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Michael McCracken

Citations

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

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Michael W. McCracken, 2012. "Consistent testing for structural change at the ends of the sample," Working Papers 2012-029, Federal Reserve Bank of St. Louis.

    Mentioned in:

    1. My "Must Read" List
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-09-27 06:33:00

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Todd E. Clark & Michael W. McCracken, 2011. "Reality Checks and Comparisons of Nested Predictive Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 53-66, February.

    Mentioned in:

    1. > Econometrics > Forecasting

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. 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.

    Mentioned in:

    1. Averaging forecasts from VARs with uncertain instabilities (Journal of Applied Econometrics 2010) in ReplicationWiki ()

Working papers

  1. Silvia Goncalves & Michael W. McCracken & Yongxu Yao, 2023. "Bootstrapping out-of-sample predictability tests with real-time data," Working Papers 2023-029, Federal Reserve Bank of St. Louis, revised 03 Sep 2024.

    Cited by:

    1. Buccheri, Giuseppe & Renò, Roberto & Vocalelli, Giorgio, 2025. "Taking advantage of biased proxies for forecast evaluation," Journal of Econometrics, Elsevier, vol. 251(C).

  2. Aaron Amburgey & Michael W. McCracken, 2023. "Growth-at-Risk is Investment-at-Risk," Working Papers 2023-020, Federal Reserve Bank of St. Louis, revised 14 Aug 2025.

    Cited by:

    1. Zhou, Yang, 2024. "Benefits and costs: The impact of capital control on growth-at-risk in China," International Review of Financial Analysis, Elsevier, vol. 93(C).

  3. Aaron Amburgey & Michael W. McCracken, 2022. "On the Real-Time Predictive Content of Financial Conditions Indices for Growth," Working Papers 2022-003, Federal Reserve Bank of St. Louis, revised 03 Jun 2022.

    Cited by:

    1. Hilde C. Bjørnland & Roberto Casarin & Marco Lorusso & Francesco Ravazzolo, 2023. "Fiscal Policy Regimes in Resource-Rich Economies," Working Papers No 13/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.
    4. Efrem Castelnuovo & Lorenzo Mori, 2022. "Uncertainty, Skewness, and the Business Cycle through the MIDAS Lens," CESifo Working Paper Series 10062, CESifo.
    5. Ta-Chung Chi & Ting-Han Fan & Raffaele M. Ghigliazza & Domenico Giannone & Zixuan & Wang, 2025. "Macroeconomic Forecasting and Machine Learning," Papers 2510.11008, arXiv.org.
    6. 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.
    7. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    8. Tobias Adrian & Hongqi Chen & Max-Sebastian Dov`i & Ji Hyung Lee, 2025. "Machine-learning Growth at Risk," Papers 2506.00572, arXiv.org.
    9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024. "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.

  4. Michael W. McCracken & Serena Ng, 2020. "FRED-QD: A Quarterly Database for Macroeconomic Research," Working Papers 2020-005, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
    2. Leu, Shawn C.-Y. & Robertson, Mari L., 2021. "Mortgage credit volumes and monetary policy after the Great Recession," Economic Modelling, Elsevier, vol. 94(C), pages 483-500.
    3. Yongxia Zhang & Qi Wang & Maozai Tian, 2022. "Smoothed Quantile Regression with Factor-Augmented Regularized Variable Selection for High Correlated Data," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    4. Marco Brianti & Vito Cormun, 2023. "Expectation-Driven Boom-Bust Cycles," Working Papers 2023-04, University of Alberta, Department of Economics.
    5. Tiziana Assenza & Fabrice Collard & Patrick Fève & Stefanie Huber, 2024. "From Buzz to Bust: How Fake News Shapes the Business Cycle," ECONtribute Discussion Papers Series 287, University of Bonn and University of Cologne, Germany.
    6. Florian Huber & Gary Koop, 2021. "Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions," Papers 2107.07804, arXiv.org.
    7. Masud Alam, 2021. "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return," Papers 2107.10455, arXiv.org.
    8. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org, revised Jan 2026.
    9. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
    10. Jan Ditzen & Ovidijus Stauskas, 2025. "On Selection of Cross-Section Averages in Non-stationary Environments," Papers 2505.08615, arXiv.org, revised Oct 2025.
    11. Soroosh Soofi-Siavash & Emanuel Moench, 2021. "What Moves Treasury Yields?," Bank of Lithuania Working Paper Series 88, Bank of Lithuania.
    12. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    13. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
      • Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    14. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    15. Chang, Yoosoon & Kwak, Boreum & Qiu, Shi, 2021. "U.S. monetary and fiscal policy regime changes and their interactions," IWH Discussion Papers 12/2021, Halle Institute for Economic Research (IWH).
    16. Gomez-Gonzalez, Jose E. & Hirs-Garzón, Jorge & Uribe, Jorge M., 2022. "Interdependent capital structure choices and the macroeconomy," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    17. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    18. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    19. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    20. Lin, Jiahe & Michailidis, George, 2024. "A multi-task encoder-dual-decoder framework for mixed frequency data prediction," International Journal of Forecasting, Elsevier, vol. 40(3), pages 942-957.
    21. Zhenzhong Wang & Zhengyuan Zhu & Cindy Yu, 2020. "Variable Selection in Macroeconomic Forecasting with Many Predictors," Papers 2007.10160, arXiv.org.
    22. Korobilis, Dimitris & Koop, Gary, 2020. "Bayesian dynamic variable selection in high dimensions," MPRA Paper 100164, University Library of Munich, Germany.
    23. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    24. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    25. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    26. Jørgensen, Peter L. & Ravn, Søren H., 2022. "The inflation response to government spending shocks: A fiscal price puzzle?," European Economic Review, Elsevier, vol. 141(C).
    27. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    28. Ryan Cumings-Menon & Minchul Shin, 2020. "Probability Forecast Combination via Entropy Regularized Wasserstein Distance," Working Papers 20-31/R, Federal Reserve Bank of Philadelphia.
    29. Liao, Jun & Zou, Guohua & Gao, Yan & Zhang, Xinyu, 2021. "Model averaging prediction for time series models with a diverging number of parameters," Journal of Econometrics, Elsevier, vol. 223(1), pages 190-221.
    30. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2025. "Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables," CIRANO Working Papers 2025s-15, CIRANO.
    31. Eugster, Patrick & Uhl, Matthias W., 2024. "Forecasting inflation using sentiment," Economics Letters, Elsevier, vol. 236(C).
    32. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    33. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    34. Szydlo, Jan, 2023. "Forecasting Credit Dynamics : VAR, VECM or modern Factor-Augmented VAR approach?," Warwick-Monash Economics Student Papers 63, Warwick Monash Economics Student Papers.
    35. Martinoli, Mario & Moneta, Alessio & Pallante, Gianluca, 2024. "Calibration and validation of macroeconomic simulation models by statistical causal search," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    36. Paul Carrillo-Maldonado & Javier Díaz-Cassou & Miguel Flores, 2023. "What are the main variables that influence the dynamics of Ecuador’s sovereign risk?," Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2158009-215, December.
    37. Ziwei Mei & Zhentao Shi & Peter C. B. Phillips, 2022. "The boosted HP filter is more general than you might think," Cowles Foundation Discussion Papers 2348, Cowles Foundation for Research in Economics, Yale University.
    38. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
    39. Xiao Huang, 2023. "Composite Quantile Factor Model," Papers 2308.02450, arXiv.org, revised Nov 2024.
    40. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    41. Martin Iseringhausen & Konstantinos Theodoridis, 2025. "A survey-based measure of asymmetric macroeconomic risk in the euro area," Working Papers 68, European Stability Mechanism, revised 11 Feb 2025.
    42. Ahmed, M. Iqbal & Farah, Quazi Fidia, 2022. "On the macroeconomic effects of news about innovations of information technology," Journal of Macroeconomics, Elsevier, vol. 71(C).
    43. Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org, revised Nov 2024.
    44. Herath, H.M. Wiranthe B. & Samadi, S. Yaser, 2025. "Scaled envelope models for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 205(C).
    45. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    46. Chen, Jia & Li, Degui & Li, Yu-Ning & Linton, Oliver, 2025. "Estimating time-varying networks for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 249(PC).
    47. Funovits, Bernd, 2024. "Identifiability and estimation of possibly non-invertible SVARMA Models: The normalised canonical WHF parametrisation," Journal of Econometrics, Elsevier, vol. 241(2).
    48. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    49. Christian Bayer & Luis Calderon & Moritz Kuhn, 2025. "Distributional Dynamics," CRC TR 224 Discussion Paper Series crctr224_2025_625, University of Bonn and University of Mannheim, Germany.
    50. Carrillo-Maldonado, Paul & Díaz-Cassou, Javier, 2023. "An anatomy of external shocks in the Andean region," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    51. Moramarco, Graziano, 2024. "Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States," International Journal of Forecasting, Elsevier, vol. 40(2), pages 777-795.
    52. Creal, Drew & Kim, Jaeho, 2024. "Bayesian estimation of cluster covariance matrices of unknown form," Journal of Econometrics, Elsevier, vol. 241(1).
    53. Huang, Feiqing & Lu, Kexin & Zheng, Yao & Li, Guodong, 2025. "Supervised factor modeling for high-dimensional linear time series," Journal of Econometrics, Elsevier, vol. 249(PB).
    54. Anna Mikusheva & Mikkel S{o}lvsten, 2023. "Linear Regression with Weak Exogeneity," Papers 2308.08958, arXiv.org, revised Jan 2024.
    55. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    56. Damiano Di Francesco & Omar Pietro Carnevale, 2025. "Are Hysteresis Effects Nonlinear?," LEM Papers Series 2025/32, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    57. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2020. "Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US," University of Göttingen Working Papers in Economics 404, University of Goettingen, Department of Economics.
    58. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2024. "Averaging impulse responses using prediction pools," Journal of Monetary Economics, Elsevier, vol. 146(C).
    59. 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).
    60. Goulet Coulombe, Philippe, 2025. "Time-varying parameters as ridge regressions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 982-1002.
    61. Matteo Barigozzi & Claudio Lissona & Lorenzo Tonni, 2024. "Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy," Papers 2410.05082, arXiv.org, revised Nov 2025.
    62. Marc Anderes, 2023. "Housing demand shocks and households’ balance sheets," Empirical Economics, Springer, vol. 65(6), pages 2711-2749, December.
    63. Martin Iseringhausen & Ivan Petrella & Konstantinos Theodoridis, 2022. "Aggregate skewness and the business cycle," Working Papers 53, European Stability Mechanism.
    64. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
    65. Tobias Adrian & Hongqi Chen & Max-Sebastian Dov`i & Ji Hyung Lee, 2025. "Machine-learning Growth at Risk," Papers 2506.00572, arXiv.org.
    66. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Papers 2305.16827, arXiv.org.
    67. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    68. Dimitris Korobilis & Maximilian Schroder, 2022. "Probabilistic Quantile Factor Analysis," Papers 2212.10301, arXiv.org, revised Aug 2024.
    69. Alessandro Morico & Ovidijus Stauskas, 2025. "Robust Tests for Factor-Augmented Regressions with an Application to the novel EA-MD-QD Dataset," Papers 2504.08455, arXiv.org, revised Nov 2025.
    70. Ying Lun Cheung, 2024. "Identification of Time-Varying Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 76-94, January.
    71. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2020. "Point and Density Forecasting of Macroeconomic and Financial Uncertainties of the United States," Working Papers 202058, University of Pretoria, Department of Economics.
    72. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    73. Joaqui-Barandica, Orlando & Manotas-Duque, Diego F. & Uribe, Jorge M., 2022. "Commonality, macroeconomic factors and banking profitability," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    74. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    75. Xin Sheng & Rangan Gupta & Qiang Ji, 2023. "The Effects of Disaggregate Oil Shocks on Aggregate Expected Skewness of the United States," Working Papers 202302, University of Pretoria, Department of Economics.
    76. Iania, Leonardo & Algieri, Bernardina & Leccadito, Arturo, 2022. "Forecasting total energy’s CO2 emissions," LIDAM Discussion Papers LFIN 2022003, Université catholique de Louvain, Louvain Finance (LFIN).
    77. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    78. Zhu, Felix & Dong, Yumo & Huang, Fei, 2025. "Data-rich economic forecasting for actuarial applications," Insurance: Mathematics and Economics, Elsevier, vol. 124(C).
    79. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
    80. Direye, Eli & Khemraj, Tarron, 2021. "Central bank securities and FX market intervention in a developing economy," MPRA Paper 111533, University Library of Munich, Germany, revised 09 Aug 2021.
    81. Marc Anderes, 2021. "Housing Demand Shocks and Households Balance Sheets," KOF Working papers 21-492, KOF Swiss Economic Institute, ETH Zurich.
    82. Aromi, J. Daniel & Clements, Adam, 2021. "Facial expressions and the business cycle," Economic Modelling, Elsevier, vol. 102(C).
    83. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Feb 2025.

  5. Michael W. McCracken, 2019. "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers 2019-11, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    2. Jiang, He & Dong, Yawei & Dong, Yao & Wang, Jianzhou, 2025. "Probabilistic electricity price forecasting by integrating interpretable model," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    3. Florens Odendahl & Tatevik Sekhposyan & Barbara Rossi, 2021. "Evaluating Forecast Performance with State Dependence," Working Papers 1295, Barcelona School of Economics.
    4. Jörg Breitung & Malte Knüppel, 2021. "How far can we forecast? Statistical tests of the predictive content," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 369-392, June.
    5. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

  6. Michael W. McCracken & Joseph McGillicuddy & Michael T. Owyang, 2019. "Binary Conditional Forecasts," Working Papers 2019-029, Federal Reserve Bank of St. Louis, revised Apr 2021.

    Cited by:

    1. Chan, Joshua C.C. & Pettenuzzo, Davide & Poon, Aubrey & Zhu, Dan, 2025. "Conditional forecasts in large Bayesian VARs with multiple equality and inequality constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 173(C).

  7. Michael W. McCracken, 2019. "Diverging Tests of Equal Predictive Ability," Working Papers 2019-018, Federal Reserve Bank of St. Louis, revised 09 Mar 2020.

    Cited by:

    1. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    2. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    3. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Coroneo, Laura & Iacone, Fabrizio, 2025. "Testing for equal predictive accuracy with strong dependence," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1073-1092.
    5. Filip Stanek, 2021. "Optimal Out-of-Sample Forecast Evaluation under Stationarity," CERGE-EI Working Papers wp712, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    6. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. David Staines, 2023. "Stochastic Equilibrium the Lucas Critique and Keynesian Economics," Papers 2312.16214, arXiv.org, revised Jun 2024.
    8. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    9. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2025. "Testing Clustered Equal Predictive Ability with Unknown Clusters," Papers 2507.14621, arXiv.org, revised Jul 2025.
    10. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).

  8. Michael W. McCracken & Joseph McGillicuddy, 2017. "An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts," Working Papers 2017-40, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    2. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
    3. Michael W. McCracken & Joseph McGillicuddy & Michael T. Owyang, 2019. "Binary Conditional Forecasts," Working Papers 2019-029, Federal Reserve Bank of St. Louis, revised Apr 2021.
    4. Paponpat Taveeapiradeecharoen & Nattapol Aunsri, 2025. "Forecasting in small open emerging economies Evidence from Thailand," Papers 2509.14805, arXiv.org.
    5. Kevin Moran & Dalibor Stevanovic & Stephane Surprenant, 2024. "Risk Scenarios and Macroeconomic Forecasts," Working Papers 24-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    6. Paponpat Taveeapiradeecharoen & Popkarn Arwatchanakarn, 2025. "Forecasting Thai inflation from univariate Bayesian regression perspective," Papers 2505.05334, arXiv.org, revised May 2025.
    7. Matteo Mogliani & Florens Odendahl, 2024. "Density forecast transformations," Papers 2412.06092, arXiv.org.
    8. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    9. Kevin Moran & Dalibor Stevanovic & Stéphane Surprenant, 2024. "Risk Scenarios and Macroeconomic Impacts: Insights for Canadian Policy," CIRANO Working Papers 2024s-03, CIRANO.
    10. Dossani, Asad, 2024. "Monetary policy and currency variance risk premia," Research in International Business and Finance, Elsevier, vol. 69(C).
    11. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.

  9. Todd E Clark & Michael W McCracken & Elmar Mertens, 2017. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," BIS Working Papers 667, Bank for International Settlements.

    Cited by:

    1. 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.
    2. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    3. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    5. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    6. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
    7. Efrem Castelnuovo, 2022. "Uncertainty Before and During COVID-19: A Survey," "Marco Fanno" Working Papers 0279, Dipartimento di Scienze Economiche "Marco Fanno".
    8. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    9. Mirela Miescu, 2019. "Uncertainty shocks in emerging economies," Working Papers 277077821, Lancaster University Management School, Economics Department.
    10. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.
    11. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    12. Yoosoon Chang & Yong-gun Kim & Boreum Kwak & Joon Y. Park, 2024. "Using Density Forecast for Growth-at-Risk to Improve Mean Forecast of GDP Growth in Korea," CAEPR Working Papers 2024-005 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    13. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    14. Barbara Rossi, 2018. "Identifying and estimating the effects of unconventional monetary policy in the data: How to do It and what have we learned?," Economics Working Papers 1641, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2020.
    15. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
    16. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.
    17. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
    18. Schick, Manuel, 2024. "Real-time Nowcasting Growth-at-Risk using the Survey of Professional Forecasters," Working Papers 0750, University of Heidelberg, Department of Economics.
    19. Weiqi Zhang & Huong Ha & Hui Ting Evelyn Gay, 2020. "Analysts’ forecasts between last consensus and earning announcement date," Journal of Financial Reporting and Accounting, Emerald Group Publishing Limited, vol. 18(4), pages 779-793, November.
    20. Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).
    21. Heinisch, Katja, 2024. "Step by step - A quarterly evaluation of EU Commission's GDP forecasts," IWH Discussion Papers 22/2024, Halle Institute for Economic Research (IWH).
    22. Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2021. "Forecasting macroeconomic risks," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1173-1191.
    23. Fabian Mendez Ramos, 2025. "Variance and skewness in density forecasts: assessing world GDP growth," Empirical Economics, Springer, vol. 68(6), pages 2897-2932, June.
    24. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    25. Ganics, Gergely & Mertens, Elmar & Clark, Todd E., 2023. "What Is the Predictive Value of SPF Point and Density Forecasts?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277622, Verein für Socialpolitik / German Economic Association.
    26. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).
    27. Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
    28. María del Carmen Ramos-Herrera & Simón Sosvilla-Rivero, 2017. "Inflation, real economic growth and unemployment expectations: An empirical analysis based on the ECB Survey of Professional Forecasters," Working Papers 17-02, Asociación Española de Economía y Finanzas Internacionales.

  10. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2015. "Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR," Working Papers 2015-030, Federal Reserve Bank of St. Louis, revised 10 Apr 2020.

    Cited by:

    1. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Using hierarchical aggregation constraints to nowcast regional economic aggregates," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-04, Economic Statistics Centre of Excellence (ESCoE).
    2. Edward S. Knotek & Saeed Zaman, 2024. "Nowcasting Inflation," Working Papers 24-06, Federal Reserve Bank of Cleveland.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    4. Berger, Tino & Morley, James & Wong, Benjamin, 2023. "Nowcasting the output gap," Journal of Econometrics, Elsevier, vol. 232(1), pages 18-34.
      • Tino Berger & James Morley & Benjamin Wong, 2020. "Nowcasting the Output Gap," CAMA Working Papers 2020-78, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Serhii Lupenko & Andrii Horkunenko, 2025. "Stochastic Model and Rhythm-Adaptive Technologies of Statistical Analysis and Forecasting of Economic Processes with Cyclic Components," Forecasting, MDPI, vol. 7(2), pages 1-26, May.
    6. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    7. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    8. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    9. Alain Hecq & Marie Ternes & Ines Wilms, 2025. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1946-1968, September.
    10. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    11. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    12. Juvonen, Petteri & Lindblad, Annika, 2025. "Nowcasting in real time: Large Bayesian vector autoregression in a test," Bank of Finland Research Discussion Papers 6/2025, Bank of Finland.
    13. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    14. Javier Sánchez García & Salvador Cruz Rambaud, 2022. "Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    15. Knotek, Edward S. & Zaman, Saeed, 2019. "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1708-1724.
    16. Anna Cole & Julian Kozlowski & Joseph Martorana, 2025. "The Dynamics of Long-Run Inflation Expectations: A Market-Based Perspective," Review, Federal Reserve Bank of St. Louis, vol. 107(13), pages 1-14, September.
    17. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2024. "Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 626-640.

  11. Silvia Goncalves & Michael W. McCracken & Benoit Perron, 2015. "Tests of Equal Accuracy for Nested Models with Estimated Factors," Working Papers 2015-25, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Massacci, Daniele & Kapetanios, George, 2024. "Forecasting in factor augmented regressions under structural change," International Journal of Forecasting, Elsevier, vol. 40(1), pages 62-76.
    3. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    4. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    5. Arabinda Basistha & Richard Startz, 2023. "Measuring Persistent Global Economic Factors with Output, Commodity Price, and Commodity Currency Data," Working Papers 23-05, Department of Economics, West Virginia University.
    6. Luiz Renato Lima & Lucas Lúcio Godeiro, 2023. "Equity‐premium prediction: Attention is all you need," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 105-122, January.
    7. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    8. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    9. Luca Margaritella & Ovidijus Stauskas, 2024. "New Tests of Equal Forecast Accuracy for Factor-Augmented Regressions with Weaker Loadings," Papers 2409.20415, arXiv.org, revised Nov 2025.
    10. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    11. Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
    12. Fosten, Jack, 2017. "Confidence intervals in regressions with estimated factors and idiosyncratic components," Economics Letters, Elsevier, vol. 157(C), pages 71-74.
    13. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    14. Michael W. McCracken, 2020. "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers 2020-050, Federal Reserve Bank of St. Louis.
    15. Tingting Cheng & Jiachen Cong & Fei Liu & Xuanbin Yang, 2025. "Binary Response Forecasting under a Factor-Augmented Framework," Papers 2507.16462, arXiv.org.
    16. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
    17. Alessandro Morico & Ovidijus Stauskas, 2025. "Robust Tests for Factor-Augmented Regressions with an Application to the novel EA-MD-QD Dataset," Papers 2504.08455, arXiv.org, revised Nov 2025.
    18. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    19. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    20. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.

  12. Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    2. Jozef Barunik & Mattia Bevilacqua & Michael Ellington, 2023. "Common Firm-level Investor Fears: Evidence from Equity Options," Papers 2309.03968, arXiv.org.
    3. Zhang, Bo & Nguyen, Bao H. & Sun, Chuanwang, 2024. "Forecasting oil prices: Can large BVARs help?," Energy Economics, Elsevier, vol. 137(C).
    4. Jean-Armand Gnagne & Kevin Moran, 2020. "Forecasting Bank Failures in a Data-Rich Environment," Working Papers 20-13, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    6. Hirano, Keisuke & Wright, Jonathan H., 2022. "Analyzing cross-validation for forecasting with structural instability," Journal of Econometrics, Elsevier, vol. 226(1), pages 139-154.
    7. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    8. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    9. Wu, Jianhong, 2019. "Detecting irrelevant variables in possible proxies for the latent factors in macroeconomics and finance," Economics Letters, Elsevier, vol. 176(C), pages 60-63.
    10. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Zhao, Junyi, 2025. "Is machine learning a necessity? A regression-based approach for stock return prediction," Journal of Empirical Finance, Elsevier, vol. 81(C).
    11. Marco Hoeberichts & Jan Willem van den End, 2024. "Detecting turning points in the inflation cycle," Working Papers 808, DNB.
    12. Guilherme Schultz Lindenmeyer & Hudson Silva Torrent, 2024. "Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 377-409, July.
    13. Moench, Emanuel & Soofi-Siavash, Soroosh, 2022. "What moves treasury yields?," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1016-1043.
    14. Miranda Gualdrón, Karen Alejandra & Poncela, Pilar & Ruiz Ortega, Esther, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    16. Joaqui-Barandica, Orlando & Oviedo-Gómez, Andres & Manotas-Duque, Diego F., 2023. "Directional predictability between interest rates and the Stoxx 600 Banks index: A quantile approach," Finance Research Letters, Elsevier, vol. 58(PA).
    17. Cormun, Vito & Ristolainen, Kim, 2024. "Exchange rate narratives," Bank of Finland Research Discussion Papers 11/2024, Bank of Finland.
    18. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    19. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    20. Pablo Montero-Manso & Rob J Hyndman, 2020. "Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality," Monash Econometrics and Business Statistics Working Papers 45/20, Monash University, Department of Econometrics and Business Statistics.
    21. Lim, Kian Guan & Nomikos, Nikos K. & Yap, Nelson, 2019. "Understanding the fundamentals of freight markets volatility," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 1-15.
    22. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    23. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
    24. H. Rad & R. Low & J. Miffre & R. Faff, 2023. "The commodity risk premium and neural networks," Post-Print hal-04322519, HAL.
    25. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2022. "Addressing COVID-19 outliers in BVARs with stochastic volatility," Discussion Papers 13/2022, Deutsche Bundesbank.
    26. Jan Ditzen & Ovidijus Stauskas, 2025. "On Selection of Cross-Section Averages in Non-stationary Environments," Papers 2505.08615, arXiv.org, revised Oct 2025.
    27. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    28. Tom Boot & Bart Keijsers, 2025. "Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections," Papers 2506.09575, arXiv.org.
    29. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    30. Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2019. "Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors," CREATES Research Papers 2019-21, Department of Economics and Business Economics, Aarhus University.
    31. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.
    32. Fan, Jianqing & Ke, Yuan & Wang, Kaizheng, 2020. "Factor-adjusted regularized model selection," Journal of Econometrics, Elsevier, vol. 216(1), pages 71-85.
    33. Goodhead, Robert & Kolb, Benedikt, 2018. "Monetary policy communication shocks and the macroeconomy," Discussion Papers 46/2018, Deutsche Bundesbank.
    34. Eleonora Granziera & Vegard H. Larsen & Greta Meggiorini & Leonardo Melosi, 2025. "Speaking of Inflation: The Influence of Fed Speeches on Expectations," CESifo Working Paper Series 11992, CESifo.
    35. He, Yong & Li, Lingxiao & Liu, Dong & Zhou, Wen-Xin, 2025. "Huber Principal Component Analysis for large-dimensional factor models," Journal of Econometrics, Elsevier, vol. 249(PB).
    36. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
    37. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
      • Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    38. Barigozzi, Matteo & Massacci, Daniele, 2025. "Modelling large dimensional datasets with Markov switching factor models," Journal of Econometrics, Elsevier, vol. 247(C).
    39. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2020. "Computationally efficient inference in large Bayesian mixed frequency VARs," Economics Letters, Elsevier, vol. 191(C).
    40. Eksi, Ozan & Onur Tas, Bedri Kamil, 2022. "Time-varying effect of uncertainty shocks on unemployment," Economic Modelling, Elsevier, vol. 110(C).
    41. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    42. Apergis, Nicholas, 2024. "COVID-19 and US females’ portfolio decisions," International Review of Economics & Finance, Elsevier, vol. 95(C).
    43. 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).
    44. Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
    45. Mengheng Li & Ivan Mendieta-Munoz, 2019. "The multivariate simultaneous unobserved components model and identification via heteroskedasticity," Working Paper Series 2019/08, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    46. Ethan Struby & Michael F. Connolly, 2022. "Shadow Rate Models and Monetary Policy," Working Papers 2022-03, Carleton College, Department of Economics.
    47. Laurent Ferrara & Luca Metelli & Filippo Natoli & Daniele Siena, 2020. "Questioning the puzzle: Fiscal policy, exchange rate and inflation," Working papers 752, Banque de France.
    48. Matteo Barigozzi & Marc Hallin, 2024. "The Dynamic, the Static, and the Weak: Factor models and the analysis of high-dimensional time series," Papers 2407.10653, arXiv.org, revised May 2025.
    49. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    50. Tu, Yundong & Wang, Siwei, 2025. "Quantile prediction with factor-augmented regression: Structural instability and model uncertainty," Journal of Econometrics, Elsevier, vol. 249(PB).
    51. Ateeb Akhter Shah Syed & Hassan Raza & Mohsin Waheed, 2023. "Easydata-MD: A Monthly Dataset for Macroeconomic Research on Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 28(1), pages 63-88, Jan-June.
    52. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Economic Forecasts Using Many Noises," Papers 2312.05593, arXiv.org, revised Dec 2023.
    53. Urga, Giovanni & Wang, Fa, 2022. "Estimation and Inference for High Dimensional Factor Model with Regime Switching," MPRA Paper 117012, University Library of Munich, Germany, revised 10 Apr 2023.
    54. Rossi, Lorenza & Zanetti Chini, Emilio, 2021. "Temporal disaggregation of business dynamics: New evidence for U.S. economy," Journal of Macroeconomics, Elsevier, vol. 69(C).
    55. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.
    56. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    57. Herwartz, Helmut & Rohloff, Hannes, 2018. "Less bang for the buck? Assessing the role of inflation uncertainty for U.S. monetary policy transmission in a data rich environment," University of Göttingen Working Papers in Economics 358, University of Goettingen, Department of Economics.
    58. Cho, Dooyeon & Jung, Jaehun, 2025. "Machine learning goes beyond: Time-varying monetary policy and oil price pass-through to inflation expectations," Journal of Macroeconomics, Elsevier, vol. 85(C).
    59. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    60. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    61. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    62. Coqueret, Guillaume & Giroux, Thomas & Zerbib, Olivier David, 2025. "The biodiversity premium," Ecological Economics, Elsevier, vol. 228(C).
    63. Joshua C. C. Chan & Gary Koop & Xuewen Yu, 2021. "Large Order-Invariant Bayesian VARs with Stochastic Volatility," Papers 2111.07225, arXiv.org.
    64. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    65. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    66. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    67. Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2016. "Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting," Working Papers ECARES ECARES 2016-16, ULB -- Universite Libre de Bruxelles.
    68. Coussin, Maximilien, 2025. "The multifaceted effect of monetary policy on U.S. credit aggregates," Journal of Macroeconomics, Elsevier, vol. 84(C).
    69. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    70. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    71. Martin Feldkircher & Florian Huber & Gregor Kastner, 2018. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?," Department of Economics Working Papers wuwp260, Vienna University of Economics and Business, Department of Economics.
    72. Barbarino, Alessandro & Bura, Efstathia, 2024. "Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 1-18.
    73. Tom Boot & Didier Nibbering, 2017. "Inference in high-dimensional linear regression models," Tinbergen Institute Discussion Papers 17-032/III, Tinbergen Institute, revised 05 Jul 2017.
    74. Florens Odendahl & Tatevik Sekhposyan & Barbara Rossi, 2021. "Evaluating Forecast Performance with State Dependence," Working Papers 1295, Barcelona School of Economics.
    75. Lin, Jiahe & Michailidis, George, 2024. "A multi-task encoder-dual-decoder framework for mixed frequency data prediction," International Journal of Forecasting, Elsevier, vol. 40(3), pages 942-957.
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    407. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2020. "Measuring Uncertainty and Its Effects in the COVID-19 Era," Working Papers 20-32R, Federal Reserve Bank of Cleveland, revised 05 Jan 2022.
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    411. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
    412. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
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    418. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    419. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    420. Han, Xu, 2025. "Global identification, estimation and inference of structural impulse response functions in factor models: A unified framework," Journal of Econometrics, Elsevier, vol. 251(C).
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    422. Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
    423. Xiao Huang, 2022. "Boosted p-Values for High-Dimensional Vector Autoregression," Papers 2211.02215, arXiv.org, revised Mar 2023.
    424. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
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    427. Manfred M. Fischer & Florian Huber & Michael Pfarrhofer & Petra Staufer-Steinnocher, 2018. "The dynamic impact of monetary policy on regional housing prices in the United States," Working Papers in Economics 2018-7, University of Salzburg.
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    430. Lusompa, Amaze, 2019. "Local Projections, Autocorrelation, and Efficiency," MPRA Paper 99856, University Library of Munich, Germany, revised 11 Apr 2020.
    431. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," PSE Working Papers halshs-03626503, HAL.
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  13. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Craig S. Hakkio & Jun Nie, 2014. "Implications of recent U.S. energy trends for trade forecasts," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 29-51.
    2. Sune Karlsson & Pär Österholm, 2023. "Is the US Phillips curve stable? Evidence from Bayesian vector autoregressions," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(1), pages 287-314, January.
    3. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    4. Mehmet Pasaogullari, 2015. "Forecasts from Reduced-form Models under the Zero-Lower-Bound Constraint," Working Papers (Old Series) 1512, Federal Reserve Bank of Cleveland.
    5. 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.
    6. Tim Oliver Berg, 2015. "Forecast Accuracy of a BVAR under Alternative Specifications of the Zero Lower Bound," ifo Working Paper Series 203, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    7. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: a multi-country BVAR benchmark for the Eurosystem projections," Working Paper Series 2227, European Central Bank.
    8. Tomas Reichenbachas, 2020. "Assessing the impact of macroprudential measures: The case of the LTV limit in Lithuania," Bank of Lithuania Working Paper Series 80, Bank of Lithuania.
    9. Travis J. Berge & Andrew C. Chang & Nitish R. Sinha, 2019. "Evaluating the Conditionality of Judgmental Forecasts," Finance and Economics Discussion Series 2019-002, Board of Governors of the Federal Reserve System (U.S.).
    10. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    11. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
    12. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2014. "Have standard VARs remained stable since the crisis?," Working Paper 2014/13, Norges Bank.
    13. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    14. D. Tutberidze & D. Japaridze, 2017. "Macroeconomic Forecasting Using Bayesian Vector Autoregressive Approach," Вестник Киевского национального университета имени Тараса Шевченко. Экономика., Socionet;Киевский национальный университет имени Тараса Шевченко, vol. 2(191), pages 42-49.

  14. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    2. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    3. Pincheira-Brown, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2019. "Forecasting inflation in Latin America with core measures," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1060-1071.
    4. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    5. Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
    6. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
    7. Mihaela Simionescu, 2015. "The Improvement of Unemployment Rate Predictions Accuracy," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(3), pages 274-286.

  15. Michael W. McCracken & Tucker S. McElroy, 2012. "Multi-step ahead forecasting of vector time series," Working Papers 2012-060, Federal Reserve Bank of St. Louis.

    Cited by:

    1. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    2. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    3. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.

  16. Michael W. McCracken & Giorgio Valente, 2012. "Asymptotic Inference for Performance Fees and the Predictability of Asset Returns," Working Papers 2012-049, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    2. Jiahan Li & Ilias Tsiakas, 2016. "Equity Premium Prediction: The Role of Economic and Statistical Constraints," Working Paper series 16-25, Rimini Centre for Economic Analysis.
    3. Stein, Tobias, 2024. "Forecasting the equity premium with frequency-decomposed technical indicators," International Journal of Forecasting, Elsevier, vol. 40(1), pages 6-28.
    4. Lyócsa, Štefan & Todorova, Neda, 2020. "Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 628-645.
    5. Potì, Valerio & Levich, Richard & Conlon, Thomas, 2020. "Predictability and pricing efficiency in forward and spot, developed and emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 107(C).
    6. 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.
    7. Daniel de Almeida & Ana-Maria Fuertes & Luiz Koodi Hotta, 2025. "Out-of-Sample Predictability of the Equity Risk Premium," Mathematics, MDPI, vol. 13(2), pages 1-23, January.
    8. Kole, H.J.W.G. & van Dijk, D.J.C., 2013. "How to Identify and Forecast Bull and Bear Markets?," ERIM Report Series Research in Management ERS-2013-016-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  17. Michael W. McCracken, 2012. "Consistent testing for structural change at the ends of the sample," Working Papers 2012-029, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2016. "Ethanol and field crops: Is there a price connection?," Food Policy, Elsevier, vol. 63(C), pages 53-61.

  18. Todd E. Clark & Michael W. McCracken, 2011. "Tests of equal forecast accuracy for overlapping models," Working Papers (Old Series) 1121, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    2. T. S. McElroy, 2016. "Nonnested model comparisons for time series," Biometrika, Biometrika Trust, vol. 103(4), pages 905-914.
    3. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    4. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    5. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    6. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    7. Arbués, Ignacio & Matilla-García, Mariano, 2024. "Multibenchmark reality checks," Economic Modelling, Elsevier, vol. 140(C).
    8. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2025. "Testing Clustered Equal Predictive Ability with Unknown Clusters," Papers 2507.14621, arXiv.org, revised Jul 2025.
    9. Brent Meyer & Saeed Zaman, 2013. "It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting," Working Papers (Old Series) 1303, Federal Reserve Bank of Cleveland.
    10. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-sample forecast tests robust to the choice of window size," Economics Working Papers 1404, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).

  19. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers (Old Series) 1120, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    4. Anwen Yin, 2022. "Does the kitchen‐sink model work forecasting the equity premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 223-247, March.
    5. Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova & Michael Obersteiner, 2024. "Regime‐dependent commodity price dynamics: A predictive analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2822-2847, November.
    6. Christian Hutter, 2020. "A new indicator for nowcasting employment subject to social security contributions in Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-10, December.
    7. Shahzad Ahmad & Farooq Pasha, 2015. "A Pragmatic Model for Monetary Policy Analysis I: The Case of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 1-42.
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    63. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2025. "Switching Macroeconomic Growth and Volatility: Evidence from a Mean-Variance Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
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    65. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    66. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    67. 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.
    68. Nicholas Fawcett & Lena Koerber & Riccardo Masolo & Matthew Waldron, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England working papers 538, Bank of England.
    69. Jonathan H. Wright, 2015. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 12-13, January.
    70. 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.
    71. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    72. Nima Nonejad, 2022. "New Findings Regarding the Out-of-Sample Predictive Impact of the Price of Crude Oil on the United States Industrial Production," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 1-35, March.
    73. Gagnon, Marie-Hélène & Power, Gabriel J. & Toupin, Dominique, 2023. "The sum of all fears: Forecasting international returns using option-implied risk measures," Journal of Banking & Finance, Elsevier, vol. 146(C).
    74. Tunaru, Diana, 2017. "Gaussian estimation and forecasting of the U.K. yield curve with multi-factor continuous-time models," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 119-129.
    75. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    76. Håvard Hungnes, 2020. "Predicting the exchange rate path. The importance of using up-to-date observations in the forecasts," Discussion Papers 934, Statistics Norway, Research Department.
    77. 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.
    78. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2025. "Testing Clustered Equal Predictive Ability with Unknown Clusters," Papers 2507.14621, arXiv.org, revised Jul 2025.
    79. Stephen McKnight & Alexander Mihailov & Fabio Rumler, 2018. "NKPC-Based Inflation Forecasts with a Time-Varying Trend," Serie documentos de trabajo del Centro de Estudios Económicos 2018-05, El Colegio de México, Centro de Estudios Económicos.
    80. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    81. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    82. Kurmaş Akdoğan, 2019. "Size and sign asymmetries in house price adjustments," Applied Economics, Taylor & Francis Journals, vol. 51(48), pages 5268-5281, October.
    83. 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.
    84. 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.
    85. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    86. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    87. Sekkel, Rodrigo M., 2015. "Balance sheets of financial intermediaries: Do they forecast economic activity?," International Journal of Forecasting, Elsevier, vol. 31(2), pages 263-275.
    88. Petri Kuosmanen & Juuso Vataja, 2017. "The return of financial variables in forecasting GDP growth in the G-7," Economic Change and Restructuring, Springer, vol. 50(3), pages 259-277, August.
    89. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    90. Duffee, Gregory, 2013. "Forecasting Interest Rates," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 385-426, Elsevier.
    91. Pincheira, Pablo & Hardy, Nicolas, 2022. "Correlation Based Tests of Predictability," MPRA Paper 112014, University Library of Munich, Germany.
    92. 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.
    93. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2021. "Time-Varying Comovement of Foreign Exchange Markets: A GLS-Based Time-Varying Model Approach," Mathematics, MDPI, vol. 9(8), pages 1-13, April.
    94. Anwen Yin, 2021. "Forecasting the Market Equity Premium: Does Nonlinearity Matter?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(5), pages 1-9, May.
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  20. Chanont Banternghansa & Michael W. McCracken, 2010. "Real-time forecast averaging with ALFRED," Working Papers 2010-033, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar, 2013. "Forecasting Aggregate Retail Sales: The Case of South Africa," Working Papers 201312, University of Pretoria, Department of Economics.
    2. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.
    3. Dimpfl, Thomas & Peter, Franziska J., 2018. "Analyzing volatility transmission using group transfer entropy," Energy Economics, Elsevier, vol. 75(C), pages 368-376.
    4. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2012. "Examination of property forecasting models - accuracy and its improvement through combination forecasting," ERES eres2012_082, European Real Estate Society (ERES).

  21. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Kirstin Hubrich & Kenneth D. West, 2008. "Forecast Evaluation of Small Nested Model Sets," NBER Working Papers 14601, National Bureau of Economic Research, Inc.
    2. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.

  22. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
    3. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers 2011-025, Federal Reserve Bank of St. Louis.
    4. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Working Papers (Old Series) 1134, Federal Reserve Bank of Cleveland.
    5. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: El caso colombiano," Borradores de Economia 821, Banco de la Republica de Colombia.
    6. 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.
    7. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pron�sticos para una econom�a menos vol�til: El caso colombiano," Borradores de Economia 11252, Banco de la Republica.

  23. Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Research Working Paper RWP 09-11, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
    2. Rodney Edvinsson & Sune Karlsson & Pär Österholm, 2025. "Does money growth predict inflation in Sweden? Evidence from vector autoregressions using four centuries of data," Empirical Economics, Springer, vol. 68(4), pages 1613-1635, April.
    3. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    4. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    5. Bjørnland, Hilde C. & Ravazzolo, Francesco & Thorsrud, Leif Anders, 2017. "Forecasting GDP with global components: This time is different," International Journal of Forecasting, Elsevier, vol. 33(1), pages 153-173.
    6. Christian Hutter, 2020. "A new indicator for nowcasting employment subject to social security contributions in Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-10, December.
    7. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," Working Papers 2020_08, Business School - Economics, University of Glasgow.
    8. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    9. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    10. Kilian, Lutz & Alquist, Ron & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
    11. Boriss Siliverstovs, 2015. "Short-term forecasting with mixed-frequency data: A MIDASSO approach," KOF Working papers 15-375, KOF Swiss Economic Institute, ETH Zurich.
    12. Nicolas S. Magner & Nicolás Hardy & Tiago Ferreira & Jaime F. Lavin, 2023. "“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis," Mathematics, MDPI, vol. 11(7), pages 1-16, March.
    13. Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Hutter, Christian & Weber, Enzo, 2013. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," IAB-Discussion Paper 201317, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    15. Robert Gausden & Mohammad Hasan, 2022. "A reappraisal of Katona’s adaptive theory of consumer behaviour using U.K. data," Manchester School, University of Manchester, vol. 90(2), pages 122-143, March.
    16. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    17. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    18. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    19. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
    20. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    21. Nikolsko-Rzhevskyy, Alex & Prodan, Ruxandra, 2012. "Markov switching and exchange rate predictability," International Journal of Forecasting, Elsevier, vol. 28(2), pages 353-365.
    22. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
    23. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    24. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    25. Wensheng Kang & Ronald A. Ratti & Joaquin L. Vespignani, 2016. "The Implications of Liquidity Expansion in China for the US Dollar," CAMA Working Papers 2016-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    26. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "The implications of monetary expansion in China for the US dollar," Journal of Asian Economics, Elsevier, vol. 46(C), pages 71-84.
    27. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers 2011-025, Federal Reserve Bank of St. Louis.
    28. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    29. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
    30. Nima Nonejad, 2024. "Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark," Empirical Economics, Springer, vol. 67(4), pages 1497-1539, October.
    31. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    32. Kichian, Maral & Rumler, Fabio, 2014. "Forecasting Canadian inflation: A semi-structural NKPC approach," Economic Modelling, Elsevier, vol. 43(C), pages 183-191.
    33. Michael W. McCracken, 2019. "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers 2019-11, Federal Reserve Bank of St. Louis.
    34. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    35. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    36. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2020. "Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts," Papers 2003.02803, arXiv.org, revised Feb 2023.
    37. Arai, Natsuki, 2014. "Using forecast evaluation to improve the accuracy of the Greenbook forecast," International Journal of Forecasting, Elsevier, vol. 30(1), pages 12-19.
    38. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Working Papers (Old Series) 1134, Federal Reserve Bank of Cleveland.
    39. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    40. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org, revised Sep 2025.
    41. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    42. Kilian, Lutz & Vigfusson, Robert J., 2012. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," CEPR Discussion Papers 8980, C.E.P.R. Discussion Papers.
    43. 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.
    44. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.
    45. Clark, Todd E. & McCracken, Michael W., 2012. "In-sample tests of predictive ability: A new approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 1-14.
    46. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2021. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Tinbergen Institute Discussion Papers 21-016/III, Tinbergen Institute.
    47. 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.
    48. Håvard Hungnes, 2020. "Predicting the exchange rate path. The importance of using up-to-date observations in the forecasts," Discussion Papers 934, Statistics Norway, Research Department.
    49. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2025. "Testing Clustered Equal Predictive Ability with Unknown Clusters," Papers 2507.14621, arXiv.org, revised Jul 2025.
    50. Mohr, Matthias & Maurin, Laurent & Guérin, Pierre, 2011. "Trend-cycle decomposition of output and euro area inflation forecasts: a real-time approach based on model combination," Working Paper Series 1384, European Central Bank.
    51. Hutter, Christian & Weber, Enzo, 2014. "Forecasting with a mismatch-enhanced labor market matching function," IAB-Discussion Paper 201416, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    52. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    53. Christian Hutter & Enzo Weber, 2017. "Mismatch and the Forecasting Performance of Matching Functions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(1), pages 101-123, February.
    54. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    55. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    56. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    57. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    58. Benjamin K. Johannsen & Elmar Mertens, 2021. "A Time‐Series Model of Interest Rates with the Effective Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(5), pages 1005-1046, August.
    59. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    60. Lukas Bauer, 2025. "Evaluating financial tail risk forecasts: Testing Equal Predictive Ability," Papers 2505.23333, arXiv.org.

  24. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Hsiu-Hsin Ko, 2016. "Exchange Rate Predictability in Finite Samples," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 361-378, September.
    2. Burcu Erik & Marco Jacopo Lombardi & Dubravko Mihaljek & Hyun Song Shin, 2020. "The dollar, bank leverage and real economic activity: an evolving relationship," BIS Working Papers 847, Bank for International Settlements.
    3. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    4. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    5. Marcelle Chauvet & Zeynep Senyuz & Emre Yoldas, 2013. "What does financial volatility tell us about macroeconomic fluctuations?," Finance and Economics Discussion Series 2013-61, Board of Governors of the Federal Reserve System (U.S.).
    6. Tom Boot & Andreas Pick, 2017. "A near optimal test for structural breaks when forecasting under square error loss," Tinbergen Institute Discussion Papers 17-039/III, Tinbergen Institute.
    7. Hsiu-Hsin Ko, 2016. "Exchange Rate Predictability in Finite Samples," The Japanese Economic Review, Springer, vol. 67(3), pages 361-378, September.
    8. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    9. Pincheira-Brown, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2019. "Forecasting inflation in Latin America with core measures," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1060-1071.
    10. Su, Hao & Ying, Chengwei & Zhu, Xiaoneng, 2022. "Disaster risk matters in the bond market," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Kilian, Lutz & Vigfusson, Robert J., 2012. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," CEPR Discussion Papers 8980, C.E.P.R. Discussion Papers.
    12. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    13. Petrella, Ivan & Drechsel, Thomas & Antolin-Diaz, Juan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    14. Mohan Subbiah & Frank J Fabozzi, 2016. "Equity style allocation: A nonparametric approach," Journal of Asset Management, Palgrave Macmillan, vol. 17(3), pages 141-164, May.
    15. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2016. "Tracking the slowdown in long-run GDP growth," Bank of England working papers 587, Bank of England.
    16. Burcu Erik & Marco Jacopo Lombardi & Dubravko Mihaljek & Hyun Song Shin, 2019. "Financial conditions and purchasing managers' indices: exploring the links," BIS Quarterly Review, Bank for International Settlements, September.

  25. Chanont Banternghansa & Michael W. McCracken, 2009. "Forecast disagreement among FOMC members," Working Papers 2009-059, Federal Reserve Bank of St. Louis.

    Cited by:

    1. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    2. Tillmann, Peter, 2011. "Strategic forecasting on the FOMC," European Journal of Political Economy, Elsevier, vol. 27(3), pages 547-553, September.
    3. 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).
    4. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    5. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    6. El-Shagi, Makram & Jung, Alexander, 2015. "Does the Greenspan era provide evidence on leadership in the FOMC?," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 173-190.
    7. A. Jung, 2013. "Policymakers’ Interest Rate Preferences: Recent Evidence for Three Monetary Policy Committees," International Journal of Central Banking, International Journal of Central Banking, vol. 9(3), pages 150-197, September.
    8. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    9. 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.
    10. Tara M. Sinclair & H.O. Stekler & Warren Carnow, 2012. "Evaluating A Vector Of The Fed’S Forecasts," Working Papers 2012-002, The George Washington University, The Center for Economic Research.
    11. 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).
    12. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    13. Tara M. Sinclair & H. O. Stekler & Warren Carnow, 2012. "A new approach for evaluating economic forecasts," Economics Bulletin, AccessEcon, vol. 32(3), pages 2332-2342.
    14. Rülke, Jan-Christoph & Tillmann, Peter, 2011. "Do FOMC members herd?," Economics Letters, Elsevier, vol. 113(2), pages 176-179.
    15. Jaime Marquez & S Yanki Kalfa, 2021. "The Forecasts of Individual FOMC Members: New Evidence after Ten Years," Working Papers 2021-003, The George Washington University, The Center for Economic Research.
    16. Sheng, Xuguang (Simon), 2015. "Evaluating the economic forecasts of FOMC members," International Journal of Forecasting, Elsevier, vol. 31(1), pages 165-175.
    17. Dovern, Jonas, 2015. "A multivariate analysis of forecast disagreement: Confronting models of disagreement with survey data," European Economic Review, Elsevier, vol. 80(C), pages 16-35.
    18. Man-Keung Tang & Mr. Xiangrong Yu, 2011. "Communication of Central Bank Thinking and Inflation Dynamics," IMF Working Papers 2011/209, International Monetary Fund.
    19. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    20. Hayo, Bernd & Neuenkirch, Matthias, 2013. "Do Federal Reserve presidents communicate with a regional bias?," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 62-72.
    21. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.

  26. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    2. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    3. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    4. Dany Brouillette & Marie-Noëlle Robitaille & Laurence Savoie-Chabot & Pierre St-Amant & Bassirou Gueye & Elise Martin, 2019. "The Trend Unemployment Rate in Canada: Searching for the Unobservable," Staff Working Papers 19-13, Bank of Canada.
    5. Christian Hutter, 2020. "A new indicator for nowcasting employment subject to social security contributions in Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-10, December.
    6. Kilian, Lutz & Baumeister, Christiane, 2012. "What Central Bankers Need to Know about Forecasting Oil Prices," CEPR Discussion Papers 9118, C.E.P.R. Discussion Papers.
    7. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    8. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    9. Gonçalves, Sílvia & McCracken, Michael W. & Yao, Yongxu, 2025. "Bootstrapping out-of-sample predictability tests with real-time data," Journal of Econometrics, Elsevier, vol. 247(C).
    10. Baumeister, Christiane & Kilian, Lutz, 2013. "Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis," CFS Working Paper Series 2013/09, Center for Financial Studies (CFS).
    11. Augustus J. Panton, 2020. "Climate Hysteresis and Monetary Policy," CAMA Working Papers 2020-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Todd E. Clark & Michael W. McCracken, 2008. "Averaging forecasts from VARs with uncertain instabilities," Working Papers 2008-030, Federal Reserve Bank of St. Louis.
    13. Marcelle Chauvet & Zeynep Senyuz & Emre Yoldas, 2013. "What does financial volatility tell us about macroeconomic fluctuations?," Finance and Economics Discussion Series 2013-61, Board of Governors of the Federal Reserve System (U.S.).
    14. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    15. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    16. 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.
    17. Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    18. Hutter, Christian & Weber, Enzo, 2013. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," IAB-Discussion Paper 201317, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    19. Philippe de Peretti & Oren Tapiero, 2014. "A GARCH analysis of dark-pool trades," Post-Print hal-00984834, HAL.
    20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    21. Lise Pichette & Marie-Noëlle Robitaille, 2017. "Assessing the Business Outlook Survey Indicator Using Real-Time Data," Discussion Papers 17-5, Bank of Canada.
    22. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    23. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
    24. John W. Galbraith & Greg Tkacz, 2013. "Nowcasting GDP: Electronic Payments, Data Vintages and the Timing of Data Releases," CIRANO Working Papers 2013s-25, CIRANO.
    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. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    27. 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.
    28. 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.
    29. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    30. Benjamin Beckers, 2015. "The Real-Time Predictive Content of Asset Price Bubbles for Macro Forecasts," Discussion Papers of DIW Berlin 1496, DIW Berlin, German Institute for Economic Research.
    31. Calista Cheung & Luke Frymire & Lise Pichette, 2020. "Can the Business Outlook Survey Help Improve Estimates of the Canadian Output Gap?," Discussion Papers 2020-14, Bank of Canada.
    32. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    33. Alfonso Mendoza Velazquez & Peter N. Smith, 2013. "Equity Returns and the Business Cycle: The Role of Supply and Demand Shocks," CAMA Working Papers 2013-22, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    34. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," NBER Working Papers 18391, National Bureau of Economic Research, Inc.
    35. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    36. Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
    37. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Forecasting Performance of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7763, C.E.P.R. Discussion Papers.
    38. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting industrial production: the role of information and methods," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The IFC's contribution to the 57th ISI Session, Durban, August 2009, volume 33, pages 227-235, Bank for International Settlements.
    39. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Staff Working Papers 11-16, Bank of Canada.
    40. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    41. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    42. Lise Pichette & Marie-Noëlle Robitaille & Mohanad Salameh & Pierre St-Amant, 2018. "Dismiss the Gap? A Real-Time Assessment of the Usefulness of Canadian Output Gaps in Forecasting Inflation," Staff Working Papers 18-10, Bank of Canada.
    43. Q. Farooq Akram, 2010. "Policy analysis in real time using IMF's monetary model," Working Paper 2010/10, Norges Bank.
    44. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    45. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    46. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    47. Philippe de Peretti & Oren Tapiero, 2014. "A GARCH analysis of dark-pool trades," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00984834, HAL.
    48. Knotek, Edward S. & Zaman, Saeed, 2019. "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1708-1724.
    49. Pichette, Lise & Robitaille, Marie-Noëlle & Salameh, Mohanad & St-Amant, Pierre, 2019. "Dismiss the output gaps? To use with caution given their limitations," Economic Modelling, Elsevier, vol. 76(C), pages 199-215.
    50. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
    51. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    52. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    53. Norman R. Swanson & Valentina Corradi & Andres Fernandez, 2011. "Information in the Revision Process of Real-Time Datasets," Departmental Working Papers 201107, Rutgers University, Department of Economics.
    54. Nima Nonejad, 2022. "New Findings Regarding the Out-of-Sample Predictive Impact of the Price of Crude Oil on the United States Industrial Production," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 1-35, March.
    55. 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.
    56. Norman R. Swanson & Andres Fernandez, 2011. "Real-Time Datasets Really Do Make a Difference: Definitional Change, Data Release, and Forecasting," Departmental Working Papers 201113, Rutgers University, Department of Economics.
    57. Silvia Goncalves & Michael W. McCracken & Yongxu Yao, 2025. "Out-of-Sample Inference with Annual Benchmark Revisions," Working Papers 2025-020, Federal Reserve Bank of St. Louis.
    58. Matthieu Lemoine & Gian Luigi Mazzi & Paola Monperrus-Veroni & Frédéric Reynes, 2010. "A new production function estimate of the euro area output gap This paper is based on a report for Eurostat: 'Real time estimation of potential output, output gap, NAIRU and Phillips curve for Euro-zo," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 29-53.
    59. Mohr, Matthias & Maurin, Laurent & Guérin, Pierre, 2011. "Trend-cycle decomposition of output and euro area inflation forecasts: a real-time approach based on model combination," Working Paper Series 1384, European Central Bank.
    60. 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.
    61. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
    62. Tanya Molodtsova & Alex Nikolsko‐Rzhevskyy & David H. Papell, 2011. "Taylor Rules and the Euro," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(2‐3), pages 535-552, March.
    63. Marcellino, Massimiliano & Musso, Alberto, 2010. "Real time estimates of the euro area output gap: reliability and forecasting performance," Working Paper Series 1157, European Central Bank.
    64. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Papers No 3/2014, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    65. Francesco Ravazzolo & Philip Rothman, 2015. "Oil-Price Density Forecasts of U.S. GDP," Working Papers No 10/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    66. Kocięcki, Andrzej & Kolasa, Marcin & Rubaszek, Michał, 2012. "A Bayesian method of combining judgmental and model-based density forecasts," Economic Modelling, Elsevier, vol. 29(4), pages 1349-1355.
    67. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    68. Xueting Yu & Yuhan Zhu & Guangming Lv, 2020. "Analysis of the Impact of China’s GDP Data Revision on Monetary Policy from the Perspective of Uncertainty," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(6), pages 1251-1274, May.
    69. Edward S. Knotek & Saeed Zaman, 2017. "Nowcasting U.S. Headline and Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 931-968, August.
    70. Todd E. Clark & Michael W. McCracken, 2008. "Tests of equal predictive ability with real-time data," Working Papers 2008-029, Federal Reserve Bank of St. Louis.
    71. Anthony Garratt & Gary Koop & Emi Mise & Shaun P Vahey, 2007. "Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty," Birkbeck Working Papers in Economics and Finance 0714, Birkbeck, Department of Economics, Mathematics & Statistics.
    72. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
    73. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.

  27. Todd E. Clark & Michael W. McCracken, 2007. "Averaging forecasts from VARs with uncertain instabilities," Finance and Economics Discussion Series 2007-42, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    2. Pesaran, M.H. & Pick, A., 2008. "Forecasting Random Walks Under Drift Instability," Cambridge Working Papers in Economics 0814, Faculty of Economics, University of Cambridge.
    3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    4. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    5. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage Combination from a Real-Time Dataset," CESifo Working Paper Series 3372, CESifo.
    6. 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.
    7. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    8. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    9. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    10. 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.
    11. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    12. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    13. Gian Luigi Mazzi & James Mitchell & Gaetana Montana, 2014. "Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 233-256, April.
    14. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    15. Andrew C. Chang & Phillip Li, 2015. "Measurement Error in Macroeconomic Data and Economics Research: Data Revisions, Gross Domestic Product, and Gross Domestic Income," Finance and Economics Discussion Series 2015-102, Board of Governors of the Federal Reserve System (U.S.).
    16. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    17. Leo Krippner & Leif Anders Thorsrud, 2009. "Forecasting New Zealand's economic growth using yield curve information," Reserve Bank of New Zealand Discussion Paper Series DP2009/18, Reserve Bank of New Zealand.
    18. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    19. di Mauro, Filippo & Fornari, Fabio & Mannucci, Dario, 2011. "Stock market firm-level information and real economic activity," Working Paper Series 1366, European Central Bank.
    20. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an Uncertain Economic Environment," Tinbergen Institute Discussion Papers 14-152/III, Tinbergen Institute.
    21. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    22. Tony Chernis & Taylor Webley, 2022. "Nowcasting Canadian GDP with Density Combinations," Discussion Papers 2022-12, Bank of Canada.
    23. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    24. John M. Maheu & Yong Song, 2012. "A New Structural Break Model with Application to Canadian Inflation Forecasting," Working Paper series 27_12, Rimini Centre for Economic Analysis.
    25. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    26. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal Forecast under Structural Breaks," Working Papers 202208, University of California at Riverside, Department of Economics.
    27. 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.
    28. Francesco Ravazzolo & Tommy Sveen & Sepideh K. Zahiri, 2016. "Commodity Futures and Forecasting Commodity Currencies," Working Papers No 7/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    29. Cristina Conflitti & Christine De Mol & Domenico Giannone, 2012. "Optimal Combination of Survey Forecasts," Working Papers ECARES ECARES 2012-023, ULB -- Universite Libre de Bruxelles.
    30. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    31. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
    32. Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
    33. Hilde C. Bjørnland & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud & Christie Smith, 2010. "Does forecast combination improve Norges Bank inflation forecasts?," Working Papers No 2/2010, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    34. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    35. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," Research Technical Papers 07/RT/12, Central Bank of Ireland.
    36. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    37. Sean P. Grover & Kevin L. Kliesen & Michael W. McCracken, 2016. "A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth," Review, Federal Reserve Bank of St. Louis, vol. 98(4), pages 277-296.
    38. Bjørnland, Hilde C. & Gerdrup, Karsten & Jore, Anne Sofie & Smith, Christie & Thorsrud, Leif Anders, 2011. "Weights and pools for a Norwegian density combination," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 61-76, January.
    39. Wolden Bache, Ida & Sofie Jore, Anne & Mitchell, James & Vahey, Shaun P., 2011. "Combining VAR and DSGE forecast densities," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1659-1670, October.
    40. 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.
    41. Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study," ifo Working Paper Series 65, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    42. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2010. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Discussion Papers in Economics 11442, University of Munich, Department of Economics.
    43. Knut Are Aastveit & Jamie Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Tinbergen Institute Discussion Papers 21-053/III, Tinbergen Institute.
    44. Kuang-Liang Chang & Nan-Kuang Chen & Charles Ka Yui Leung, 2016. "Losing Track of the Asset Markets: the Case of Housing and Stock," International Real Estate Review, Global Social Science Institute, vol. 19(4), pages 435-492.
    45. Shahnaz Parsaeian, 2023. "Structural Breaks in Seemingly Unrelated Regression Models," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202308, University of Kansas, Department of Economics.
    46. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-Time Inflation Forecasting in a Changing World," Working Paper 2009/16, Norges Bank.
    47. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    48. John M Maheu & Thomas H McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Papers tecipa-293, University of Toronto, Department of Economics.
    49. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.
    50. Pierre Gosselin & Aileen Lotz & Charles Wyplosz, 2008. "The Expected Interest Rate Path: Alignment of Expectations vs. Creative Opacity," International Journal of Central Banking, International Journal of Central Banking, vol. 4(3), pages 145-185, September.
    51. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Working Papers (Old Series) 1134, Federal Reserve Bank of Cleveland.
    52. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    53. Duc Do, Nguyen, 2024. "Money/asset ratio as a predictor of inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    54. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    55. 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.
    56. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    57. 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.
    58. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    59. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    60. 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.
    61. 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.
    62. 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.
    63. 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.
    64. Anthony Garratt & James Mitchell & Shaun P. Vahey & Elizabeth C. Wakerly, 2009. "Real-time Inflation Forecast Densities from Ensemble Phillips Curves," Birkbeck Working Papers in Economics and Finance 0910, Birkbeck, Department of Economics, Mathematics & Statistics.
    65. Korobilis, Dimitris, 2009. "VAR forecasting using Bayesian variable selection," MPRA Paper 21124, University Library of Munich, Germany.
    66. Andrew C. Chang & Phillip Li, 2015. "Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say \"Usually Not\"," Finance and Economics Discussion Series 2015-83, Board of Governors of the Federal Reserve System (U.S.).
    67. 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.
    68. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    69. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    70. 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.
    71. 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).
    72. Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank.
    73. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
    74. Nguyen Duc Do, 2025. "Using a Wage–Price‐Setting Model to Forecast US Inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 803-832, March.
    75. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    76. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Optimal Formulations for Nonlinear Autoregressive Processes," Tinbergen Institute Discussion Papers 14-103/III, Tinbergen Institute.
    77. Chanont Banternghansa & Michael W. McCracken, 2010. "Real-time forecast averaging with ALFRED," Working Papers 2010-033, Federal Reserve Bank of St. Louis.
    78. Filippo di Mauro & Filippo di Mauro, Fabio Fornari, 2014. "Going granular: The importance of firm-level equity information in anticipating economic activity," EcoMod2014 6809, EcoMod.
    79. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    80. 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.
    81. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    82. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    83. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    84. Timmermann, Allan & Elliott, Graham, 2016. "Forecasting in Economics and Finance," CEPR Discussion Papers 11354, C.E.P.R. Discussion Papers.
    85. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.
    86. 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.
    87. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    88. 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.

  28. Todd E. Clark & Michael W. McCracken, 2007. "Forecasting with small macroeconomic VARs in the presence of instabilities," Finance and Economics Discussion Series 2007-41, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage Combination from a Real-Time Dataset," CESifo Working Paper Series 3372, CESifo.
    3. Hendry, David F. & Hubrich, Kirstin, 2010. "Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate," Working Paper Series 1155, European Central Bank.
    4. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
    5. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    6. Rubaszek, Michal & Skrzypczynski, Pawel, 2008. "On the forecasting performance of a small-scale DSGE model," International Journal of Forecasting, Elsevier, vol. 24(3), pages 498-512.
    7. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.
    8. Edward S. Knotek, 2007. "How useful is Okun's law?," Economic Review, Federal Reserve Bank of Kansas City, vol. 92(Q IV), pages 73-103.
    9. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    10. Michal Rubaszek & Pawel Skrzypczynski, 2007. "Can a simple DSGE model outperform Professional Forecasters?," NBP Working Papers 43, Narodowy Bank Polski.
    11. 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.
    12. 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.
    13. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    14. 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.

  29. Todd E. Clark & Michael W. McCracken, 2007. "Combining forecasts from nested models," Finance and Economics Discussion Series 2007-43, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    2. Hong, Han & Preston, Bruce, 2012. "Bayesian averaging, prediction and nonnested model selection," Journal of Econometrics, Elsevier, vol. 167(2), pages 358-369.
    3. Emrah Gulay & Serkan Aras, 2024. "Does a meta-combining method lead to more accurate forecasts in the decision-making process?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 101-124.
    4. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2008. "A panel data approach to economic forecasting: the bias-corrected average forecast," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 668, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    5. Morales-Arias, Leonardo & Moura, Guilherme V., 2010. "A conditionally heteroskedastic global inflation model," Kiel Working Papers 1666, Kiel Institute for the World Economy.
    6. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper RWP 06-12, Federal Reserve Bank of Kansas City.
    7. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    8. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    9. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
    10. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.
    11. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    12. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.
    13. 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.
    14. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    15. Andrea Carriero & Raffaella Giacomini, 2011. "How useful are no-arbitrage restrictions for forecasting the term structure of interest rates?," Post-Print hal-00844809, HAL.
    16. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    17. Huiyu Huang & Tae-Hwy Lee, 2006. "To Combine Forecasts or to Combine Information?," Working Papers 200806, University of California at Riverside, Department of Economics, revised Feb 2009.

  30. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Mihaela BRATU SIMIONESCU, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
    2. Giannone, Domenico & D’Agostino, Antonello & Gambetti, Luca, 2009. "Macroeconomic Forecasting and Structural Change," CEPR Discussion Papers 7542, C.E.P.R. Discussion Papers.
    3. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper RWP 06-12, Federal Reserve Bank of Kansas City.
    4. Batra, Shallu & Tiwari, Aviral Kumar & Yadav, Mahender & Danso, Albert, 2025. "Connectedness among diverse financial assets: Evidence from cryptocurrency uncertainty indices," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    5. Kocięcki, Andrzej & Kolasa, Marcin & Rubaszek, Michał, 2012. "A Bayesian method of combining judgmental and model-based density forecasts," Economic Modelling, Elsevier, vol. 29(4), pages 1349-1355.
    6. Çepni, Oğuzhan & Gül, Selçuk & Hacıhasanoğlu, Yavuz Selim & Yılmaz, Muhammed Hasan, 2020. "Global uncertainties and portfolio flow dynamics of the BRICS countries," Research in International Business and Finance, Elsevier, vol. 54(C).

  31. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Pesaran, M.H. & Pick, A., 2008. "Forecasting Random Walks Under Drift Instability," Cambridge Working Papers in Economics 0814, Faculty of Economics, University of Cambridge.
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. 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.
    4. Fotis Papailias & Dimitrios Thomakos, 2015. "Covariance averaging for improved estimation and portfolio allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(1), pages 31-59, February.
    5. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
    6. Cho, Dooyeon, 2021. "On the predictability of the distribution of excess returns in currency markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 511-530.
    7. Craig S. Hakkio, 2008. "PCE and CPI inflation differentials: converting inflation forecasts," Economic Review, Federal Reserve Bank of Kansas City, vol. 93(Q I), pages 51-68.
    8. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    9. 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.
    10. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
    11. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    12. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
    13. Qihao Chen & Zhuo Huang, 2025. "Forecasting Chinese Stock Market Volatility With Intraday and Overnight Volatility Components of INE Oil Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(10), pages 1665-1682, October.
    14. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    15. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    16. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an Uncertain Economic Environment," Tinbergen Institute Discussion Papers 14-152/III, Tinbergen Institute.
    17. 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.
    18. Zihui Yang & Yinggang Zhou, 2017. "Quantitative Easing and Volatility Spillovers Across Countries and Asset Classes," Management Science, INFORMS, vol. 63(2), pages 333-354, February.
    19. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
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    21. Alina Barnett & Haroon Mumtaz & Konstantinos Theodoridis, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
    22. 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.
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    27. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    28. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    29. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    30. Jianying Xie, 2021. "A New Multivariate Predictive Model for Stock Returns," Papers 2110.01873, arXiv.org.
    31. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    32. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    33. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    34. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    35. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
    36. Morales-Arias, Leonardo & Moura, Guilherme V., 2013. "Adaptive forecasting of exchange rates with panel data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 493-509.
    37. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper RWP 06-12, Federal Reserve Bank of Kansas City.
    38. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
    39. 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.
    40. 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.
    41. Graziano Moramarco, 2022. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers," Econometrics, MDPI, vol. 11(1), pages 1-29, December.
    42. Yuqing Feng & Yaojie Zhang & Yudong Wang, 2024. "Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 567-582, April.
    43. 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.
    44. 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.
    45. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    46. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
    47. Jeongwoo Kim, 2019. "Optimally adjusted last cluster for prediction based on balancing the bias and variance by bootstrapping," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-31, November.
    48. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    49. Tan, Pei P. & Galagedera, Don U.A. & Maharaj, Elizabeth A., 2012. "A wavelet based investigation of long memory in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2330-2341.
    50. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    51. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    52. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    53. 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.
    54. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
    55. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.
    56. 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.
    57. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    58. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    59. Charles Hoffreumon & Nicolas van Zeebroeck, 2018. "Forecasting short-term transaction fees on a smart contracts platform," Working Papers TIMES² 2018-028, ULB -- Universite Libre de Bruxelles.
    60. Bauwens, Luc & Korobilis, Dimitris & Koop, Gary & Rombouts, Jeroen V.K., 2011. "A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models," SIRE Discussion Papers 2011-25, Scottish Institute for Research in Economics (SIRE).
    61. 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.
    62. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Forecasting the oil–gasoline price relationship: Do asymmetries help?," Energy Economics, Elsevier, vol. 46(S1), pages 44-56.
    63. 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.
    64. Mihaela SIMIONESCU, 2014. "Improving The Inflation Rate Forecasts Of Romanian Experts Using A Fixed-Effects Models Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 87-102, June.
    65. Vellachami, Sanggetha & Hasanov, Akram Shavkatovich & Brooks, Robert, 2023. "Risk transmission from the energy markets to the carbon market: Evidence from the recursive window approach," International Review of Financial Analysis, Elsevier, vol. 89(C).
    66. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
    67. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
    68. Zongwu Cai & Ted Juhl, 2020. "The Distribution Of Rolling Regression Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202218, University of Kansas, Department of Economics, revised Dec 2022.
    69. Damiano B. Silipo & Giovanni Verga & Sviatlana Hlebik, 2023. "Managerial Beliefs and Banking Behavior," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(3), pages 401-431, December.
    70. Marie Bessec, 2010. "Étalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Économie et Prévision, Programme National Persée, vol. 193(2), pages 77-99.
    71. Leung, Charles Ka Yui & Tang, Edward Chi Ho, 2014. "Availability, Affordability and Volatility: the case of Hong Kong Housing Market," MPRA Paper 58770, University Library of Munich, Germany.
    72. Davide De Gaetano, 2018. "Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets," Mathematics, MDPI, vol. 6(3), pages 1-19, March.
    73. 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.
    74. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    75. João F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    76. 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).
    77. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    78. Davide De Gaetano, 2018. "Forecast Combinations for Structural Breaks in Volatility: Evidence from BRICS Countries," JRFM, MDPI, vol. 11(4), pages 1-13, October.
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    81. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
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    84. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    85. Tsvetomir Tsvetkov, 2025. "Factors of Economic Growth in Bulgaria 1995-2019," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 75-97.
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  32. Todd E. Clark & Michael W. McCracken, 2003. "The predictive content of the output gap for inflation : resolving in-sample and out-of-sample evidence," Research Working Paper RWP 03-06, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    2. Semei Coronado & Rangan Gupta & Saban Nazlioglu & Omar Rojas, 2020. "Time-Varying Causality between Bond and Oil Markets of the United States: Evidence from Over One and Half Centuries of Data," Working Papers 202006, University of Pretoria, Department of Economics.
    3. Ercio Muñoz S. & Alfredo Pistelli M., 2010. "¿Tienen los Terremotos un Impacto Inflacionario en el Corto Plazo? Evidencia para una Muestra de Países," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(2), pages 113-127, April.
    4. Güneş Kamber & James Morley & Benjamin Wong, 2017. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," Reserve Bank of New Zealand Discussion Paper Series DP2017/01, Reserve Bank of New Zealand.
    5. Scott Brave & Jonas D. M. Fisher, 2004. "In search of a robust inflation forecast," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 28(Q IV), pages 12-31.
    6. Alexander Doser & Ricardo Nunes & Nikhil Rao & Viacheslav Sheremirov, 2023. "Inflation expectations and nonlinearities in the Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 453-471, June.
    7. Andrew Keinsley & Sandeep Kumar Rangaraju, 2021. "The Nonlinear Unemployment-Inflation Relationship and the Factors That Define It," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 47(3), pages 354-377, June.
    8. Michael Dotsey & Shigeru Fujita & Tom Stark, 2017. "Do Phillips Curves Conditionally Help to Forecast Inflation?," Working Papers 17-26, Federal Reserve Bank of Philadelphia.
    9. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
    10. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    11. Mayer, Alexander & Wied, Dominik & Troster, Victor, 2025. "Quantile Granger causality in the presence of instability," Journal of Econometrics, Elsevier, vol. 249(PB).
    12. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    13. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
    14. Giacomini, Raffaella & Rossi, Barbara, 2006. "Detecting and predicting forecast breakdowns," Working Paper Series 638, European Central Bank.
    15. Pablo Pincheira & Andrés Gatty, 2014. "Forecasting Chilean Inflation with International Factors," Working Papers Central Bank of Chile 723, Central Bank of Chile.
    16. Sune Karlsson & Pär Österholm, 2023. "Is the US Phillips curve stable? Evidence from Bayesian vector autoregressions," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(1), pages 287-314, January.
    17. Camila Figueroa & Jorge Fornero & Pablo García, 2019. "Hindsight vs. Real time measurement of the output gap: Implications for the Phillips curve in the Chilean Case," Working Papers Central Bank of Chile 854, Central Bank of Chile.
    18. Ahmad, Saad & Civelli, Andrea, 2016. "Globalization and inflation: A threshold investigation," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 283-304.
    19. Gonzalo Llosa & Shirley Miller, 2005. "Using additional information in estimating the output gap in Peru: a multivariate unobserved component approach," Working Papers 2005-004, Banco Central de Reserva del Perú.
    20. Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2020. "Time-Varying Influence of Household Debt on Inequality in United Kingdom," Working Papers 202017, University of Pretoria, Department of Economics.
    21. Danila Ovechkin, 2026. "Estimation and forecasting with a Nonlinear Phillips Curve based on heterogeneous sensitivity between economic activity and CPI components," Bank of Russia Working Paper Series wps161, Bank of Russia.
    22. 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.
    23. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    24. Qin, Ting & Enders, Walter, 2008. "In-sample and out-of-sample properties of linear and nonlinear Taylor rules," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 428-443, March.
    25. Eo, Yunjong & Morley, James, 2017. "Why has the US economy stagnated since the Great Recession?," Working Papers 2017-14, University of Sydney, School of Economics, revised Jun 2019.
    26. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    27. Zhang, Qian & Li, Zeguang, 2021. "Time-varying risk attitude and the foreign exchange market behavior," Research in International Business and Finance, Elsevier, vol. 57(C).
    28. Don H. Kim, 2008. "Challenges in macro-finance modeling," Finance and Economics Discussion Series 2008-06, Board of Governors of the Federal Reserve System (U.S.).
    29. Don H. Kim, 2009. "Challenges in macro-finance modeling," Review, Federal Reserve Bank of St. Louis, vol. 91(Sep), pages 519-544.
    30. Serhan Cevik & Fedor Miryugin, 2025. "It’s Never Different: Fiscal Policy Shocks and Inflation," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 67(1), pages 186-220, March.
    31. Yonglian Wang & Lijun Wang & Han Liu & Yongjing Wang, 2021. "The Robust Causal Relationships Among Domestic Tourism Demand, Carbon Emissions, and Economic Growth in China," SAGE Open, , vol. 11(4), pages 21582440211, October.
    32. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
    33. 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.
    34. Mr. Serhan Cevik & Tianle Zhu, 2019. "Trinity Strikes Back: Monetary Independence and Inflation in the Caribbean," IMF Working Papers 2019/197, International Monetary Fund.
    35. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    36. Rossi, Barbara & Wang, Yiru, 2019. "Vector autoregressive-based Granger causality test in the presence of instabilities," MPRA Paper 101492, University Library of Munich, Germany.
    37. 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.
    38. Hamza Bennani, 2018. "Media Perception of Fed Chair's Overconfidence and Market Expectations," EconomiX Working Papers 2018-29, University of Paris Nanterre, EconomiX.
    39. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    40. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.
    41. Mehmet Balcilar & Gizem Uzuner & Festus Victor Bekun & Mark E. Wohar, 2023. "Housing price uncertainty and housing prices in the UK in a time-varying environment," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(2), pages 523-549, May.
    42. Quast, Josefine & Wolters, Maik H., 2019. "Reliable Real-time Output Gap Estimates Based on a Modified Hamilton Filter," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203535, Verein für Socialpolitik / German Economic Association.
    43. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper RWP 06-12, Federal Reserve Bank of Kansas City.
    44. Michał Hulej & Grzegorz Grabek, 2015. "Output gap measure based on survey data," NBP Working Papers 200, Narodowy Bank Polski.
    45. Lee, Dong Jin & Yoon, Jai Hyung, 2016. "The New Keynesian Phillips Curve in multiple quantiles and the asymmetry of monetary policy," Economic Modelling, Elsevier, vol. 55(C), pages 102-114.
    46. Alessandro Barbarino & Travis J. Berge & Andrea Stella, 2024. "The stability and economic relevance of output gap estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(6), pages 1065-1081, September.
    47. Driver, Ciaran & Trapani, Lorenzo & Urga, Giovanni, 2013. "On the use of cross-sectional measures of forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 29(3), pages 367-377.
    48. Adriana Arreaza & Enid Blanco & Miguel Dorta, 2004. "A Small Scale Macroeconomic Model for Venezuela," Money Affairs, CEMLA, vol. 0(1), pages 25-38, January-J.
    49. Serhan Cevik, João Tovar Jalles, 2023. "Eye of the Storm: The Impact of Climate Shocks on Inflation and Growth," Working Papers REM 2023/0276, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    50. Yiru Wang & Barbara Rossi, 2019. "VAR-based Granger-causality test in the presence of instabilities," Economics Working Papers 1642, Department of Economics and Business, Universitat Pompeu Fabra.
    51. Jean-Stéphane Mésonnier, 2006. "The Reliability of Macroeconomic Forecasts based on Real Interest Rate Gap Estimates in Real Time: an Assessment for the Euro Area," Working papers 157, Banque de France.
    52. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Forecasting Performance of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7763, C.E.P.R. Discussion Papers.
    53. Dong Jin Lee, 2009. "Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process," Working papers 2009-26, University of Connecticut, Department of Economics.
    54. Don H Kim & Athanasios Orphanides, 2007. "The bond market term premium: what is it, and how can we measure it?," BIS Quarterly Review, Bank for International Settlements, June.
    55. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    56. Todd E. Clark & Michael W. McCracken, 2003. "The predictive content of the output gap for inflation : resolving in-sample and out-of-sample evidence," Research Working Paper RWP 03-06, Federal Reserve Bank of Kansas City.
    57. Tri Minh Phan, 2024. "Sentiment-semantic word vectors: A new method to estimate management sentiment," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-22, December.
    58. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
    59. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    60. Pincheira-Brown, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2019. "Forecasting inflation in Latin America with core measures," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1060-1071.
    61. Mr. Andreas Billmeier, 2004. "Measuring a Roller Coaster: Evidenceon the Finnish Output Gap," IMF Working Papers 2004/057, International Monetary Fund.
    62. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    63. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    64. Athanasios Orphanides & Simon van Norden, 2004. "The reliability of inflation forecasts based on output gap estimates in real time," Finance and Economics Discussion Series 2004-68, Board of Governors of the Federal Reserve System (U.S.).
    65. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
    66. Martha López P., 2004. "Efficient Policy Rule for Inflation Targeting in Colombia," Money Affairs, CEMLA, vol. 0(1), pages 1-24, January-J.
    67. Lance J. Bachmeier & Norman R. Swanson, 2003. "Predicting Inflation: Does The Quantity Theory Help?," Departmental Working Papers 200317, Rutgers University, Department of Economics.
    68. Jeremy M. Piger & Robert H. Rasche, 2006. "Inflation: do expectations trump the gap?," Working Papers 2006-013, Federal Reserve Bank of St. Louis.
    69. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
    70. Kabukçuoğlu, Ayşe & Martínez-García, Enrique, 2018. "Inflation as a global phenomenon—Some implications for inflation modeling and forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 46-73.
    71. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
    72. Christina Anderl & Guglielmo Maria Caporale, 2023. "Asymmetries, uncertainty and inflation: evidence from developed and emerging economies," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(4), pages 984-1017, December.
    73. Richard Ashley & Randal J. Verbrugge, 2019. "The Intermittent Phillips Curve: Finding a Stable (But Persistence-Dependent) Phillips Curve Model Specification," Working Papers 19-09R2, Federal Reserve Bank of Cleveland, revised 14 Feb 2023.
    74. Heaton, Chris, 2015. "Testing for multiple-period predictability between serially dependent time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 587-597.
    75. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.
    76. Lillian Kamal, 2014. "Do GAP Models Still have a Role to Play in Forecasting Inflation?," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(3), pages 1-12.
    77. James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
    78. Oguzhan Cepni & David Gabauer & Rangan Gupta & Khuliso Ramabulana, 2020. "Time-Varying Spillover of US Trade War on the Growth of Emerging Economies," Working Papers 202002, University of Pretoria, Department of Economics.
    79. Karlsson, Sune & Österholm, Pär, 2025. "On the Stability of Macroeconomic Relationships in Australia," Working Papers 2025:15, Örebro University, School of Business.
    80. Nicholas Apergis & Panagiotis G. Artikis, 2016. "Foreign Exchange Risk, Equity Risk Factors and Economic Growth," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 44(4), pages 425-445, December.
    81. Sune Karlsson & Pär Österholm, 2020. "A note on the stability of the Swedish Phillips curve," Empirical Economics, Springer, vol. 59(6), pages 2573-2612, December.
    82. Lance Bachmeier & Qi Li & Dandan Liu, 2008. "Should Oil Prices Receive So Much Attention? An Evaluation Of The Predictive Power Of Oil Prices For The U.S. Economy," Economic Inquiry, Western Economic Association International, vol. 46(4), pages 528-539, October.
    83. César Calderón & Klaus Schmidt-Hebbel, 2010. "What Drives Inflation in the World?," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    84. Christina Anderl & Guglielmo Maria Caporale, 2023. "The Asymmetric Impact of Economic Policy and Oil Price Uncertainty on Inflation: Evidence from Developed and Emerging Economies," CESifo Working Paper Series 10276, CESifo.
    85. Ulrich Gunter, 2019. "Estimating and forecasting with a two-country DSGE model of the Euro area and the USA: the merits of diverging interest-rate rules," Empirical Economics, Springer, vol. 56(4), pages 1283-1323, April.
    86. Pablo Pincheira & Hernán Rubio, 2010. "The Low Predictive Power of Simple Phillips Curves in Chile: A Real-Time Evaluation," Working Papers Central Bank of Chile 559, Central Bank of Chile.
    87. Luca Agnello & Vítor Castro & Ricardo M. Sousa, 2023. "Interest rate gaps in an uncertain global context: why “too” low (high) for “so” long?," Empirical Economics, Springer, vol. 64(2), pages 539-565, February.
    88. Dieppe, Alistair & Ortega, Eva & D'Agostino, Antonello & Karlsson, Tohmas & Benkovskis, Konstantins & Caivano, Michele & Hurtado, Samuel & Várnai, Tímea, 2011. "Assessing the sensitivity of inflation to economic activity," Working Paper Series 1357, European Central Bank.
    89. Frédérick Demers, 2003. "The Canadian Phillips Curve and Regime Shifting," Staff Working Papers 03-32, Bank of Canada.
    90. Yonglian Wang & Lijun Wang & Changchun Pan, 2022. "Tourism–Growth Nexus in the Presence of Instability," Sustainability, MDPI, vol. 14(4), pages 1-11, February.
    91. Gary Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2018. "Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation," BEA Working Papers 0156, Bureau of Economic Analysis.
    92. Marcellino, Massimiliano & Musso, Alberto, 2010. "Real time estimates of the euro area output gap: reliability and forecasting performance," Working Paper Series 1157, European Central Bank.
    93. Jean-Stéphane MESONNIER, 2007. "The predictive content of the real interest rate gap for macroeconomic variables in the euro area," Money Macro and Finance (MMF) Research Group Conference 2006 102, Money Macro and Finance Research Group.
    94. Dur, Ayşe & Martínez García, Enrique, 2020. "Mind the gap!—A monetarist view of the open-economy Phillips curve," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    95. Dong Jin Lee & Jai Hyung Yoon, 2012. "The New Keynesian Phillips Curves in Multiple Quantiles and the Asymmetry of Monetary Policy," Working papers 2012-03, University of Connecticut, Department of Economics.
    96. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    97. Anthony Garratt & Gary Koop & Emi Mise & Shaun Vahey, 2008. "Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2008/13, Reserve Bank of New Zealand.
    98. Troy Matheson, 2006. "Phillips curve forecasting in a small open economy," Reserve Bank of New Zealand Discussion Paper Series DP2006/01, Reserve Bank of New Zealand.
    99. Wayne Robinson, 2004. "Real Shocks, Credibility & Stabilization Policy in a Small Open Economy," Money Affairs, CEMLA, vol. 0(1), pages 39-55, January-J.
    100. Todd E. Clark & Taisuke Nakata, 2008. "Has the behavior of inflation and long-term inflation expectations changed?," Economic Review, Federal Reserve Bank of Kansas City, vol. 93(Q I), pages 17-50.
    101. Hilde C. Bjørnland & Leif Brubakk & Anne Sofie Jore, 2006. "Forecasting inflation with an uncertain output gap," Working Paper 2006/02, Norges Bank.
    102. Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020. "What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC," Papers 2006.06274, arXiv.org, revised Aug 2022.
    103. Helder Ferreira de Mendonça & Luciano Vereda & Luan Mateus Matos de Araújo, 2025. "Fundamentals Models Versus Random Walk: Evidence From an Emerging Economy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1884-1906, September.
    104. Mr. Serhan Cevik & João Tovar Jalles, 2023. "Eye of the Storm: The Impact of Climate Shocks on Inflation and Growth," IMF Working Papers 2023/087, International Monetary Fund.

  33. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    2. Kilian, Lutz & Inoue, Atsushi, 2002. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," CEPR Discussion Papers 3671, C.E.P.R. Discussion Papers.
    3. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    4. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
    5. Todd E. Clark & Michael W. McCracken, 2003. "The predictive content of the output gap for inflation : resolving in-sample and out-of-sample evidence," Research Working Paper RWP 03-06, Federal Reserve Bank of Kansas City.
    6. Burns, Kelly & Moosa, Imad A., 2015. "Enhancing the forecasting power of exchange rate models by introducing nonlinearity: Does it work?," Economic Modelling, Elsevier, vol. 50(C), pages 27-39.
    7. Jorge Selaive & Vicente Tuesta, 2004. "Can Fluctuations in the Consumption-Wealth Ratio Help to Predict Exchange Rates?," International Finance 0404014, University Library of Munich, Germany.

  34. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Nelson C. Mark & Donggyu Sul, 2004. "The Use of Predictive Regressions at Alternative Horizons in Finance and Economics," Finance 0409032, University Library of Munich, Germany.
    2. Martin D.D. Evans & Richard K. Lyons, 2005. "Meese-Rogoff Redux: Micro-Based Exchange Rate Forecasting," NBER Working Papers 11042, National Bureau of Economic Research, Inc.
    3. Boucher, Christophe, 2006. "Stock prices-inflation puzzle and the predictability of stock market returns," Economics Letters, Elsevier, vol. 90(2), pages 205-212, February.
    4. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    5. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working papers 2012-12, University of Connecticut, Department of Economics.
    6. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    7. Mestre, Ricardo, 2007. "Are survey-based inflation expections in the euro area informative?," Working Paper Series 721, European Central Bank.
    8. Barbara Rossi, 2007. "Expectations hypotheses tests at Long Horizons," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 554-579, November.
    9. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    10. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
    11. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
    12. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
    13. Todd E. Clark & Michael W. McCracken, 2003. "The predictive content of the output gap for inflation : resolving in-sample and out-of-sample evidence," Research Working Paper RWP 03-06, Federal Reserve Bank of Kansas City.
    14. Catherine Bruno & Olivier de Bandt & Alexis Flageollet & Emmanuel Michaux, 2003. "Forecasting Inflation using Economic Indicators: the Case of France," Working papers 101, Banque de France.
    15. Athanasios Orphanides & Simon van Norden, 2004. "The reliability of inflation forecasts based on output gap estimates in real time," Finance and Economics Discussion Series 2004-68, Board of Governors of the Federal Reserve System (U.S.).
    16. Michael Steiner, 2009. "Predicting premiums for the market, size, value, and momentum factors," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(2), pages 137-155, June.
    17. Gabriel Moser & Fabio Rumler & Johann Scharler, 2004. "Forecasting Austrian Inflation," Working Papers 91, Oesterreichische Nationalbank (Austrian Central Bank).
    18. Rapach, David E. & Wohar, Mark E., 2006. "In-sample vs. out-of-sample tests of stock return predictability in the context of data mining," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 231-247, March.
    19. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    20. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, University Library of Munich, Germany.
    21. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.

  35. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.

    Cited by:

    1. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    2. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    3. Clements, Michael P. & Smith, Jeremy, 2002. "Evaluating multivariate forecast densities: a comparison of two approaches," International Journal of Forecasting, Elsevier, vol. 18(3), pages 397-407.
    4. Gabriela De Raaij & Burkhard Raunig, 2005. "Evaluating density forecasts from models of stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 151-166.
    5. Robledo, Carlos W. & Zapata, Hector O. & McCracken, Michael, 2001. "New Mse Tests For Evaluating Forecasting Performance: Empirics And Bootstrap," 2001 Annual meeting, August 5-8, Chicago, IL 20686, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    7. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
    8. Stanislav Anatolyev, 2007. "Inference about predictive ability when there are many predictors," Working Papers w0096, Center for Economic and Financial Research (CEFIR).
    9. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
    10. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    11. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Robust tests of predictive accuracy," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 161-184.
    12. Daniel Buncic, 2009. "Understanding forecast failure of ESTAR models of real exchange rates," EERI Research Paper Series EERI_RP_2009_18, Economics and Econometrics Research Institute (EERI), Brussels.

  36. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.

    Cited by:

    1. Makin, Anthony J. & Ratnasiri, Shyama, 2015. "Competitiveness and government expenditure: The Australian example," Economic Modelling, Elsevier, vol. 49(C), pages 154-161.
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    4. Mario Porqueddu & Fabrizio Venditti, 2012. "Do food commodity prices have asymmetric effects on Euro-Area inflation?," Temi di discussione (Economic working papers) 878, Bank of Italy, Economic Research and International Relations Area.
    5. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    6. Adam J. Check & Anna K Nolan & Tyler C. Schipper, 2019. "Forecasting GDP Growth using Disaggregated GDP Revisions," Economics Bulletin, AccessEcon, vol. 39(4), pages 2580-2588.
    7. Nicolas Chanut & Mario Marcel & Carlos Medel, 2018. "Can Economic Perception Surveys Improve Macroeconomic Forecasting in Chile?," Working Papers Central Bank of Chile 824, Central Bank of Chile.
    8. Rodney Edvinsson & Sune Karlsson & Pär Österholm, 2025. "Does money growth predict inflation in Sweden? Evidence from vector autoregressions using four centuries of data," Empirical Economics, Springer, vol. 68(4), pages 1613-1635, April.
    9. Kwon, Kyung Yoon & Min, Byoung-Kyu & Sun, Chenfei, 2022. "Enhancing the profitability of lottery strategies," Journal of Empirical Finance, Elsevier, vol. 69(C), pages 166-184.
    10. Lee, Kevin & Olekalns, Nils & Shields, Kalvinder, 2009. "Nowcasting, Business Cycle Dating and the Interpretation of New Information when Real-Time Data are Available," CEPR Discussion Papers 7426, C.E.P.R. Discussion Papers.
    11. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    12. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.
    13. 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.
    14. Pollet, Joshua M. & Wilson, Mungo, 2010. "Average correlation and stock market returns," Journal of Financial Economics, Elsevier, vol. 96(3), pages 364-380, June.
    15. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    16. Meichi Huang, 2013. "Housing bubble implications: The perspective of housing price predictability," Economics Bulletin, AccessEcon, vol. 33(1), pages 586-596.
    17. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    18. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    19. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    20. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    21. Jondeau, Eric & Zhang, Qunzi & Zhu, Xiaoneng, 2019. "Average skewness matters," Journal of Financial Economics, Elsevier, vol. 134(1), pages 29-47.
    22. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    23. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    24. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
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    95. Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics.
    96. 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.
    97. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    98. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the best volatility models: the model confidence set approach," FRB Atlanta Working Paper 2003-28, Federal Reserve Bank of Atlanta.
    99. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    100. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    101. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    102. Liu, Ping & James Hueng, C., 2017. "Measuring real business condition in China," China Economic Review, Elsevier, vol. 46(C), pages 261-274.
    103. Avramov, Doron, 2002. "Stock return predictability and model uncertainty," Journal of Financial Economics, Elsevier, vol. 64(3), pages 423-458, June.
    104. Campa, Jose Manuel & Chang, P. H. Kevin, 1998. "The forecasting ability of correlations implied in foreign exchange options," Journal of International Money and Finance, Elsevier, vol. 17(6), pages 855-880, December.
    105. Taylor, Mark & Clarida, Richard & Sarno, Lucio & Valente, Giorgio, 2005. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," CEPR Discussion Papers 4835, C.E.P.R. Discussion Papers.
    106. Yi-Ting Chen, 2016. "Testing for Granger Causality in Moments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(2), pages 265-288, April.
    107. Allan Timmermann & Graham Elliott & Ivana Komunjer, 2004. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Econometric Society 2004 North American Summer Meetings 601, Econometric Society.
    108. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    109. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    110. Lamont, Owen A., 2001. "Economic tracking portfolios," Journal of Econometrics, Elsevier, vol. 105(1), pages 161-184, November.
    111. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    112. Poshakwale, Sunil S. & Mandal, Anandadeep, 2016. "Determinants of asymmetric return comovements of gold and other financial assets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 229-242.
    113. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
    114. Florackis, Chris & Giorgioni, Gianluigi & Kostakis, Alexandros & Milas, Costas, 2014. "On stock market illiquidity and real-time GDP growth," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 210-229.
    115. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    116. Nicolau, Mihaela & Palomba, Giulio, 2015. "Dynamic relationships between spot and futures prices. The case of energy and gold commodities," Resources Policy, Elsevier, vol. 45(C), pages 130-143.
    117. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    118. Martínez-Martin, Jaime & Morris, Richard & Onorante, Luca & Piersanti, Fabio M., 2019. "Merging structural and reduced-form models for forecasting: opening the DSGE-VAR box," Working Paper Series 2335, European Central Bank.
    119. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    120. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    121. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    122. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Robust tests of predictive accuracy," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 161-184.
    123. Alfonso Dufour & Robert F Engle, 2000. "The ACD Model: Predictability of the Time Between Concecutive Trades," ICMA Centre Discussion Papers in Finance icma-dp2000-05, Henley Business School, University of Reading.
    124. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    125. Jean-Yves Pitarakis, 2020. "A Novel Approach to Predictive Accuracy Testing in Nested Environments," Papers 2008.08387, arXiv.org, revised Oct 2023.
    126. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
    127. Rossi, Barbara & Sekhposyan, Tatevik, 2016. "Forecast Rationality Tests in the Presence of Instabilities, With Applications to Federal Reserve and Survey Forecasts," CEPR Discussion Papers 11391, C.E.P.R. Discussion Papers.
    128. Pavel Yaskov, 2010. "Testing for predictive ability in the presence of structural breaks (in Russian)," Quantile, Quantile, issue 8, pages 127-135, July.
    129. Patrick Minford & Ruthira Naraidoo, 2010. "Vicious And Virtuous Circles – The Political Economy Of Unemployment," South African Journal of Economics, Economic Society of South Africa, vol. 78(1), pages 1-22, March.
    130. Taylor, James W., 2020. "A strategic predictive distribution for tests of probabilistic calibration," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1380-1388.
    131. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    132. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.
    133. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
    134. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.

Articles

  1. Gonçalves, Sílvia & McCracken, Michael W. & Yao, Yongxu, 2025. "Bootstrapping out-of-sample predictability tests with real-time data," Journal of Econometrics, Elsevier, vol. 247(C).
    See citations under working paper version above.
  2. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    See citations under working paper version above.
  3. Michael W. McCracken & Joseph T. McGillicuddy & Michael T. Owyang, 2022. "Binary Conditional Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1246-1258, June.
    See citations under working paper version above.
  4. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    See citations under working paper version above.
  5. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    See citations under working paper version above.
  6. Michael W. McCracken, 2020. "Diverging Tests of Equal Predictive Ability," Econometrica, Econometric Society, vol. 88(4), pages 1753-1754, July.
    See citations under working paper version above.
  7. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    See citations under working paper version above.
  8. Michael W. McCracken & Joseph T. McGillicuddy, 2019. "An empirical investigation of direct and iterated multistep conditional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 181-204, March.
    See citations under working paper version above.
  9. Michael W. McCracken & Giorgio Valente, 2018. "Asymptotic Inference for Performance Fees and the Predictability of Asset Returns," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 426-437, July.
    See citations under working paper version above.
  10. Todd E. Clark & Michael W. McCracken, 2017. "Tests of Predictive Ability for Vector Autoregressions Used for Conditional Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 533-553, April.

    Cited by:

    1. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    2. Michael W. McCracken & Joseph McGillicuddy, 2017. "An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts," Working Papers 2017-40, Federal Reserve Bank of St. Louis.
    3. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    4. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    5. Chen, Chaoyi & Gospodinov, Nikolay & Maynard, Alex & Pesavento, Elena, 2022. "Long-horizon stock valuation and return forecasts based on demographic projections," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 190-215.
    6. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.

  11. Tucker McElroy & Michael W. McCracken, 2017. "Multistep ahead forecasting of vector time series," Econometric Reviews, Taylor & Francis Journals, vol. 36(5), pages 495-513, May.
    See citations under working paper version above.
  12. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    See citations under working paper version above.
  13. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    See citations under working paper version above.
  14. Sean P. Grover & Kevin L. Kliesen & Michael W. McCracken, 2016. "A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth," Review, Federal Reserve Bank of St. Louis, vol. 98(4), pages 277-296.

    Cited by:

    1. Alberto Caruso, 2018. "Macroeconomic News and Market Reaction: Surprise Indexes meet Nowcasting," Working Papers ECARES 2018-06, ULB -- Universite Libre de Bruxelles.

  15. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    See citations under working paper version above.
  16. Todd E. Clark & Michael W. Mccracken, 2014. "Tests Of Equal Forecast Accuracy For Overlapping Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 415-430, April.
    See citations under working paper version above.
  17. Sean P. Grover & Michael W. McCracken, 2014. "Factor-based prediction of industry-wide bank stress," Review, Federal Reserve Bank of St. Louis, vol. 96(2), pages 173-194.

    Cited by:

    1. Dalibor Stevanovic & Rachidi Kotchoni, 2016. "Forecasting U.S. Recessions and Economic Activity," CIRANO Working Papers 2016s-36, CIRANO.
    2. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    3. Rachidi Kotchoni & Dalibor Stevanovic, 2020. "GDP Forecast Accuracy During Recessions," Working Papers 20-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    4. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    5. Fang, Cao & Yeager, Timothy J., 2020. "A historical loss approach to community bank stress testing," Journal of Banking & Finance, Elsevier, vol. 118(C).

  18. Clark, Todd E. & McCracken, Michael W., 2012. "In-sample tests of predictive ability: A new approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 1-14.
    See citations under working paper version above.
  19. Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, vol. 93(Jan), pages 49-66.
    See citations under working paper version above.
  20. Todd E. Clark & Michael W. McCracken, 2011. "Reality Checks and Comparisons of Nested Predictive Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 53-66, February.

    Cited by:

    1. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    2. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    3. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    4. Grégory Levieuge, 2015. "Explaining and forecasting bank loans. Good times and crisis," Working papers 566, Banque de France.
    5. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    6. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    7. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    8. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    9. Khalaf, Lynda & Saunders, Charles J., 2017. "Monte Carlo forecast evaluation with persistent data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 1-10.
    10. Ahmed, Shamim & Tsvetanov, Daniel, 2016. "The predictive performance of commodity futures risk factors," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 20-36.
    11. Wang, Rudan & Morley, Bruce & Stamatogiannis, Michalis P., 2019. "Forecasting the exchange rate using nonlinear Taylor rule based models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 429-442.
    12. Kolev, Gueorgui I. & Karapandza, Rasa, 2017. "Out-of-sample equity premium predictability and sample split–invariant inference," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 188-201.
    13. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    14. Wada, Tatsuma, 2022. "Out-of-sample forecasting of foreign exchange rates: The band spectral regression and LASSO," Journal of International Money and Finance, Elsevier, vol. 128(C).
    15. Nima Nonejad, 2021. "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, vol. 61(5), pages 2913-2930, November.
    16. Jaqueson K. Galimberti, 2020. "Forecasting GDP growth from outer space," Working Papers 2020-02, Auckland University of Technology, Department of Economics.
    17. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    18. Enoch Cheng & Clemens C. Struck, 2019. "Time-Series Momentum: A Monte-Carlo Approach," Working Papers 201906, School of Economics, University College Dublin.
    19. 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.
    20. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    21. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org, revised Sep 2025.
    22. Arbués, Ignacio & Matilla-García, Mariano, 2024. "Multibenchmark reality checks," Economic Modelling, Elsevier, vol. 140(C).
    23. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    24. Todd E. Clark & Michael W. McCracken, 2011. "Tests of equal forecast accuracy for overlapping models," Working Papers 2011-024, Federal Reserve Bank of St. Louis.
    25. Rudan Wang & Bruce Morley & Javier Ordóñez, 2015. "The Taylor Rule, Wealth Effects and the Exchange Rate," Working Papers 2015/08, Economics Department, Universitat Jaume I, Castellón (Spain).
    26. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    27. 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.
    28. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    29. Alessandro Morico & Ovidijus Stauskas, 2025. "Robust Tests for Factor-Augmented Regressions with an Application to the novel EA-MD-QD Dataset," Papers 2504.08455, arXiv.org, revised Nov 2025.
    30. Tarassow, Artur, 2019. "Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques," International Journal of Forecasting, Elsevier, vol. 35(2), pages 443-457.
    31. Zeng-Hua Lu, 2019. "Extended MinP Tests for Global and Multiple testing," Papers 1911.04696, arXiv.org, revised Aug 2024.
    32. Christian Hutter & Enzo Weber, 2017. "Mismatch and the Forecasting Performance of Matching Functions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(1), pages 101-123, February.
    33. Sander, Magnus, 2018. "Market timing over the business cycle," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 130-145.
    34. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2017. "International stock return predictability: Evidence from new statistical tests," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 97-113.
    35. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "International Tail Risk and World Fear," Hannover Economic Papers (HEP) dp-620, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    36. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
    37. Tobback, Ellen & Naudts, Hans & Daelemans, Walter & Junqué de Fortuny, Enric & Martens, David, 2018. "Belgian economic policy uncertainty index: Improvement through text mining," International Journal of Forecasting, Elsevier, vol. 34(2), pages 355-365.
    38. Granziera, Eleonora & Hubrich, Kirstin & Moon, Hyungsik Roger, 2014. "A predictability test for a small number of nested models," Journal of Econometrics, Elsevier, vol. 182(1), pages 174-185.

  21. Kevin L. Kliesen & Michael W. McCracken & Linpeng Zheng, 2011. "Initial claims and employment growth: are we at the threshold?," Economic Synopses, Federal Reserve Bank of St. Louis.

    Cited by:

    1. John Carter Braxton, 2013. "Revisiting the use of initial jobless claims as a labor market indicator," Research Working Paper RWP 13-03, Federal Reserve Bank of Kansas City.
    2. Kevin L. Kliesen, 2014. "A guide to tracking the U.S. economy," Review, Federal Reserve Bank of St. Louis, vol. 96(1), pages 35-54.

  22. Michael W. McCracken, 2011. "Housing's role in a recovery," Economic Synopses, Federal Reserve Bank of St. Louis.

    Cited by:

    1. Lunsford, Kurt G., 2015. "Forecasting residential investment in the United States," International Journal of Forecasting, Elsevier, vol. 31(2), pages 276-285.

  23. 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.
    See citations under working paper version above.
  24. Michael W. McCracken, 2010. "Using stock market liquidity to forecast recessions," Economic Synopses, Federal Reserve Bank of St. Louis.

    Cited by:

  25. Michael W. McCracken, 2010. "Disagreement at the FOMC: the dissenting votes are just part of the story," The Regional Economist, Federal Reserve Bank of St. Louis, issue Oct, pages 10-16.

    Cited by:

    1. El-Shagi, Makram & Giesen, Sebastian & Jung, Alexander, 2016. "Revisiting the relative forecast performances of Fed staff and private forecasters: A dynamic approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 313-323.
    2. Alexander Jung & Gergely Kiss, 2012. "Voting by monetary policy committees: evidence from the CEE inflation-targeting countries," MNB Working Papers 2012/2, Magyar Nemzeti Bank (Central Bank of Hungary).
    3. Jung, Alexander & Latsos, Sophia, 2015. "Do federal reserve bank presidents have a regional bias?," European Journal of Political Economy, Elsevier, vol. 40(PA), pages 173-183.
    4. El-Shagi, Makram & Jung, Alexander, 2015. "Does the Greenspan era provide evidence on leadership in the FOMC?," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 173-190.
    5. A. Jung, 2013. "Policymakers’ Interest Rate Preferences: Recent Evidence for Three Monetary Policy Committees," International Journal of Central Banking, International Journal of Central Banking, vol. 9(3), pages 150-197, September.
    6. Hamza Bennani, 2016. "Measuring Monetary Policy Stress for Fed District Representatives," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(2), pages 156-176, May.
    7. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2013. "Does Central Bank Staff Beat Private Forecasters?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79925, Verein für Socialpolitik / German Economic Association.
    8. Jung, Alexander & Kiss, Gergely, 2012. "Preference heterogeneity in the CEE inflation-targeting countries," European Journal of Political Economy, Elsevier, vol. 28(4), pages 445-460.
    9. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2014. "Does the federal reserve staff still beat private forecasters?," Working Paper Series 1635, European Central Bank.

  26. Michael W. McCracken, 2010. "Using FOMC forecasts to forecast the economy," Economic Synopses, Federal Reserve Bank of St. Louis.

    Cited by:

    1. 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).
    2. Carola Conces Binder & Rodrigo Sekkel, 2023. "Central Bank Forecasting: A Survey," Staff Working Papers 23-18, Bank of Canada.
    3. Tillmann, Peter, 2011. "Strategic forecasting on the FOMC," European Journal of Political Economy, Elsevier, vol. 27(3), pages 547-553, September.
    4. 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).
    5. 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).
    6. Stefan Eichler & Tom Lähner, 2014. "Forecast dispersion, dissenting votes, and monetary policy preferences of FOMC members: the role of individual career characteristics and political aspects," Public Choice, Springer, vol. 160(3), pages 429-453, September.
    7. Fendel, Ralf & Rülke, Jan-Christoph, 2012. "Are heterogeneous FOMC forecasts consistent with the Fed’s monetary policy?," Economics Letters, Elsevier, vol. 116(1), pages 5-7.
    8. Rülke, Jan-Christoph & Tillmann, Peter, 2011. "Do FOMC members herd?," Economics Letters, Elsevier, vol. 113(2), pages 176-179.

  27. Clark, Todd E. & McCracken, Michael W., 2009. "Tests of Equal Predictive Ability With Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 441-454.
    See citations under working paper version above.
  28. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    See citations under working paper version above.
  29. Michael W. McCracken, 2009. "How accurate are forecasts in a recession?," National Economic Trends, Federal Reserve Bank of St. Louis, issue Feb.

    Cited by:

  30. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.
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  32. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
    See citations under working paper version above.
  33. McCracken, Michael W & Sapp, Stephen G, 2005. "Evaluating the Predictability of Exchange Rates Using Long-Horizon Regressions: Mind Your p's and q's!," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 473-494, June.

    Cited by:

    1. Engel, Charles, 2014. "Exchange Rates and Interest Parity," Handbook of International Economics, in: Gopinath, G. & Helpman, . & Rogoff, K. (ed.), Handbook of International Economics, edition 1, volume 4, chapter 0, pages 453-522, Elsevier.
    2. 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).
    3. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    4. Sarno, Lucio & Valente, Giorgio, 2008. "Exchange Rates and Fundamentals: Footloose or Evolving Relationship?," CEPR Discussion Papers 6638, C.E.P.R. Discussion Papers.
    5. Darvas, Zsolt & Schepp, Zoltán, 2007. "Kelet-közép-európai devizaárfolyamok előrejelzése határidős árfolyamok segítségével [Forecasting the exchange rates of three Central-Eastern European currencies with forward exchange rates]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 501-528.
    6. Keith Cuthbertson & Dirk Nitzsche & Niall O'Sullivan, 2012. "False Discoveries in UK Mutual Fund Performance," European Financial Management, European Financial Management Association, vol. 18(3), pages 444-463, June.
    7. Michael Bleaney, 2006. "Fundamentals And Exchange Rate Volatility," Discussion Papers 06/03, University of Nottingham, School of Economics.
    8. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    9. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
    10. Cuthbertson, Keith & Nitzsche, Dirk, 2013. "Performance, stock selection and market timing of the German equity mutual fund industry," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 86-101.
    11. Zsolt DARVAS & Zoltán SCHEPP, 2008. "Forecasting Exchange Rates of Major Currencies with Long Maturity Forward Rates," EcoMod2008 23800026, EcoMod.
    12. Jorge Selaive & Vicente Tuesta, 2004. "Can Fluctuations in the Consumption-Wealth Ratio Help to Predict Exchange Rates?," International Finance 0404014, University Library of Munich, Germany.
    13. Adrian Austin & Swarna Dutt, 2015. "Exchange Rates and Fundamentals: A New Look at the Evidence on Long-Horizon Predictability," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 43(1), pages 147-159, March.
    14. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    15. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    16. Pablo Pincheira, 2006. "Conditional Evaluation of Exchange Rate Predictive Ability in Long Run Regressions," Working Papers Central Bank of Chile 378, Central Bank of Chile.
    17. Kim, Sangbae & In, Francis, 2012. "False discoveries in volatility timing of mutual funds," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2083-2094.
    18. Swanson, Norman R. & Urbach, Richard, 2015. "Prediction and simulation using simple models characterized by nonstationarity and seasonality," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 312-323.
    19. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
    20. Menzie D. Chinn & Ron Alquist, 2006. "Conventional and Unconventional Approaches to Exchange Rate Modeling and Assessment," NBER Working Papers 12481, National Bureau of Economic Research, Inc.
    21. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers 200423, Rutgers University, Department of Economics.
    22. Bredin, Don & Cuthbertson, Keith & Nitzsche, Dirk & Thomas, Dylan C., 2014. "Performance and performance persistence of UK closed-end equity funds," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 189-199.

  34. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.

    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. Mario Porqueddu & Fabrizio Venditti, 2012. "Do food commodity prices have asymmetric effects on Euro-Area inflation?," Temi di discussione (Economic working papers) 878, Bank of Italy, Economic Research and International Relations Area.
    4. Fabian Baetje & Lukas Menkhoff, 2016. "Equity Premium Prediction: Are Economic and Technical Indicators Unstable?," Discussion Papers of DIW Berlin 1552, DIW Berlin, German Institute for Economic Research.
    5. John Y. Campbell & Samuel B. Thompson, 2005. "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?," NBER Working Papers 11468, National Bureau of Economic Research, Inc.
    6. Cremaschini, Alessandro & Maruotti, Antonello, 2023. "A finite mixture analysis of structural breaks in the G-7 gross domestic product series," Research in Economics, Elsevier, vol. 77(1), pages 76-90.
    7. Cai Zongwu & Chen Linna & Fang Ying, 2012. "A New Forecasting Model for USD/CNY Exchange Rate," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-20, September.
    8. Marilena Furno, 2011. "Goodness of Fit and Misspecification in Quantile Regressions," Journal of Educational and Behavioral Statistics, , vol. 36(1), pages 105-131, February.
    9. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2019. "Stock Returns and Investor Sentiment: Textual Analysis and Social Media," Working Papers 19-03, Department of Economics, West Virginia University.
    10. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    11. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    12. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
    13. Ana Beatriz Galvão, 2007. "Changes in Predictive Ability with Mixed Frequency Data," Working Papers 595, Queen Mary University of London, School of Economics and Finance.
    14. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    15. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    16. Jing Tian & Heather M. Anderson, 2011. "Forecasting Under Strucural Break Uncertainty," Monash Econometrics and Business Statistics Working Papers 8/11, Monash University, Department of Econometrics and Business Statistics.
    17. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    18. Eichengreen, Barry & Mody, Ashoka & Nedeljkovic, Milan & Sarno, Lucio, 2012. "How the Subprime Crisis went global: Evidence from bank credit default swap spreads," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1299-1318.
    19. Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," International Finance 0503006, University Library of Munich, Germany.
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    1. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    2. Arratibel, Olga & Leiner-Killinger, Nadine & Kamps, Christophe, 2009. "Inflation forecasting in the new EU Member States," Working Paper Series 1015, European Central Bank.
    3. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    4. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    5. Mario Forni & Luca Gambetti, 2014. "Government Spending Shocks in Open Economy VARs," Center for Economic Research (RECent) 105, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    6. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    7. Vivian, Andrew & Wohar, Mark E., 2013. "The output gap and stock returns: Do cyclical fluctuations predict portfolio returns?," International Review of Financial Analysis, Elsevier, vol. 26(C), pages 40-50.
    8. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
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    11. Berger, Helge & Österholm, Pär, 2008. "Does money matter for U.S. inflation? Evidence from Bayesian VARs," Discussion Papers 2008/9, Free University Berlin, School of Business & Economics.
    12. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
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    15. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
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    20. LAURENT, Sébastien & VIOLANTE, Francesco, 2012. "Volatility forecasts evaluation and comparison," LIDAM Reprints CORE 2414, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    21. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
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    25. Roi D. Taussig, 2017. "Stickiness of employee expenses and implications for stock returns," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 7(2), pages 297-309, August.
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    35. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
    36. 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.
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    39. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    40. Kevin L. Kliesen, 2007. "How well does employment predict output?," Review, Federal Reserve Bank of St. Louis, vol. 89(Sep), pages 433-446.
    41. Peter Reinhard Hansen & Allan Timmermann, 2012. "Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics," CREATES Research Papers 2012-45, Department of Economics and Business Economics, Aarhus University.
    42. Helge Berger & Pär Österholm, 2011. "Does Money Growth Granger Cause Inflation in the Euro Area? Evidence from Out‐of‐Sample Forecasts Using Bayesian VARs," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 45-60, March.
    43. McGurk, Zachary, 2020. "US real estate inflation prediction: Exchange rates and net foreign assets," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 53-66.
    44. Norman R. Swanson & Nii Ayi Armah, 2011. "Some Variables are More Worthy Than Others: New Diffusion Index Evidence on the Monitoring of Key Economic Indicators," Departmental Working Papers 201115, Rutgers University, Department of Economics.
    45. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    46. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
    47. Kevin L. Kliesen, 2008. "Oil and the U.S. macroeconomy: an update and a simple forecasting exercise," Working Papers 2008-009, Federal Reserve Bank of St. Louis.
    48. Valseth, Siri, 2016. "Informed trading in Hybrid Bond Markets," UiS Working Papers in Economics and Finance 2016/13, University of Stavanger.
    49. Chava, Sudheer & Gallmeyer, Michael & Park, Heungju, 2015. "Credit conditions and stock return predictability," Journal of Monetary Economics, Elsevier, vol. 74(C), pages 117-132.
    50. Driver, Ciaran & Trapani, Lorenzo & Urga, Giovanni, 2013. "On the use of cross-sectional measures of forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 29(3), pages 367-377.
    51. Haskamp, Ulrich, 2017. "Forecasting exchange rates: The time-varying relationship between exchange rates and Taylor rule fundamentals," Ruhr Economic Papers 704, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    52. Pablo PINCHEIRA-BROWN & Nicolás HARDY, 2024. "More predictable than ever, with the worst MSPE ever," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-30, December.
    53. Jean-Stéphane Mésonnier, 2006. "The Reliability of Macroeconomic Forecasts based on Real Interest Rate Gap Estimates in Real Time: an Assessment for the Euro Area," Working papers 157, Banque de France.
    54. Söderberg, Jonas, 2008. "Do Macroeconomic Variables Forecast Changes in Liquidity? An Out-of-sample Study on the Order-driven Stock Markets in Scandinavia," CAFO Working Papers 2009:10, Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics.
    55. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
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    58. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    59. 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.
    60. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    61. A A Syntetos & N C Georgantzas & J E Boylan & B C Dangerfield, 2011. "Judgement and supply chain dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1138-1158, June.
    62. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    63. Ekaterini Panopoulou & Nikitas Pittis & Sarantis Kalyvitis, 2010. "Looking far in the past: revisiting the growth-returns nexus with non-parametric tests," Empirical Economics, Springer, vol. 38(3), pages 743-766, June.
    64. Jon Faust & Jonathan H. Wright, 2018. "Risk Premia in the 8:30 Economy," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-19, September.
    65. Medel, Carlos A., 2015. "A Critical Review of Posch, J. and F. Rumler (2015), 'Semi-Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve,' Journal of Forecasting 34(2): 145-62," MPRA Paper 65665, University Library of Munich, Germany.
    66. Pincheira-Brown, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2019. "Forecasting inflation in Latin America with core measures," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1060-1071.
    67. Christian Fieberg & Gerrit Liedtke & Thorsten Poddig, 2025. "Recurrent double-conditional factor model," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 47(1), pages 205-254, March.
    68. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    69. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    70. R Naraidoo & I Paya, 2010. "Forecasting Monetary Policy Rules in South Africa," Working Papers 611194, Lancaster University Management School, Economics Department.
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    73. Ana Sequeira, 2013. "Predicting aggregate returns using valuation ratios out-of-sample," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    74. Ruthira Naraidoo & Ivan Paya, 2010. "Forecasting Monetary Rules in South Africa," Working Papers 201007, University of Pretoria, Department of Economics.
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    108. Ekaterini Panopoulou & Sotiria Plastira, 2014. "Fama French factors and US stock return predictability," Journal of Asset Management, Palgrave Macmillan, vol. 15(2), pages 110-128, April.
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    110. D'Agostino, Antonello & Surico, Paolo, 2007. "Does global liquidity help to forecast US inflation?," Research Technical Papers 10/RT/07, Central Bank of Ireland.
    111. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    112. Todd E. Clark & Michael W. McCracken, 2006. "Combining forecasts from nested models," Research Working Paper RWP 06-02, Federal Reserve Bank of Kansas City.
    113. Onur Ince, 2013. "Forecasting Exchange Rates Out-of-Sample with Panel Methods and Real-Time Data," Working Papers 13-04, Department of Economics, Appalachian State University.
    114. Jean-Yves Pitarakis, 2020. "A Novel Approach to Predictive Accuracy Testing in Nested Environments," Papers 2008.08387, arXiv.org, revised Oct 2023.
    115. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    116. Doyle, Matthew, 2006. "Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown?," Staff General Research Papers Archive 12684, Iowa State University, Department of Economics.
    117. Pablo Pincheira Brown & Nicolás Hardy, 2023. "Forecasting base metal prices with exchange rate expectations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2341-2362, December.
    118. Granziera, Eleonora & Hubrich, Kirstin & Moon, Hyungsik Roger, 2014. "A predictability test for a small number of nested models," Journal of Econometrics, Elsevier, vol. 182(1), pages 174-185.
    119. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.

  36. McCracken, Michael W., 2004. "Parameter estimation and tests of equal forecast accuracy between non-nested models," International Journal of Forecasting, Elsevier, vol. 20(3), pages 503-514.

    Cited by:

    1. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    2. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    3. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    4. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    5. 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.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    8. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers (Old Series) 1413, Federal Reserve Bank of Cleveland.
    9. Massimo Guidolin & Daniel L. Thornton, 2010. "Predictions of short-term rates and the expectations hypothesis," Working Papers 2010-013, Federal Reserve Bank of St. Louis.
    10. Hartmann, Daniel & Pierdzioch, Christian, 2006. "International Equity Flows and the Predictability of U.S. Stock Returns," MPRA Paper 562, University Library of Munich, Germany, revised Apr 2006.
    11. Arbués, Ignacio & Matilla-García, Mariano, 2024. "Multibenchmark reality checks," Economic Modelling, Elsevier, vol. 140(C).
    12. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    13. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    14. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    15. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    16. Andrea Ajello & Luca Benzoni & Olena Chyruk, 2012. "Core and 'Crust': Consumer Prices and the Term Structure of Interest Rates," Working Paper Series WP-2014-11, Federal Reserve Bank of Chicago.
    17. Aaron Amburgey & Michael W. McCracken, 2022. "On the Real-Time Predictive Content of Financial Conditions Indices for Growth," Working Papers 2022-003, Federal Reserve Bank of St. Louis, revised 03 Jun 2022.
    18. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    19. Francisco Javier Eransus & Alfonso Novales Cinca, 2014. "Parameter Estimation Error in Tests of Predictive Performance under Discrete Loss Functions," Documentos de Trabajo del ICAE 2014-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    20. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    21. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.

  37. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    See citations under working paper version above.
  38. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.

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    1. Thomakos, Dimitrios D. & Guerard, John Jr., 2004. "Naive, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance," International Journal of Forecasting, Elsevier, vol. 20(1), pages 53-67.
    2. 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.
    3. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    4. Marc Joëts, 2012. "Mood-misattribution effect on energy markets: a biorhythm approach," EconomiX Working Papers 2012-24, University of Paris Nanterre, EconomiX.
    5. Lucio Sarno, 2003. "Nonlinear Exchange Rate Models: A Selective Overview," Rivista di Politica Economica, SIPI Spa, vol. 93(4), pages 3-46, July-Augu.
    6. Sarno, Lucio & Thornton, Daniel L & Valente, Giorgio, 2005. "Federal Funds Rate Prediction," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 449-471, June.
    7. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    8. Giacomini, Raffaella & Rossi, Barbara, 2006. "Detecting and predicting forecast breakdowns," Working Paper Series 638, European Central Bank.
    9. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    10. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Paper series 42_09, Rimini Centre for Economic Analysis.
    11. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2015. "Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models," FinMaP-Working Papers 46, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    12. Kasai, Ndahiriwe & Naraidoo, Ruthira, 2011. "Evaluating the forecasting performance of linear and nonlinear monetary policy rules for South Africa," MPRA Paper 40699, University Library of Munich, Germany.
    13. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 214, European Central Bank.
    14. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    15. Saša ŽIKOVIÆ & Randall K. FILER, 2013. "Ranking of VaR and ES Models: Performance in Developed and Emerging Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(4), pages 327-359, August.
    16. Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk, 2021. "Nonparametric tests for Optimal Predictive Ability," International Journal of Forecasting, Elsevier, vol. 37(2), pages 881-898.
    17. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    18. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    19. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    20. Pesaran, M. Hashem & Zaffaroni, Paolo, 2005. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi-Asset Volatility Models for Risk Management," CEPR Discussion Papers 5279, C.E.P.R. Discussion Papers.
    21. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    22. 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.
    23. Andreas Billmeier, 2009. "Ghostbusting: which output gap really matters?," International Economics and Economic Policy, Springer, vol. 6(4), pages 391-419, December.
    24. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
    25. Fernández-Avilés, Gema & Mattera, Raffaele & Scepi, Germana, 2024. "Factor-Augmented Autoregressive Neural Network to forecast NOx in the city of Madrid," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    26. Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
    27. McCracken, Michael W., 2004. "Parameter estimation and tests of equal forecast accuracy between non-nested models," International Journal of Forecasting, Elsevier, vol. 20(3), pages 503-514.
    28. Sang-Kuck Chung, 2006. "The out-of-sample forecasts of nonlinear long-memory models of the real exchange rate," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 355-370.
    29. Renee van Eyden & Goodness C. Aye & Rangan Gupta, 2012. "Predictive Ability of Competing Models for South Africa’s Fixed Business Non- Residential Investment Spending," Working Papers 201229, University of Pretoria, Department of Economics.
    30. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    31. West, Kenneth D., 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 495-497, December.
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    33. Marc Joëts, 2013. "Heterogeneous Beliefs, Regret, and Uncertainty: The Role of Speculation in Energy Price Dynamics," Working Papers 2013.32, Fondazione Eni Enrico Mattei.
    34. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2019. "Robust optimization of forecast combinations," International Journal of Forecasting, Elsevier, vol. 35(3), pages 910-926.
    35. Aurea Grané & Helena Veiga, 2012. "Asymmetry, realised volatility and stock return risk estimates," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 11(2), pages 147-164, August.
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    38. Massimo Guidolin & Daniel L. Thornton, 2010. "Predictions of short-term rates and the expectations hypothesis," Working Papers 2010-013, Federal Reserve Bank of St. Louis.
    39. Yin, Anwen, 2019. "Out-of-sample equity premium prediction in the presence of structural breaks," International Review of Financial Analysis, Elsevier, vol. 65(C).
    40. Park, Timothy A. & Gubanova, Tatiana & Lohr, Luanne & Escalante, Cesar L., 2005. "Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability," 2005 Annual meeting, July 24-27, Providence, RI 19412, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    41. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2007. "Long Run and Cyclical Dynamics in the US Stock Market," CESifo Working Paper Series 2046, CESifo.
    42. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    43. Aue, Alexander & Horváth, Lajos & Hušková, Marie, 2012. "Segmenting mean-nonstationary time series via trending regressions," Journal of Econometrics, Elsevier, vol. 168(2), pages 367-381.
    44. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
    45. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    46. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    47. Patton, Andrew J. & Timmermann, Allan, 2005. "Testable implications of forecast optimality," LSE Research Online Documents on Economics 6834, London School of Economics and Political Science, LSE Library.
    48. Cristina Danciulescu, 2010. "Backtesting Value-at-Risk Models: A Multivariate Approach," CAEPR Working Papers 2010-004, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    49. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    50. Olmo, J. & Pouliot, W., 2008. "Early Detection Techniques for Market Risk Failure," Working Papers 08/09, Department of Economics, City St George's, University of London.
    51. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    52. Sander Barendse & Erik Kole & Dick van Dijk, 2019. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Tinbergen Institute Discussion Papers 19-058/III, Tinbergen Institute.
    53. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    54. Lance J. Bachmeier & Norman R. Swanson, 2003. "Predicting Inflation: Does The Quantity Theory Help?," Departmental Working Papers 200317, Rutgers University, Department of Economics.
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    57. Mark E. Wohar & David E. Rapach, 2007. "Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models This is a significantly revised version of our previous paper, 'Forecasting US Business Fixed ," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 33-51.
    58. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
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    61. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    62. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
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    65. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
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    69. Boucher, Christophe M. & Danielsson, Jon & Kouontchou, Patrick S. & Maillet, Bertrand B., 2014. "Risk models–at–risk," LSE Research Online Documents on Economics 59299, London School of Economics and Political Science, LSE Library.
    70. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
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    100. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    101. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.

  39. West, Kenneth D & McCracken, Michael W, 1998. "Regression-Based Tests of Predictive Ability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-840, November.
    See citations under working paper version above.

Chapters

  1. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    See citations under working paper version above.
  2. Michael W. McCracken, 2012. "Consistent Testing for Structural Change at the Ends of the Sample," Advances in Econometrics, in: 30th Anniversary Edition, pages 133-169, Emerald Group Publishing Limited.
    See citations under working paper version above.
  3. Todd E. Clark & Michael W. McCracken, 2008. "Chapter 3 Forecasting with Small Macroeconomic VARs in the Presence of Instabilities," Frontiers of Economics and Globalization, in: Forecasting in the Presence of Structural Breaks and Model Uncertainty, pages 93-147, Emerald Group Publishing Limited.

    Cited by:

    1. Duc Do, Nguyen, 2024. "Money/asset ratio as a predictor of inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).

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