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Multiple-Predictor Regressions: Hypothesis Testing

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Author Info

  • Yakov Amihud
  • Clifford M. Hurvich
  • Yi Wang

Abstract

We propose a new hypothesis-testing method for multipredictor regressions in small samples, where the dependent variable is regressed on lagged variables that are autoregressive. The new test is based on the augmented regression method (Amihud and Hurvich, 2004), which produces reduced-bias coefficients and is easy to implement. The method's usefulness is demonstrated by simulations and by testing a model where stock returns are predicted by two variables, income-to-consumption and dividend yield. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/rfs/hhn056
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Bibliographic Info

Article provided by Society for Financial Studies in its journal The Review of Financial Studies.

Volume (Year): 22 (2009)
Issue (Month): 1 (January)
Pages: 413-434

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Handle: RePEc:oup:rfinst:v:22:y:2009:i:1:p:413-434

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Cited by:
  1. Ferson, Wayne & Nallareddy, Suresh & Xie, Biqin, 2013. "The “out-of-sample” performance of long run risk models," Journal of Financial Economics, Elsevier, vol. 107(3), pages 537-556.
  2. Schrimpf, Andreas, 2008. "International Stock Return Predictability Under Model Uncertainty," ZEW Discussion Papers 08-048, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  3. Bakshi, Gurdip & Panayotov, George, 2013. "Predictability of currency carry trades and asset pricing implications," Journal of Financial Economics, Elsevier, vol. 110(1), pages 139-163.
  4. Stig V. Møller & Jesper Rangvid, 2012. "End-of-the-year economic growth and time-varying expected returns," CREATES Research Papers 2012-42, School of Economics and Management, University of Aarhus.
  5. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
  6. Tom Engsted & Thomas Q. Pedersen, 2014. "Bias-Correction in Vector Autoregressive Models: A Simulation Study," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 45-71, March.
  7. Paulo M.M. Rodrigues & Antonio Rubia, 2011. "A Class of Robust Tests in Augmented Predictive Regressions," Working Papers w201126, Banco de Portugal, Economics and Research Department.
  8. Engsted, Tom & Pedersen, Thomas Q., 2012. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 241-253.
  9. Nuno Silva, 2013. "Equity Premia Predictability in the EuroZone," GEMF Working Papers 2013-22, GEMF - Faculdade de Economia, Universidade de Coimbra.
  10. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
  11. Fukang Zhu & Zongwu Cai & Liang Peng, 2014. "Predictive regressions for macroeconomic data," Papers 1404.7642, arXiv.org.
  12. Wayne E. Ferson & Suresh K. Nallareddy & Biqin Xie, 2012. "The "Out of Sample" Performance of Long-run Risk Models," NBER Working Papers 17848, National Bureau of Economic Research, Inc.

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