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Bias Correction and Out-of-Sample Forecast Accuracy

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  • Kim, Hyeongwoo
  • Durmaz, Nazif

Abstract

The least squares (LS) estimator suffers from signicant downward bias in autoregressive models that include an intercept. By construction, the LS estimator yields the best in-sample fit among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To answer this question, we evaluate the usefulness of the two popular bias correction methods, proposed by Hansen (1999) and So and Shin (1999), by comparing their out-of-sample forecast performances with that of the LS estimator. We find that bias-corrected estimators overall outperform the LS estimator. Especially, Hansen's grid bootstrap estimator combined with a rolling window method performs the best.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16780.

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Date of creation: May 2009
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Handle: RePEc:pra:mprapa:16780

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Keywords: Small-Sample Bias; Grid Bootstrap; Recursive Mean Adjustment; Out-of-Sample Forecast; Diebold-Mariano Test;

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  1. Kim, Hyeongwoo & Moh, Young-Kyu, 2012. "Examining the evidence of purchasing power parity by recursive mean adjustment," Economic Modelling, Elsevier, Elsevier, vol. 29(5), pages 1850-1857.
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  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 13(3), pages 253-63, July.
  4. Kim, Hyeongwoo & Stern, Liliana V. & Stern, Michael L., 2010. "Half-life bias correction and the G7 stock markets," Economics Letters, Elsevier, Elsevier, vol. 109(1), pages 1-3, October.
  5. Nikolay Gospodinov, 1999. "Median Unbiased Forecasts for Highly Persistent Autoregressive Processes," Computing in Economics and Finance 1999, Society for Computational Economics 533, Society for Computational Economics.
  6. Donald W.K. Andrews & Christopher J. Monahan, 1990. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 942, Cowles Foundation for Research in Economics, Yale University.
  7. Peter C.B. Phillips & Chi-Young Choi & Donggyu Sul, 2004. "Prewhitening Bias in HAC Estimation," Yale School of Management Working Papers, Yale School of Management ysm426, Yale School of Management.
  8. J�n Steinsson, 2008. "The Dynamic Behavior of the Real Exchange Rate in Sticky Price Models," American Economic Review, American Economic Association, American Economic Association, vol. 98(1), pages 519-33, March.
  9. Rossi, Barbara, 2005. "Confidence Intervals for Half-Life Deviations From Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 23, pages 432-442, October.
  10. Bruce E. Hansen, 1999. "The Grid Bootstrap And The Autoregressive Model," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 594-607, November.
  11. Masao Ogaki & Hyeongwoo Kim, 2009. "Purchasing Power Parity and the Taylor Rule," Working Papers, Ohio State University, Department of Economics 09-03, Ohio State University, Department of Economics.
  12. Kim, Jae H., 2003. "Forecasting autoregressive time series with bias-corrected parameter estimators," International Journal of Forecasting, Elsevier, Elsevier, vol. 19(3), pages 493-502.
  13. Andrews, Donald W K & Chen, Hong-Yuan, 1994. "Approximately Median-Unbiased Estimation of Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 12(2), pages 187-204, April.
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  15. Menelaos Karananos & S.H Sekioua & N Zeng, 2005. "On the order of integration of monthly US ex-ante and ex-post real interest rates new evidence from over a century of data," Money Macro and Finance (MMF) Research Group Conference 2005, Money Macro and Finance Research Group 21, Money Macro and Finance Research Group.
  16. Murray, Christian J. & Papell, David H., 2002. "The purchasing power parity persistence paradigm," Journal of International Economics, Elsevier, Elsevier, vol. 56(1), pages 1-19, January.
  17. Cheung, Yin-Wong & Lai, Kon S., 2000. "On the purchasing power parity puzzle," Journal of International Economics, Elsevier, Elsevier, vol. 52(2), pages 321-330, December.
  18. Taylor, Alan M, 2001. "Potential Pitfalls for the Purchasing-Power-Parity Puzzle? Sampling and Specification Biases in Mean-Reversion Tests of the Law of One Price," Econometrica, Econometric Society, Econometric Society, vol. 69(2), pages 473-98, March.
  19. Chi-Young Choi & Nelson C. Mark & Donggyu Sul, 2010. "Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 567-599, October.
  20. Kim, Hyeongwoo, 2009. "On the usefulness of the contrarian strategy across national stock markets: A grid bootstrap analysis," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(5), pages 734-744, December.
  21. So, Beong Soo & Shin, Dong Wan, 1999. "Recursive mean adjustment in time-series inferences," Statistics & Probability Letters, Elsevier, Elsevier, vol. 43(1), pages 65-73, May.
  22. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, Econometric Society, vol. 69(6), pages 1519-1554, November.
  23. Cook, Steven, 2002. "Correcting size distortion of the Dickey-Fuller test via recursive mean adjustment," Statistics & Probability Letters, Elsevier, Elsevier, vol. 60(1), pages 75-79, November.
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