IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v12y2013i1p122-150.html
   My bibliography  Save this article

Understanding Spurious Regression in Financial Economics

Author

Listed:
  • 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. Copyright The Author, 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.

Suggested Citation

  • Ai Deng, 2013. "Understanding Spurious Regression in Financial Economics," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 122-150, December.
    • Handle: RePEc:oup:jfinec:v:12:y:2013:i:1:p:122-150
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbs025
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots," NBER Chapters, in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220, National Bureau of Economic Research, Inc.
    2. Amihud, Yakov & Hurvich, Clifford M., 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 813-841, December.
    3. 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.
    4. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
    5. Moon, Hyungsik R. & Phillips, Peter C.B., 2000. "Estimation Of Autoregressive Roots Near Unity Using Panel Data," Econometric Theory, Cambridge University Press, vol. 16(6), pages 927-997, December.
    6. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    7. Nabeya, Seiji & Perron, Pierre, 1994. "Local asymptotic distribution related to the AR(1) model with dependent errors," Journal of Econometrics, Elsevier, vol. 62(2), pages 229-264, June.
    8. Elliott, Graham & Stock, James H., 1994. "Inference in Time Series Regression When the Order of Integration of a Regressor is Unknown," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 672-700, August.
    9. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 63(3), pages 435-463.
    10. Phillips, Peter C.B. & Moon, Hyungsik Roger & Xiao, Zhijie, 2001. "How To Estimate Autoregressive Roots Near Unity," Econometric Theory, Cambridge University Press, vol. 17(1), pages 29-69, February.
    11. Peter C. B. Phillips, 1998. "New Tools for Understanding Spurious Regressions," Econometrica, Econometric Society, vol. 66(6), pages 1299-1326, November.
    12. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    13. 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.
    14. Cavanagh, Christopher L. & Elliott, Graham & Stock, James H., 1995. "Inference in Models with Nearly Integrated Regressors," Econometric Theory, Cambridge University Press, vol. 11(5), pages 1131-1147, October.
    15. 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.
    16. Shively, Philip A, 2000. "Stationary Components in Stock Prices: An Exact Pointwise Most Powerful Invariant Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 489-496, October.
    17. Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-1354, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kam Fong Chan & John G. Powell & Jing Shi & Tom Smith, 2018. "Dividend persistence and dividend behaviour," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(1), pages 127-147, March.
    2. 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.
    3. Gourieroux, Christian & Jasiak, Joann, 2010. "Inference for Noisy Long Run Component Process," MPRA Paper 98987, University Library of Munich, Germany.
    4. Lin, Boqiang & Omoju, Oluwasola E., 2017. "Focusing on the right targets: Economic factors driving non-hydro renewable energy transition," Renewable Energy, Elsevier, vol. 113(C), pages 52-63.
    5. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Coqueret, Guillaume & Deguest, Romain, 2024. "Unexpected opportunities in misspecified predictive regressions," European Journal of Operational Research, Elsevier, vol. 318(2), pages 686-700.
    7. 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.
    8. Docherty, Paul & Hurst, Gareth, 2018. "Return dispersion and conditional momentum returns: International evidence," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 263-278.
    9. 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.
    10. Andersen, Torben G. & Varneskov, Rasmus T., 2021. "Consistent inference for predictive regressions in persistent economic systems," Journal of Econometrics, Elsevier, vol. 224(1), pages 215-244.
    11. Shuping Cheng & Lingjie Meng & Weizhong Wang, 2022. "The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    12. 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.
    13. Guillaume Coqueret & Romain Deguest, 2024. "Unexpected opportunities in misspecified predictive regressions," Post-Print hal-04595355, HAL.
    14. 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.
    15. 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.
    16. Xu, Bin & Lin, Boqiang, 2016. "Reducing CO2 emissions in China's manufacturing industry: Evidence from nonparametric additive regression models," Energy, Elsevier, vol. 101(C), pages 161-173.
    17. Wang Cindy Shin-Huei & Hafner Christian M., 2018. "A simple solution of the spurious regression problem," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(3), pages 1-14, June.
    18. Hjalmarsson, Erik, 2018. "Maximal predictability under long-term mean reversion," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 269-282.
    19. Deng, Kaihua, 2016. "A refined asymptotic framework for dividend yield in predictive regressions," Economics Letters, Elsevier, vol. 138(C), pages 60-63.
    20. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cosme Vodounou, 1998. "Inférence fondée sur les statistiques des rendements de long terme," CIRANO Working Papers 98s-20, CIRANO.
    2. Chi-Young Choi & Ling Hu & Masao Ogaki, 2005. "Structural Spurious Regressions and A Hausman-type Cointegration Test," RCER Working Papers 517, University of Rochester - Center for Economic Research (RCER).
    3. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    4. Tilak Abeysinghe & Gulasekaran Rajaguru, 2009. "A Gaussian Test for Cointegration," Microeconomics Working Papers 22013, East Asian Bureau of Economic Research.
    5. Peter C. B. Phillips, 2003. "Laws and Limits of Econometrics," Economic Journal, Royal Economic Society, vol. 113(486), pages 26-52, March.
    6. Peter Phillips & Hyungsik Moon, 2000. "Nonstationary panel data analysis: an overview of some recent developments," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 263-286.
    7. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2021. "Simple tests for stock return predictability with good size and power properties," Journal of Econometrics, Elsevier, vol. 224(1), pages 198-214.
    8. D. Ventosa-Santaulària, 2009. "Spurious Regression," Journal of Probability and Statistics, Hindawi, vol. 2009, pages 1-27, August.
    9. Ferson, Wayne E. & Sarkissian, Sergei & Simin, Timothy, 2008. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(2), pages 331-353, June.
    10. E. E. Ioannidis & G. A. Chronis, 2005. "Extreme Spectra of Var Models and Orders of Near‐Cointegration," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(3), pages 399-421, May.
    11. Yicong Lin & Hanno Reuvers, 2020. "Cointegrating Polynomial Regressions with Power Law Trends: Environmental Kuznets Curve or Omitted Time Effects?," Papers 2009.02262, arXiv.org, revised Dec 2021.
    12. Hyungsik R. Moon & Peter C.B. Phillips, 1999. "Maximum Likelihood Estimation in Panels with Incidental Trends," Cowles Foundation Discussion Papers 1246, Cowles Foundation for Research in Economics, Yale University.
    13. 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.
    14. Michael Jansson & Marcelo J. Moreira, 2006. "Optimal Inference in Regression Models with Nearly Integrated Regressors," Econometrica, Econometric Society, vol. 74(3), pages 681-714, May.
    15. Peter C.B. Phillips, 2001. "Bootstrapping Spurious Regression," Cowles Foundation Discussion Papers 1330, Cowles Foundation for Research in Economics, Yale University.
    16. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    17. Moon, Hyungsik R. & Phillips, Peter C.B., 2000. "Estimation Of Autoregressive Roots Near Unity Using Panel Data," Econometric Theory, Cambridge University Press, vol. 16(6), pages 927-997, December.
    18. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    19. Phillips, Peter C. B., 2002. "New unit root asymptotics in the presence of deterministic trends," Journal of Econometrics, Elsevier, vol. 111(2), pages 323-353, December.
    20. repec:wyi:journl:002108 is not listed on IDEAS
    21. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jfinec:v:12:y:2013:i:1:p:122-150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.