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The Stambaugh bias in panel predictive regressions

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Abstract
This paper analyzes predictive regressions in a panel data setting. The standard fixed effects estimator suffers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions. Monte Carlo evidence shows that the bias and resulting size distortions can be severe. A new bias-corrected estimator is proposed, which is shown to work well in finite samples and to lead to approximately normally distributed t-statistics. Overall, the results show that the econometric issues associated with predictive regressions when using time-series data to a large extent also carry over to the panel case. The results are illustrated with an application to predictability in international stock indices.

Suggested Citation

  • Erik Hjalmarsson, 2007. "The Stambaugh bias in panel predictive regressions," International Finance Discussion Papers 914, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:914
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    References listed on IDEAS

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    Cited by:

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    2. Lawrenz, Jochen & Zorn, Josef, 2018. "Decomposing the predictive power of local and global financial valuation ratios," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 137-149.
    3. Skintzi, Vasiliki D., 2019. "Determinants of stock-bond market comovement in the Eurozone under model uncertainty," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 20-28.
    4. Chengwang Liao & Ziwei Mei & Zhentao Shi, 2024. "Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions," Papers 2410.09825, arXiv.org.
    5. Ayelen Banegas, 2016. "Predictability of Growth in Emerging Markets: Information in Financial Aggregates," International Finance Discussion Papers 1174, Board of Governors of the Federal Reserve System (U.S.).
    6. Westerlund, Joakim & Narayan, Paresh, 2016. "Testing for predictability in panels of any time series dimension," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1162-1177.
    7. Westerlund, Joakim & Narayan, Paresh Kumar & Zheng, Xinwei, 2015. "Testing for stock return predictability in a large Chinese panel," Emerging Markets Review, Elsevier, vol. 24(C), pages 81-100.
    8. Robin Greenwood & Samuel G. Hanson & Andrei Shleifer & Jakob Ahm Sørensen, 2022. "Predictable Financial Crises," Journal of Finance, American Finance Association, vol. 77(2), pages 863-921, April.
    9. Stephan Smeekes & Joakim Westerlund, 2019. "Robust block bootstrap panel predictability tests," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1089-1107, October.
    10. Hagenhoff, Tim & Lustenhouwer, Joep, 2020. "The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations," BERG Working Paper Series 163, Bamberg University, Bamberg Economic Research Group.
    11. Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2018. "Is stock return predictability time-varying?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 152-172.
    12. Wang, Cindy S.H. & Chen, Yi-Chi & Lo, Hsin-Yu, 2021. "A fresh look at the risk-return tradeoff," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    13. Victor Troster & José Penalva & Abderrahim Taamouti & Dominik Wied, 2021. "Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1291-1309, November.
    14. Malte Rengel, 2020. "Sustainability of European fiscal balances: Just a statistical artifact?," Empirical Economics, Springer, vol. 58(4), pages 1681-1712, April.
    15. Hagenhoff, Tim & Lustenhouwer, Joep, 2020. "The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations," Working Papers 0686, University of Heidelberg, Department of Economics.
    16. Karabiyik, Hande & Westerlund, Joakim & Narayan, Paresh, 2016. "On the estimation and testing of predictive panel regressions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 115-125.

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    Keywords

    Panel analysis; Stocks;

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