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Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics

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  • Laura Coroneo
  • Fabrizio Iacone

Abstract

We consider fixed‐smoothing asymptotics for the Diebold and Mariano (Journal of Business and Economic Statistics, 1995, 13(3), 253–263) test of predictive accuracy. We show that this approach delivers predictive accuracy tests that are correctly sized even when only a small number of out‐of‐sample observations is available. We apply the fixed‐smoothing asymptotics to the Diebold and Mariano test to evaluate the predictive accuracy of the Survey of Professional Forecasters (SPF) and of the European Central Bank Survey of Professional Forecasters (ECB SPF) against a simple random walk. Our results show that the predictive abilities of the SPF and of the ECB SPF were partially spurious.

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  • Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    • Handle: RePEc:wly:japmet:v:35:y:2020:i:4:p:391-409
    DOI: 10.1002/jae.2756
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    1. Phillips, Peter C.B., 2005. "Hac Estimation By Automated Regression," Econometric Theory, Cambridge University Press, vol. 21(1), pages 116-142, February.
    2. Yixiao Sun, 2013. "A heteroskedasticity and autocorrelation robust F test using an orthonormal series variance estimator," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-26, February.
    3. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    4. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    5. 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.
    6. Raffaella Giacomini & Barbara Rossi, 2009. "Detecting and Predicting Forecast Breakdowns," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(2), pages 669-705.
    7. Yixiao Sun & Peter C. B. Phillips & Sainan Jin, 2008. "Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing," Econometrica, Econometric Society, vol. 76(1), pages 175-194, January.
    8. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    9. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," International Journal of Forecasting, Elsevier, vol. 33(4), pages 833-847.
    12. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    13. Hualde, Javier & Iacone, Fabrizio, 2017. "Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes," Economics Letters, Elsevier, vol. 150(C), pages 39-43.
    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. Wooldridge, Jeffrey M. & White, Halbert, 1988. "Some Invariance Principles and Central Limit Theorems for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 4(2), pages 210-230, August.
    16. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2010. "An Evaluation of the Growth and Unemployment Forecasts in the ECB Survey of Professional Forecasters," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-28.
    17. Gonçalves, Sílvia & Vogelsang, Timothy J., 2011. "Block Bootstrap Hac Robust Tests: The Sophistication Of The Naive Bootstrap," Econometric Theory, Cambridge University Press, vol. 27(4), pages 745-791, August.
    18. Garcí­a, Juan Angel, 2003. "An introduction to the ECB's survey of professional forecasters," Occasional Paper Series 8, European Central Bank.
    19. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 541-559, October.
    20. Laura Coroneo & Fabrizio Iacone & Fabio Profumo, 2019. "A Real-time Density Forecast Evaluation of the ECB Survey of Professional Forecasters," Discussion Papers 19/14, Department of Economics, University of York.
    21. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    22. Javier Hualde & Fabrizio Iacone, 2015. "Autocorrelation robust inference using the Daniell kernel with fixed bandwidth," Discussion Papers 15/14, Department of Economics, University of York.
    23. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    24. Juan Angel Garcia, 2003. "An introduction to the ECB’s survey of professional forecasters," Occasional Paper Series 08, European Central Bank.
    25. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    26. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice Rejoinder," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 574-575, October.
    27. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    28. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    29. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    30. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    6. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    7. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
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    9. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    10. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
    11. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
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    13. 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.
    14. Katleho Makatjane & Tshepiso Tsoku, 2022. "Bootstrapping Time-Varying Uncertainty Intervals for Extreme Daily Return Periods," IJFS, MDPI, vol. 10(1), pages 1-23, January.
    15. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
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    18. Fabrizio Iacone & Luca Rossini & Andrea Viselli, 2024. "Comparing predictive ability in presence of instability over a very short time," Papers 2405.11954, arXiv.org.

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