "Forecast Bias of Government Agencies," Cato Journal, Cato Journal, Cato Institute, vol. 34(1), pages 99-112, Winter.
    12. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    13. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    14. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    15. Andrea Betancor & Pablo Pincheira, 2008. "Forecasting Inflation Forecast Errors," Working Papers Central Bank of Chile 477, Central Bank of Chile.
    16. Julien Champagne & Guillaume Poulin‐Bellisle & Rodrigo Sekkel, 2020. "Introducing the Bank of Canada staff economic projections database," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 114-129, January.
    17. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    18. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    19. De Rezende, Rafael B., 2015. "Risks in macroeconomic fundamentals and excess bond returns predictability," Working Paper Series 295, Sveriges Riksbank (Central Bank of Sweden).
    20. Tillmann, Peter, 2010. "The Fed's perceived Phillips curve: Evidence from individual FOMC forecasts," Journal of Macroeconomics, Elsevier, vol. 32(4), pages 1008-1013, December.
    21. Julieta Caunedo & Riccardo DiCecio & Ivana Komunjer & Michael T. Owyang, 2013. "Asymmetry, Complementarities, and State Dependence in Federal Reserve Forecasts," Working Papers 2013-012, Federal Reserve Bank of St. Louis, revised 29 Dec 2017.
    22. Baghestani, Hamid & Khallaf, Ashraf, 2012. "Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 222-229.
    23. Hamid Baghestani, 2014. "On the loss structure of federal reserve forecasts of output growth," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(3), pages 518-527, July.
    24. Tsuchiya, Yoichi, 2016. "Assessing macroeconomic forecasts for Japan under an asymmetric loss function," International Journal of Forecasting, Elsevier, vol. 32(2), pages 233-242.
    25. Conrad, Christian & Lahiri, Kajal, 2024. "Heterogeneous Expectations among Professional Forecasters," Working Papers 0754, University of Heidelberg, Department of Economics.
    26. Peter Tillmann, 2011. "Reputation and Forecast Revisions: Evidence from the FOMC," MAGKS Papers on Economics 201128, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    27. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    28. Christian Pierdzioch & Jan-Christoph Rülke & Peter Tillmann, 2013. "Using forecasts to uncover the loss function of FOMC members," MAGKS Papers on Economics 201302, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    29. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    30. Timmermann Allan & Capistrán Carlos, 2006. "Disagreement and Biases in Inflation Expectations," Working Papers 2006-07, Banco de México.
    31. Katrin Wölfel & Christoph S. Weber, 2017. "Searching for the Fed’s reaction function," Empirical Economics, Springer, vol. 52(1), pages 191-227, February.
    32. Sinclair, Tara M. & Joutz, Fred & Stekler, H.O., 2010. "Can the Fed predict the state of the economy?," Economics Letters, Elsevier, vol. 108(1), pages 28-32, July.
    33. Carlos Capistrán & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2‐3), pages 365-396, March.
    34. Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    35. Demetrescu, Matei & Roling, Christoph, 2025. "Testing the Predictive Ability of Possibly Persistent Variables under Asymmetric Loss," Econometrics and Statistics, Elsevier, vol. 33(C), pages 80-104.
    36. Henning Fischer & Marta García-Bárzana & Peter Tillmann & Peter Winker, 2014. "Evaluating FOMC forecast ranges: an interval data approach," Empirical Economics, Springer, vol. 47(1), pages 365-388, August.
    37. Hamid Baghestani, 2011. "A directional analysis of Federal Reserve predictions of growth in unit labor costs and productivity," International Review of Applied Economics, Taylor & Francis Journals, vol. 25(3), pages 303-311.
    38. El in Ayka Alp & Zeynep Biyik, 2018. "Inflation Expectation Dynamics: A Structural Long-run Analysis for Turkey," International Journal of Economics and Financial Issues, Econjournals, vol. 8(2), pages 350-356.
    39. Thapar, Aditi, 2008. "Using private forecasts to estimate the effects of monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 806-824, May.
    40. Bovi, Maurizio, 2019. "A Time-Varying Expectations Formation Mechanism," MPRA Paper 97624, University Library of Munich, Germany.
    41. Tillmann, Peter, 2011. "Strategic forecasting on the FOMC," European Journal of Political Economy, Elsevier, vol. 27(3), pages 547-553, September.
    42. Nakazono, Yoshiyuki, 2013. "Strategic behavior of Federal Open Market Committee board members: Evidence from members’ forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 93(C), pages 62-70.
    43. Baghestani, Hamid, 2011. "Federal Reserve and private forecasts of growth in investment," Journal of Economics and Business, Elsevier, vol. 63(4), pages 290-305, July.
    44. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    45. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Working Papers Central Bank of Chile 556, Central Bank of Chile.
    46. Kim, Insu & Kim, Minsoo, 2009. "Irrational Bias in Inflation Forecasts," MPRA Paper 16447, University Library of Munich, Germany.
    47. Hamid Baghestani, 2013. "Evaluating Federal Reserve predictions of growth in consumer spending," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1637-1646, May.

  15. Timmermann Allan & Capistrán Carlos, 2006. "Forecast Combination with Entry and Exit of Experts," Working Papers 2006-08, Banco de México.

    Cited by:

    1. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    2. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, The Center for Economic Research.
    3. Tu, Yundong & Yi, Yanping, 2017. "Forecasting cointegrated nonstationary time series with time-varying variance," Journal of Econometrics, Elsevier, vol. 196(1), pages 83-98.
    4. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    5. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    6. Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
    7. Jonathan Benchimol & Makram El-Shagi & Yossi Saadon, 2020. "Do Expert Experience and Characteristics Affect Inflation Forecasts?," Bank of Israel Working Papers 2020.11, Bank of Israel.
    8. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    9. George Papadopoulos & Savas Papadopoulos & Thomas Sager, 2016. "Credit risk stress testing for EU15 banks: a model combination approach," Working Papers 203, Bank of Greece.
    10. 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.
    11. Mihaela Simionescu (Bratu), 2014. "The Performance of Predictions Based on the Dobrescu Macromodel for the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 179-195, October.
    12. Dan Zhu & Qingwei Wang & John Goddard, 2022. "A new hedging hypothesis regarding prediction interval formation in stock price forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 697-717, July.
    13. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    14. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    15. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    16. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joëlle, 2019. "A comprehensive appraisal of style-integration methods," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 134-150.
    17. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    18. Wang, Cindy S.H. & Fan, Rui & Xie, Yiqiang, 2023. "Market systemic risk, predictability and macroeconomics news," Finance Research Letters, Elsevier, vol. 56(C).
    19. Fiechter, Chad & Kuethe, Todd & Zhang, Wendong, 2023. "Information Rigidities and Farmland Value Expectations," ISU General Staff Papers 202306131414240000, Iowa State University, Department of Economics.
    20. Scott A. Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019. "Predicting Benchmarked US State Employment Data in Real Time," Working Papers 2019-037, Federal Reserve Bank of St. Louis, revised 11 Mar 2021.
    21. 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.
    22. Ma, Feng & Guo, Yangli & Luo, Qin & Zhong, Juandan, 2025. "Forecasting corporate bond returns amid climate change risk: A dynamic forecast combination approach," Journal of International Money and Finance, Elsevier, vol. 154(C).
    23. Jinjun Liu & Ming-Yen Cheng, 2026. "Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach," Papers 2601.04608, arXiv.org.
    24. Laura Carabotta & Peter Claeys, 2024. "Combine to compete: Improving fiscal forecast accuracy over time," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 948-982, July.
    25. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    26. Eckert, Florian & Hyndman, Rob J. & Panagiotelis, Anastasios, 2021. "Forecasting Swiss exports using Bayesian forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 693-710.
    27. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    28. Robin C. Sickles & Jiaqi Hao & Chenjun Shang, 2014. "Panel data and productivity measurement: an analysis of Asian productivity trends," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 12(3), pages 211-231, August.
    29. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    30. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    31. Blanc, Sebastian M. & Setzer, Thomas, 2016. "When to choose the simple average in forecast combination," Journal of Business Research, Elsevier, vol. 69(10), pages 3951-3962.
    32. Shi, Chunpei & Wei, Yu & Li, Xiafei & Liu, Yuntong, 2023. "Combination forecasts of China's oil futures returns based on multiple uncertainties and their connectedness with oil," Energy Economics, Elsevier, vol. 126(C).
    33. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    34. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    35. Sermpinis, Georgios & Stasinakis, Charalampos & Hassanniakalager, Arman, 2017. "Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds," European Journal of Operational Research, Elsevier, vol. 263(2), pages 540-558.
    36. Philippe Andrade & Valère Fourel & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    37. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    38. Ma, Feng & Lu, Fei & Tao, Ying, 2022. "Geopolitical risk and excess stock returns predictability: New evidence from a century of data," Finance Research Letters, Elsevier, vol. 50(C).
    39. Faias, José Afonso, 2023. "Predicting the equity risk premium using the smooth cross-sectional tail risk: The importance of correlation," Journal of Financial Markets, Elsevier, vol. 63(C).
    40. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    41. Keijsers, Bart & van Dijk, Dick, 2025. "Does economic uncertainty predict real activity in real time?," International Journal of Forecasting, Elsevier, vol. 41(2), pages 748-762.
    42. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    43. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    44. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    45. Huck, Nicolas, 2010. "Pairs trading and outranking: The multi-step-ahead forecasting case," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1702-1716, December.
    46. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    47. Svec, Jiri & Katrak, Xerxis, 2017. "Forecasting volatility with interacting multiple models," Finance Research Letters, Elsevier, vol. 20(C), pages 245-252.
    48. Zanetti Chini, Emilio, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 34(4), pages 711-732.
    49. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
    50. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    51. Lavancier, F. & Rochet, P., 2016. "A general procedure to combine estimators," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 175-192.
    52. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Paper 2020/14, Norges Bank.
    53. Liao, Cunfei & Luo, Qianlin & Tang, Guohao, 2021. "Aggregate liquidity premium and cross-sectional returns: Evidence from China," Economic Modelling, Elsevier, vol. 104(C).
    54. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
    55. Zhu, Min & Chen, Rui & Du, Ke & Wang, You-Gan, 2018. "Dividend growth and equity premium predictability," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 125-137.
    56. Zhang, Huajing & Jiang, Fuwei & Liu, Yumin, 2024. "Extrapolative beliefs and return predictability: Evidence from China," Journal of Behavioral and Experimental Finance, Elsevier, vol. 43(C).
    57. Shu, Lei & Hao, Yifan & Chen, Yu & Yang, Qing, 2025. "SFQRA: Scaled factor-augmented quantile regression with aggregation in conditional mean forecasting," Journal of Multivariate Analysis, Elsevier, vol. 207(C).
    58. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    59. Davide De Gaetano, 2016. "Forecast Combinations For Realized Volatility In Presence Of Structural Breaks," Departmental Working Papers of Economics - University 'Roma Tre' 0208, Department of Economics - University Roma Tre.
    60. Sebastiano Manzan, 2011. "Differential Interpretation in the Survey of Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(5), pages 993-1017, August.
    61. Carlos Segura-Rodriguez, 2025. "Inflation Forecasting in Costa Rica: The Contribution of Exogenous Variables in Item-Level Disaggregated Models," Documentos de Trabajo 2509, Banco Central de Costa Rica.
    62. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    63. Lv, Wendai & Qi, Jipeng, 2022. "Stock market return predictability: A combination forecast perspective," International Review of Financial Analysis, Elsevier, vol. 84(C).
    64. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    65. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    66. Adolfo Rodríguez-Vargas, 2019. "Univariate Forecasts for Costa Rican Inflation With Stochastic Volatility and GARCH Effects," Documentos de Trabajo 1604, Banco Central de Costa Rica.
    67. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    68. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    69. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    70. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.
    71. Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers 80, Brandeis University, Department of Economics and International Business School.
    72. Cheng, Gang & Yang, Yuhong, 2015. "Forecast combination with outlier protection," International Journal of Forecasting, Elsevier, vol. 31(2), pages 223-237.
    73. 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.
    74. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Fotios Petropoulos, 2015. "Forecasting with Temporal Hierarchies," Monash Econometrics and Business Statistics Working Papers 16/15, Monash University, Department of Econometrics and Business Statistics.
    75. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
    76. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    77. 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.
    78. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    79. Clements, Michael P., "undated". "Subjective and Ex Post Forecast Uncertainty: US Inflation and Output Growth," Economic Research Papers 270629, University of Warwick - Department of Economics.
    80. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    81. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    82. 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.
    83. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    84. Jan Capek & Jesus Crespo Cuaresma & Niko Hauzenberger & Vlastimil Reichel, 2020. "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," Department of Economics Working Papers wuwp305, Vienna University of Economics and Business, Department of Economics.
    85. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    86. Constantin Bürgi & Tara M. Sinclair, 2015. "A Nonparametric Approach to Identifying a Subset of Forecasters that Outperforms the Simple Average," Working Papers 2015-006, The George Washington University, The Center for Economic Research.
    87. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393, April.
    88. Costantini, Mauro & Gunter, Ulrich & Kunst, Robert M., 2014. "Forecast combinations in a DSGE-VAR lab," Economics Series 309, Institute for Advanced Studies.
    89. 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.
    90. Di Fonzo, Tommaso & Girolimetto, Daniele, 2024. "Forecast combination-based forecast reconciliation: Insights and extensions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 490-514.
    91. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
    92. Huck, Nicolas, 2009. "Pairs selection and outranking: An application to the S&P 100 index," European Journal of Operational Research, Elsevier, vol. 196(2), pages 819-825, July.
    93. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    94. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    95. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    96. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "How to Estimate Beta?," Hannover Economic Papers (HEP) dp-617, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    97. Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
    98. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "Estimating beta: Forecast adjustments and the impact of stock characteristics for a broad cross-section," Journal of Financial Markets, Elsevier, vol. 44(C), pages 91-118.
    99. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    100. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    101. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    102. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    103. Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019. "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series 28, Bank of Lithuania.
    104. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
    105. Lin, Hai & Tao, Xinyuan & Wu, Chunchi, 2022. "Forecasting earnings with combination of analyst forecasts," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 133-159.
    106. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    107. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    108. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.
    109. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    110. Mihaela Bratu (Simionescu), 2012. "Improving the accuracy of consensus forecasts for the EURO area," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 4(2), pages 11-15, Decembre.
    111. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    112. Till Weigt & Bernd Wilfling, 2016. "A new combination approach to reducing forecast errors with an application to volatility forecasting," CQE Working Papers 4616, Center for Quantitative Economics (CQE), University of Muenster.
    113. Wang, Chengyang & Nishiyama, Yoshihiko, 2015. "Volatility forecast of stock indices by model averaging using high-frequency data," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 324-337.
    114. 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.
    115. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, The Center for Economic Research.
    116. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    117. Christopher G. Gibbs, 2017. "Forecast combination, non-linear dynamics, and the macroeconomy," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(3), pages 653-686, March.
    118. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    119. Jiang, Yu & Guo, Yongji & Zhang, Yihao, 2017. "Forecasting China's GDP growth using dynamic factors and mixed-frequency data," Economic Modelling, Elsevier, vol. 66(C), pages 132-138.
    120. Park, Jin Suk & Newaz, Mohammad Khaleq, 2021. "Liquidity and short-run predictability: Evidence from international stock markets," Global Finance Journal, Elsevier, vol. 50(C).
    121. Ana Sofia Monteiro & Helder Sebastião & Nuno Silva, 2025. "Prediction and Allocation of Stocks, Bonds, and REITs in the US Market," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1191-1230, March.
    122. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    123. Antonios K. Alexandridis & Ekaterini Panopoulou & Ioannis Souropanis, 2024. "Forecasting exchange rates: An iterated combination constrained predictor approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 983-1017, July.
    124. Likun Lei & Yaojie Zhang & Yu Wei & Yi Zhang, 2021. "Forecasting the volatility of Chinese stock market: An international volatility index," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1336-1350, January.
    125. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    126. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    127. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    128. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    129. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
    130. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    131. 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.
    132. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    133. Hai Lin & Chunchi Wu & Guofu Zhou, 2018. "Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach," Management Science, INFORMS, vol. 64(9), pages 4218-4238, September.
    134. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    135. 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.
    136. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    137. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    138. Matsypura, Dmytro & Thompson, Ryan & Vasnev, Andrey L., 2018. "Optimal selection of expert forecasts with integer programming," Omega, Elsevier, vol. 78(C), pages 165-175.
    139. Stein, Tobias, 2024. "Forecasting the equity premium with frequency-decomposed technical indicators," International Journal of Forecasting, Elsevier, vol. 40(1), pages 6-28.
    140. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2018. "Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods," MPRA Paper 85523, University Library of Munich, Germany.
    141. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
    142. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    143. Magnus, Jan R. & Vasnev, Andrey L., 2023. "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1895-1908.
    144. Wei, Wei & Zhu, Dan, 2022. "Generic improvements to least squares monte carlo methods with applications to optimal stopping problems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1132-1144.
    145. Haase, Felix & Neuenkirch, Matthias, 2023. "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, vol. 39(2), pages 587-605.
    146. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    147. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    148. Reichlin, Lucrezia & Andreini, Paolo & Hasenzagl, Thomas & Senftleben-König, Charlotte & Strohsal, Till, 2020. "Nowcasting German GDP," CEPR Discussion Papers 14323, C.E.P.R. Discussion Papers.
    149. Victor Lopez-Perez, 2016. "Macroeconomic Forecast Uncertainty In The Euro Area," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(1), pages 9-41, March.
    150. 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.
    151. Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022. "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, vol. 106(C).
    152. Chao, Shih-Wei, 2016. "Do economic variables improve bond return volatility forecasts?," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 10-26.
    153. Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-04, Towson University, Department of Economics, revised Mar 2020.
    154. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-19, August.
    155. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    156. Ren, Yu & Liang, Xuanxuan & Wang, Qin, 2021. "Short-term exchange rate forecasting: A panel combination approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    157. 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.
    158. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2021. "Systemic risk measures and distribution forecasting of macroeconomic shocks," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 178-196.
    159. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    160. Xiaowen Wang & Ying Ma & Wen Li, 2021. "The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    161. Pablo Pincheira-Brown & Andrea Bentancor & Nicolás Hardy, 2023. "An Inconvenient Truth about Forecast Combinations," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    162. Alexandridis, Antonios K. & Panopoulou, Ekaterini & Souropanis, Ioannis, 2024. "Forecasting exchange rate volatility: An amalgamation approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 97(C).
    163. Li, Kaixin & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil returns with oil-related industry ESG indices," Journal of Commodity Markets, Elsevier, vol. 36(C).
    164. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    165. 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.
    166. Kajal Lahiri & Liu Yang, 2015. "A Non-linear Forecast Combination Procedure for Binary Outcomes," CESifo Working Paper Series 5175, CESifo.
    167. Fang, Debin & Hao, Peng & Hao, Jian, 2019. "Study of the influence mechanism of China's electricity consumption based on multi-period ST-LMDI model," Energy, Elsevier, vol. 170(C), pages 730-743.
    168. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    169. 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.
    170. 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.
    171. Bingzi Jin & Xiaojie Xu, 2025. "Forecasts of coking coal futures price indices through Gaussian process regressions," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 203-217, March.
    172. Monticini, Andrea & Ravazzolo, Francesco, 2014. "Forecasting the intraday market price of money," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 304-315.
    173. Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.
    174. 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.
    175. 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.
    176. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    177. Till Weigt & Bernd Wilfling, 2021. "An approach to increasing forecast‐combination accuracy through VAR error modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 686-699, July.
    178. Catherine L. Kling & Raymond W. Arritt & Gray Calhoun & David A. Keiser, 2017. "Integrated Assessment Models of the Food, Energy, and Water Nexus: A Review and an Outline of Research Needs," Annual Review of Resource Economics, Annual Reviews, vol. 9(1), pages 143-163, October.
    179. 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.
    180. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    181. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    182. Hubert Dichtl & Wolfgang Drobetz & Viktoria‐Sophie Wendt, 2021. "How to build a factor portfolio: Does the allocation strategy matter?," European Financial Management, European Financial Management Association, vol. 27(1), pages 20-58, January.
    183. Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023. "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, vol. 121(C).
    184. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    185. A. Ford Ramsey & Yong Liu, 2023. "Linear pooling of potentially related density forecasts in crop insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(3), pages 769-788, September.
    186. Sancetta, A., 2007. "Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains," Cambridge Working Papers in Economics 0735, Faculty of Economics, University of Cambridge.
    187. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
    188. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    189. Robert L. Czudaj, 2021. "Heterogeneity of Beliefs and Information Rigidity in the Crude Oil Market: Evidence from Survey Data," Chemnitz Economic Papers 050, Department of Economics, Chemnitz University of Technology, revised Sep 2021.
    190. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    191. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    192. Cai, Michael & Del Negro, Marco & Giannoni, Marc P. & Gupta, Abhi & Li, Pearl & Moszkowski, Erica, 2019. "DSGE forecasts of the lost recovery," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1770-1789.
    193. João Valle e Azevedo, 2011. "Rational vs. professional forecasts," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    194. Aparicio, Diego & Bertolotto, Manuel I., 2020. "Forecasting inflation with online prices," International Journal of Forecasting, Elsevier, vol. 36(2), pages 232-247.
    195. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2021. "ExpectHill estimation, extreme risk and heavy tails," Journal of Econometrics, Elsevier, vol. 221(1), pages 97-117.
    196. Ma, Yao & Yang, Baochen & Su, Yunpeng, 2020. "Technical trading index, return predictability and idiosyncratic volatility," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 879-900.
    197. 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.
    198. Li Liu & Yudong Wang, 2021. "Forecasting aggregate market volatility: The role of good and bad uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 40-61, January.
    199. Fei, Tianlun & Liu, Xiaoquan & Wen, Conghua, 2019. "Cross-sectional return dispersion and volatility prediction," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    200. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    201. 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.
    202. Kakuho Furukawa & Ryohei Hisano & Yukio Minoura & Tomoyuki Yagi, 2022. "A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches," Bank of Japan Working Paper Series 22-E-16, Bank of Japan.
    203. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    204. Constantin Bürgi, 2023. "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series 10203, CESifo.
    205. Narayan Kundan Kishor, 2021. "Forecasting real‐time economic activity using house prices and credit conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 213-227, March.
    206. Menkhoff, Lukas & Sakha, Sahra, 2017. "Estimating risky behavior with multiple-item risk measures," Journal of Economic Psychology, Elsevier, vol. 59(C), pages 59-86.
    207. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    208. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    209. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    210. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    211. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    212. Konstantin Kholodilin, 2014. "Business confidence and forecasting of housing prices and rents in large German cities," ERSA conference papers ersa14p9, European Regional Science Association.
    213. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    214. Pedersen, Michael, 2019. "Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1100-1107.
    215. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    216. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    217. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    218. Álvarez, Luis J. & Sánchez, Isabel, 2019. "Inflation projections for monetary policy decision making," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 568-585.
    219. Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
    220. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Papers 2006.11265, arXiv.org, revised Sep 2020.
    221. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    222. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
    223. Buchen, Teresa & Wohlrabe, Klaus, 2011. "Forecasting with many predictors: Is boosting a viable alternative?," Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
    224. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2019. "Robust optimization of forecast combinations," International Journal of Forecasting, Elsevier, vol. 35(3), pages 910-926.
    225. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    226. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    227. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    228. Maria Lycheva & Alexey Mironenkov & Alexey Kurbatskii & Dean Fantazzini, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 28-49.
    229. Lars Lien Ankile & Kjartan Krange, 2022. "Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation," Papers 2201.00426, arXiv.org, revised Nov 2022.
    230. Knüppel, Malte & Krüger, Fabian, 2019. "Forecast uncertainty, disagreement, and the linear pool," Discussion Papers 28/2019, Deutsche Bundesbank.
    231. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
    232. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    233. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    234. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    235. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
    236. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    237. Roman S. Leukhin, 2019. "Short-Term Fiscal Projections Using Forecast Combination Approach," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 9-21, June.
    238. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    239. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
    240. Sancetta, Alessio, 2013. "Weak conditions for shrinking multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 285-300.
    241. 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.
    242. Aiolfi Marco & Capistrán Carlos & Timmermann Allan, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
    243. Markiewicz, Agnieszka & Pick, Andreas, 2014. "Adaptive learning and survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 685-707.
    244. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021. "Smooth Robust Multi-Horizon Forecasts," Economics Papers 2021-W01, Economics Group, Nuffield College, University of Oxford.
    245. Baumann, Ursel & Darracq Pariès, Matthieu & Westermann, Thomas & Riggi, Marianna & Bobeica, Elena & Meyler, Aidan & Böninghausen, Benjamin & Fritzer, Friedrich & Trezzi, Riccardo & Jonckheere, Jana & , 2021. "Inflation expectations and their role in Eurosystem forecasting," Occasional Paper Series 264, European Central Bank.
    246. Melo, Luis F. & Loaiza, Rubén A. & Villamizar-Villegas, Mauricio, 2016. "Bayesian combination for inflation forecasts: The effects of a prior based on central banks’ estimates," Economic Systems, Elsevier, vol. 40(3), pages 387-397.
    247. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    248. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2021. "FX market volatility modelling: Can we use low-frequency data?," Finance Research Letters, Elsevier, vol. 40(C).
    249. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
    250. Afees A. Salisu & Ibrahim D. Raheem & Umar B. Ndako, 2017. "A sectoral analysis of asymmetric nexus between oil and stock," Working Papers 033, Centre for Econometric and Allied Research, University of Ibadan.
    251. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    252. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    253. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    254. 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.
    255. Massimiliano Giacalone, 2022. "Optimal forecasting accuracy using Lp-norm combination," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 187-230, August.
    256. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    257. Dress, Korbinian & Lessmann, Stefan & von Mettenheim, Hans-Jörg, 2018. "Residual value forecasting using asymmetric cost functions," International Journal of Forecasting, Elsevier, vol. 34(4), pages 551-565.
    258. Furukawa, Kakuho & Hisano, Ryohei & Minoura, Yukio & Yagi, Tomoyuki, 2024. "A nowcasting model of industrial production using alternative data and machine learning approaches," Japan and the World Economy, Elsevier, vol. 71(C).
    259. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    260. Aristidou, Chrystalleni & Lee, Kevin & Shields, Kalvinder, 2022. "Fundamentals, regimes and exchange rate forecasts: Insights from a meta exchange rate model," Journal of International Money and Finance, Elsevier, vol. 123(C).
    261. Andreini, Paolo & Hasenzagl, Thomas & Reichlin, Lucrezia & Senftleben-König, Charlotte & Strohsal, Till, 2023. "Nowcasting German GDP: Foreign factors, financial markets, and model averaging," International Journal of Forecasting, Elsevier, vol. 39(1), pages 298-313.
    262. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    263. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. van Dijk & Marno Verbeek, 2009. "Forecast accuracy and economic gains from Bayesian model averaging using time varying weight," Working Paper 2009/10, Norges Bank.
    264. Chan, Felix & Pauwels, Laurent L., 2018. "Some theoretical results on forecast combinations," International Journal of Forecasting, Elsevier, vol. 34(1), pages 64-74.
    265. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    266. Filipa Fernandes & Charalampos Stasinakis & Zivile Zekaite, 2019. "Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery," Annals of Operations Research, Springer, vol. 282(1), pages 87-118, November.
    267. 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.
    268. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    269. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    270. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.
    271. Karsten R. Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2009. "Evaluating ensemble density combination - forecasting GDP and inflation," Working Paper 2009/19, Norges Bank.
    272. Frank A.G. den Butter & Pieter W. Jansen, 2008. "Beating the Random Walk: a Performance Assessment of Long-term Interest Rate Forecasts," Tinbergen Institute Discussion Papers 08-102/3, Tinbergen Institute.
    273. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    274. Mr. Olivier Coibion & Mr. Yuriy Gorodnichenko, 2012. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," IMF Working Papers 2012/296, International Monetary Fund.
    275. Verena Monschang & Bernd Wilfling, 2025. "Formalizing a Postprocessing Procedure for Linear–Convex Combination Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1280-1293, July.
    276. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
    277. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    278. 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.
    279. Dahlquist, Magnus & Hasseltoft, Henrik, 2020. "Economic momentum and currency returns," Journal of Financial Economics, Elsevier, vol. 136(1), pages 152-167.
    280. Alexander Frenkel A. & Natalia Volkova N. & Anton Surkov A. & Александр Френкель Адольфович & Наталия Волкова Николаевна & Антон Сурков Александрович, 2017. "Повышение точности прогнозирования интегральных показателей на основе объединения прогнозов // Improving the Prediction Accuracy of the Integral Indicators by the Means of 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. 21(5), pages 118-127.
    281. Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024. "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 123-135.
    282. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    283. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
    284. Darvas, Zsolt & Schepp, Zoltán, 2024. "Exchange rates and fundamentals: Forecasting with long maturity forward rates," Journal of International Money and Finance, Elsevier, vol. 143(C).
    285. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    286. Gao, Huicai & Li, Hengyun & Zhang, Chen Jason, 2025. "Time and feature varying tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 112(C).
    287. Ding, Kexin & Karali, Berna, 2019. "Basis Forecasting Performance of Composite Models: An Application to Corn and Soybean Markets," 2019 Conference, April 15-16, 2019, Minneapolis, Minnesota 309625, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    288. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    289. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    290. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    291. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    292. Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
    293. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
    294. 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.
    295. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    296. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    297. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    298. Anwen Yin, 2024. "Predictive model averaging with parameter instability and heteroskedasticity," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 418-442, April.
    299. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
    300. Khoo, Zhi De & Ng, Kok Haur & Koh, You Beng & Ng, Kooi Huat, 2024. "Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    301. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2024. "Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1275-1301.
    302. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    303. Valentina Corradi & Sainan Jin & Norman R. Swanson, 2023. "Robust forecast superiority testing with an application to assessing pools of expert forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 596-622, June.
    304. Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    305. Li, Jiahan & Tsiakas, Ilias, 2017. "Equity premium prediction: The role of economic and statistical constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 56-75.
    306. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    307. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    308. 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.
    309. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    310. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    311. Raval, Devesh & Rosenbaum, Ted & Wilson, Nathan E., 2021. "How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments," Journal of Health Economics, Elsevier, vol. 78(C).
    312. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).
    313. Jonas Nygaard Eriksen, 2015. "Expected Business Conditions and Bond Risk Premia," CREATES Research Papers 2015-44, Department of Economics and Business Economics, Aarhus University.
    314. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    315. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    316. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    317. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    318. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
    319. 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.
    320. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    321. Stefano Cassella & Huseyin Gulen, 2018. "Extrapolation Bias and the Predictability of Stock Returns by Price-Scaled Variables," The Review of Financial Studies, Society for Financial Studies, vol. 31(11), pages 4345-4397.
    322. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    323. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    324. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    325. Björn Fastrich & Peter Winker, 2014. "Combining Forecasts with Missing Data: Making Use of Portfolio Theory," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 127-152, August.
    326. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    327. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    328. Michiel de Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," Working Paper 2010/01, Norges Bank.
    329. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    330. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    331. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    332. Li, Gang & Wu, Doris Chenguang & Zhou, Menglin & Liu, Anyu, 2019. "The combination of interval forecasts in tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 363-378.
    333. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    334. Dominik Wolff & Ulrich Neugebauer, 2019. "Tree-based machine learning approaches for equity market predictions," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 273-288, July.
    335. 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.
    336. Konstantin A. Kholodilin & Boriss Siliverstovs, 2015. "Think national, forecast local: A case study of 71 German urban housing markets," KOF Working papers 15-372, KOF Swiss Economic Institute, ETH Zurich.
    337. Hoornweg, V., 2013. "Some Tools for Robustifying Econometric Analyses," Econometric Institute Research Papers 50163, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    338. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    339. Møller, Jan Kloppenborg & Nystrup, Peter & Madsen, Henrik, 2024. "Likelihood-based inference in temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 40(2), pages 515-531.
    340. Tsiakas, Ilias & Li, Jiahan & Zhang, Haibin, 2020. "Equity premium prediction and the state of the economy," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 75-95.
    341. Lansing, Kevin J., 2021. "Endogenous forecast switching near the zero lower bound," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 153-169.
    342. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
    343. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    344. Laurent Ferrara & Clément Marsilli & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.
    345. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    346. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    347. Kolasa, Marcin & Rubaszek, Michał, 2015. "Forecasting using DSGE models with financial frictions," International Journal of Forecasting, Elsevier, vol. 31(1), pages 1-19.
    348. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
    349. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    350. De Rezende, Rafael B., 2017. "The interest rate effects of government bond purchases away from the lower bound," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 165-186.
    351. Andrew B. Martinez & Alexander D. Schibuola & David Beckworth, 2025. "The Reliability of the Nominal GDP Expectations Gap," Working Papers 2025-004, The George Washington University, The Center for Economic Research.
    352. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    353. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, EconSciences Journals, vol. 3(3), pages 536-559, September.

Articles

  1. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    See citations under working paper version above.
  2. Antonio Noriega & Carlos Capistrán & Manuel Ramos-Francia, 2013. "On the dynamics of inflation persistence around the world," Empirical Economics, Springer, vol. 44(3), pages 1243-1265, June.

    Cited by:

    1. 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.
    2. Ibrahim Abdulhamid Danlami, 2019. "Inflation Persistence in the West African Commonwealth Countries," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 5(3), pages 80-89, September.
    3. Evžen Kocenda & Balázs Varga & Evžen Kočenda, 2017. "The Impact of Monetary Strategies on Inflation Persistence," CESifo Working Paper Series 6306, CESifo.
    4. Yaya, OlaOluwa S, 2017. "Another Look at the Stationarity of Inflation rates in OECD countries: Application of Structural break-GARCH-based unit root tests," MPRA Paper 88769, University Library of Munich, Germany.
    5. Robinson Kruse & Daniel Ventosa-Santaulària & Antonio E. Noriega, 2013. "Changes in persistence, spurious regressions and the Fisher hypothesis," CREATES Research Papers 2013-11, Department of Economics and Business Economics, Aarhus University.
    6. Edward N. Gamber & Jeffrey P. Liebner & Julie K. Smith, 2016. "Inflation persistence: revisited," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 25-44.
    7. Antonio Noriega & Carlos Capistrán & Manuel Ramos-Francia, 2013. "On the dynamics of inflation persistence around the world," Empirical Economics, Springer, vol. 44(3), pages 1243-1265, June.
    8. Fernando Zarzosa Valdivia, 2020. "Inflation Dynamics in the ABC (Argentina, Brazil and Chile) countries," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 3(2), pages 77-99, Octubre.
    9. Hamidreza Ghorbani Dastgerdi, 2020. "Inflation Theories and Inflation Persistence in Iran," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 23(2), pages 1-20, November.
    10. Lenin Arango-Castillo & Francisco J. Martínez-Ramírez & María José Orraca, 2024. "Univariate Measures of Persistence: A Comparative Analysis," Working Papers 2024-11, Banco de México.
    11. Arize, Augustine C. & Malindretos, John, 2012. "Nonstationarity and nonlinearity in inflation rate: Some further evidence," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 224-234.
    12. Jiranyakul, Komain, 2015. "Exchange Rate Regimes and Persistence of Inflation in Thailand," MPRA Paper 66203, University Library of Munich, Germany.
    13. Garcés Díaz Daniel, 2017. "Explaining Inflation with a Classical Dichotomy Model and Switching Monetary Regimes: Mexico 1932-2013," Working Papers 2017-20, Banco de México.
    14. Sidaoui José Julián & Capistrán Carlos & Chiquiar Daniel & Ramos Francia Manuel, 2009. "A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico," Working Papers 2009-14, Banco de México.
    15. Capistrán Carlos & Ibarra-Ramírez Raúl & Ramos Francia Manuel, 2011. "Exchange Rate Pass-Through to Prices: Evidence from Mexico," Working Papers 2011-12, Banco de México.
    16. Mohitosh Kejriwal & Xuewen Yu & Pierre Perron, 2020. "Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 676-690, September.
    17. Jorge Belaire-Franch, 2019. "A note on the evidence of inflation persistence around the world," Empirical Economics, Springer, vol. 56(5), pages 1477-1487, May.
    18. Geronikolaou, George & Spyromitros, Eleftherios & Tsintzos, Panagiotis, 2016. "Inflation persistence: The path of labor market structural reforms," Economic Modelling, Elsevier, vol. 58(C), pages 317-322.
    19. Gabriel Rodríguez & Luis Surco, 2024. "Modeling the trend, persistence, and volatility of inflation in Pacific Alliance countries: an empirical application using a model with inflation bands," Documentos de Trabajo / Working Papers 2024-533, Departamento de Economía - Pontificia Universidad Católica del Perú.
    20. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Targeting: New Evidence from Fractional Integration and Cointegration," Working papers 2016-08, University of Connecticut, Department of Economics.

  3. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639. See citations under working paper version above.
  4. Capistrán, Carlos & Ibarra, Raúl & Ramos, Manuel, 2012. "El traspaso de movimientos del tipo de cambio a los precios. Un análisis para la economía mexicana," El Trimestre Económico, Fondo de Cultura Económica, vol. 79(316), pages 813-838, octubre-d.

    Cited by:

    1. Angeles Galvan Daniel & Cortés Espada Josué Fernando & Sámano Daniel, 2019. "Evolution and Characteristics of the Exchange Rate Pass Through to Prices in Mexico," Working Papers 2019-10, Banco de México.
    2. Samuel Martinez & Daniel Ventosa-Santaulària, 2025. "In Search of the Lost Exchange Rate Pass-Through in Mexico," AMSE Working Papers 2536, Aix-Marseille School of Economics, France.
    3. Solórzano, Diego, 2023. "Heterogeneous exchange rate pass-through in Mexico: What drives it?," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(4).
    4. Jorge González & Eduardo Saucedo, 2018. "Traspaso Depreciación-Inflación en México: Análisis de Precios al Consumidor y Productor," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 13(4), pages 525-545, Octubre-D.
    5. Cortés Espada Josué Fernando, 2013. "An estimation of the exchange rate pass-through to prices in Mexico," Working Papers 2013-02, Banco de México.
    6. Calderón Villarreal, Cuauhtémoc & Cuevas, Víctor M., 2019. "Industrial growth and consumer goods inflation in Mexico: an econometric analysis," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.
    7. Aguilar-Argaez Ana María & Elizondo Rocío & Roldán-Peña Jessica, 2016. "Break-Even-Inflation's Decomposition in Mexico," Working Papers 2016-22, Banco de México.
    8. Tobal Martín & Yslas Renato, 2016. "Two Models of FX Market Interventions: The Cases of Brazil and Mexico," Working Papers 2016-14, Banco de México.
    9. Javier Garcia-Cicco & Markus Kirchner & Julio Carrillo & Diego Rodríguez & Fernando Perez & Rocío Gondo & Carlos Montoro & Roberto Chang, 2017. "Financial and real shocks and the effectiveness of monetary and macroprudential policies in Latin American countries," BIS Working Papers 668, Bank for International Settlements.
    10. Eduardo Saucedo & Jorge Gonzalez, 2021. "Exchange Rate Pass-Through to Prices in Mexico: A Study of the Main Border and Non-Border Cities," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(2), pages 1-24, Abril - J.
    11. José Romero, 2013. "¿Es posible utilizar el tipo de cambio para hacer más competitiva la economía mexicana?," Serie documentos de trabajo del Centro de Estudios Económicos 2013-10, El Colegio de México, Centro de Estudios Económicos.
    12. Reyna Vergara González & Elías Eduardo Gutiérrez Alva, 2014. "Evaluación del cumplimiento de los objetivos de inflación y el papel de las expectativas: evidencia para México, 1995-2012," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-32, November.
    13. Covarrubias, Enrique & Hernández-del-Valle, Gerardo, 2016. "Inflation expectations derived from a portfolio model," MPRA Paper 69489, University Library of Munich, Germany.
    14. Donayre, Luiggi & Panovska, Irina, 2016. "State-dependent exchange rate pass-through behavior," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 170-195.
    15. Eduardo Saucedo & Jorge González, 2019. "Efecto de los precios del petróleo en la actividad económica sectorial de México. Análisis para el periodo 2002-2018," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 221-243, Abril-Jun.
    16. Kochen Federico & Sámano Daniel, 2016. "Price-Setting and Exchange Rate Pass-Through in the Mexican Economy: Evidence from CPI Micro Data," Working Papers 2016-13, Banco de México.
    17. André, Marine Charlotte & Espidio, Sebastián Medina, 2024. "Optimal robust monetary policy in a small open emerging-market economy," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    18. Cerqueira, Luiz Fernando & Feijó, Carmem Aparecida & Gonçalves, Thallis Macedo de Assis, 2023. "Pass-through of exchange rate shocks in Brazil as a small open economy," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), April.
    19. Tobal Martín, 2017. "Prudential Regulation, Currency Mismatches and Exchange Rates in Latin America and the Caribbean," Working Papers 2017-21, Banco de México.

  5. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.

    Cited by:

    1. Medel, Carlos A., 2012. "How informative are in-sample information criteria to forecasting? the case of Chilean GDP," MPRA Paper 35949, University Library of Munich, Germany.
    2. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    3. Carrera, Cesar & Ledesma, Alan, 2015. "Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos," Working Papers 2015-003, Banco Central de Reserva del Perú.
    4. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
    5. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    6. Cortés Espada Josué Fernando, 2013. "An estimation of the exchange rate pass-through to prices in Mexico," Working Papers 2013-02, Banco de México.
    7. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.
    8. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    9. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
    10. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    11. Sakai Ando & Taehoon Kim, 2024. "Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(4), pages 1386-1410, December.
    12. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    13. Pablo Pincheira & Carlos A. Medel, 2012. "Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis," Working Papers Central Bank of Chile 677, Central Bank of Chile.
    14. Ibarra-Ramírez Raúl, 2021. "The Yield Curve as a Predictor of Economic Activity in Mexico: The Role of the Term Premium," Working Papers 2021-07, Banco de México.
    15. Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 50, Peruvian Economic Association.
    16. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    17. Tomokaze Shiratori & Ken Kobayashi & Yuichi Takano, 2020. "Prediction of hierarchical time series using structured regularization and its application to artificial neural networks," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
    18. Robinson Durán & Evelyn Garrido & Carolina Godoy & Juan de Dios Tena, 2012. "Predicción de la inflación en México con modelos desagregados por componente," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 133-167.
    19. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    20. Li, Han & Chen, Hua, 2024. "Hierarchical mortality forecasting with EVT tails: An application to solvency capital requirement," International Journal of Forecasting, Elsevier, vol. 40(2), pages 549-563.
    21. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    22. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.

  6. Capistrán, Carlos & López-Moctezuma, Gabriel, 2010. "Las expectativas macroeconómicas de los especialistas. Una evaluación de pronósticos de corto plazo en México," El Trimestre Económico, Fondo de Cultura Económica, vol. 77(306), pages 275-312, abril-jun.

    Cited by:

    1. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    2. Pablo Pincheira B. & Nicolás Fernández, 2011. "Jaque Mate a las Proyecciones de Consenso," Working Papers Central Bank of Chile 630, Central Bank of Chile.

  7. Carlos Capistr¡N & Manuel Ramos-Francia, 2010. "Does Inflation Targeting Affect the Dispersion of Inflation Expectations?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 113-134, February.
    See citations under working paper version above.
  8. Carlos Capistr¡N & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2-3), pages 365-396, March.
    See citations under working paper version above.
  9. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    See citations under working paper version above.
  10. Josué Cortés Espada & Carlos Capistrán & Manuel Ramos-Francia & Alberto Torres, 2009. "An empirical analysis of the mexican term structure of interest rates," Economics Bulletin, AccessEcon, vol. 29(3), pages 2300-2313.

    Cited by:

    1. Jens H. E. Christensen & Eric Fischer & Patrick Shultz, 2019. "Bond Flows and Liquidity: Do Foreigners Matter?," Working Paper Series 2019-08, Federal Reserve Bank of San Francisco.
    2. García-Verdú Santiago & Ramos Francia Manuel & Sánchez-Martínez Manuel, 2018. "TIIE-28 Swaps as Risk-Adjusted Forecasts of Monetary Policy in Mexico," Working Papers 2018-16, Banco de México.
    3. Christensen, Jens H.E. & Fischer, Eric & Shultz, Patrick J., 2021. "Bond flows and liquidity: Do foreigners matter?," Journal of International Money and Finance, Elsevier, vol. 117(C).
    4. García-Verdú Santiago, 2011. "On the Term Structure of Interest Rates of the Mexican Government," Working Papers 2011-18, Banco de México.
    5. Remy Beauregard & Jens H. E. Christensen & Eric Fischer & Simon Zhu, 2021. "Inflation Expectations and Risk Premia in Emerging Bond Markets: Evidence from Mexico," Staff Reports 961, Federal Reserve Bank of New York.
    6. Ibarra-Ramírez Raúl, 2021. "The Yield Curve as a Predictor of Economic Activity in Mexico: The Role of the Term Premium," Working Papers 2021-07, Banco de México.
    7. Bulíř, Aleš & Vlček, Jan, 2021. "Monetary transmission: Are emerging market and low-income countries different?," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 95-108.
    8. Alba Carlos & Cuadra Gabriel & Ibarra Raúl, 2023. "Effects of the Extraordinary Measures Implemented by Banco de México during the COVID-19 Pandemic on Financial Conditions," Working Papers 2023-03, Banco de México.
    9. Cortés Espada Josué Fernando & Ramos Francia Manuel, 2008. "An Affine Model of the Term Structure of Interest Rates in Mexico," Working Papers 2008-09, Banco de México.

  11. Carlos Capistrán & Manuel Ramos‐Francia, 2009. "Inflation Dynamics In Latin America," Contemporary Economic Policy, Western Economic Association International, vol. 27(3), pages 349-362, July.
    See citations under working paper version above.
  12. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November. See citations under working paper version above.
  13. Capistran, Carlos, 2006. "On comparing multi-horizon forecasts," Economics Letters, Elsevier, vol. 93(2), pages 176-181, November.

    Cited by:

    1. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.
    2. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    3. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
    4. Capistrán Carlos, 2007. "Optimality Tests for Multi-Horizon Forecasts," Working Papers 2007-14, Banco de México.
    5. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.
    6. Capistrán Carlos & Constandse Christian & Ramos Francia Manuel, 2009. "Using Seasonal Models to Forecast Short-Run Inflation in Mexico," Working Papers 2009-05, Banco de México.

Chapters

  1. Jose Sidaoui & Carlos Capistran & Daniel Chiquiar & Manuel Ramos-Francia, 2010. "On the predictive content of the PPI on CPI inflation: the case of Mexico," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 249-257, Bank for International Settlements.

    Cited by:

    1. Mohd, Rafede & Masih, Mansur, 2018. "Testing the asymmetric and lead-lag relationship between CPI and PPI: an application of the ARDL and NARDL approaches," MPRA Paper 112500, University Library of Munich, Germany.
    2. Gabriela Antosova & Helmuth Yesid Arias Gomez, 2024. "Recent Long-Term Management of Relation Across Czech- Republic Price Indices and Exchange Rates," European Research Studies Journal, European Research Studies Journal, vol. 0(Special B), pages 129-146.
    3. Víctor Quinde Rosales & Rina Bucaram-Leverone, 2017. "Relación de causalidad entre el índice de precios al productor y el índice de precios al consumidor: Caso Ecuador," Revista Actualidad Económica, Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Economía y Finanzas, vol. 27(93), pages 5-14, Sept-Dic.
    4. Viviana A. Alfonso-Corredor & Enrique Montes-Uribe & María A. Prieto-Sánchez & Héctor M. Zárate-Solano, 2019. "Determinantes y evolución de los precios y cantidades de las principales exportaciones agrícolas de Colombia diferentes al café," Borradores de Economia 1100, Banco de la Republica de Colombia.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.