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Understanding Spurious Regression in Financial Economics

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  • Ai Deng

Abstract

A new asymptotic framework is used to provide finite sample approximations for various statistics in the spurious return predictive regression analyzed by Ferson, Sarkissian, and Simin (2003a). Our theory explains all the findings of Ferson, Sarkissian, and Simin (2003a) and confirms the theoretical possibility of a spurious regression bias. The theory developed in the article has important implications with respect to existing inferential theories in predictive regressions. We also propose a simple diagnostic test to detect potential spurious regression bias in empirical analysis. The test is applied to four variants of the SP500 monthly stock returns and the six Fama-French benchmark portfolio monthly returns.

Suggested Citation

  • Ai Deng, 2014. "Understanding Spurious Regression in Financial Economics," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 12(1), pages 122-150.
  • Handle: RePEc:oup:jfinec:v:12:y:2014:i:1:p:122-150.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbs025
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    References listed on IDEAS

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    1. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," Review of Economic Studies, Oxford University Press, vol. 63(3), pages 435-463.
    2. Anne Vila Wetherilt & Simon Wells, 2004. "Long-horizon equity return predictability: some new evidence for the United Kingdom," Bank of England working papers 244, Bank of England.
    3. Khil, Jaeuk & Lee, Bong-Soo, 2002. "A Time-Series Model of Stock Returns with a Positive Short-Term Correlation and a Negative Long-Term Correlation," Review of Quantitative Finance and Accounting, Springer, vol. 18(4), pages 381-404, June.
    4. Carl Chiarella & Shenhuai Gao, 2002. "Type I Spurious Regression in Econometrics," Working Paper Series 114, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
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    Cited by:

    1. repec:bla:acctfi:v:58:y:2018:i:1:p:127-147 is not listed on IDEAS
    2. repec:wly:japmet:v:31:y:2016:i:7:p:1467-1477 is not listed on IDEAS
    3. Torben G. Andersen & Rasmus T. Varneskov, 2018. "Consistent Inference for Predictive Regressions in Persistent VAR Economies," CREATES Research Papers 2018-09, Department of Economics and Business Economics, Aarhus University.
    4. Daniela Osterrieder & Daniel Ventosa-Santaulària & J. Eduardo Vera-Valdés, 2015. "Unbalanced Regressions and the Predictive Equation," CREATES Research Papers 2015-09, Department of Economics and Business Economics, Aarhus University.
    5. 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.
    6. Ai Deng Author-X-Name-First: Ai, 2006. "Local Power of Andrews and Ploberger Tests Against Nearly Integrated, Nearly White Noise Process," Boston University - Department of Economics - Working Papers Series WP2006-027, Boston University - Department of Economics.
    7. repec:eee:pacfin:v:50:y:2018:i:c:p:263-278 is not listed on IDEAS
    8. Powell, John G. & Shi, Jing & Smith, Tom & Whaley, Robert E., 2009. "Political regimes, business cycles, seasonalities, and returns," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1112-1128, June.
    9. repec:eee:empfin:v:45:y:2018:i:c:p:269-282 is not listed on IDEAS
    10. Gerdie Everaert & Hauke Vierke, 2016. "Demographics and Business Cycle Volatility: A Spurious Relationship?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1467-1477, November.
    11. repec:bpj:sndecm:v:22:y:2018:i:3:p:14:n:1 is not listed on IDEAS
    12. Deng, Ai, 2010. "Local power of consistent tests for serial correlation against the nearly integrated, nearly white noise process," Economics Letters, Elsevier, vol. 107(1), pages 22-25, April.

    More about this item

    Keywords

    spurious regression; signal-to-noise ratio; fixed-b asymptotics; data mining; stock return predictability;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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