IDEAS home Printed from https://ideas.repec.org/a/bla/obuest/v83y2021i2p571-588.html
   My bibliography  Save this article

A Unified test for the Intercept of a Predictive Regression Model

Author

Listed:
  • Xiaohui Liu
  • Yuzi Liu
  • Yao Rao
  • Fucai Lu

Abstract

Testing the predictability of the predictive regression model is of great interest in economics and finance. Recently, (Zhu et al. (2014) Predictive regressions for macroeconomic data, Vol. 8, pp. 577–594.) proposed a unified test to account for this issue. Their test has a desirable property that its limit distribution is standard regardless of the regressor being stationary, near unit root or unit root. However, this test depends on, a priori, whether there is an intercept in the predictive regression while this is usually unknown in practice. In this paper, using empirical likelihood inference, we develop a unified pretest for the intercept, as a pretest to determine the choice of the predictability test. Simulations studies confirm that the proposed pretest works well. Two real data examples are also provided to illustrate the importance of such pretest. The first revisits the S&P 500 index data and the second investigates stock return predictability and investor sentiment for six countries.

Suggested Citation

  • Xiaohui Liu & Yuzi Liu & Yao Rao & Fucai Lu, 2021. "A Unified test for the Intercept of a Predictive Regression Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 571-588, April.
    • Handle: RePEc:bla:obuest:v:83:y:2021:i:2:p:571-588
    DOI: 10.1111/obes.12408
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/obes.12408
    Download Restriction: no
    File URL: https://libkey.io/10.1111/obes.12408?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
    2. Basrak, Bojan & Davis, Richard A. & Mikosch, Thomas, 2002. "Regular variation of GARCH processes," Stochastic Processes and their Applications, Elsevier, vol. 99(1), pages 95-115, May.
    3. Li, D. & Chan, N. H. & Peng, L., 2014. "Empirical Likelihood Test For Causality Of Bivariate Ar(1) Processes," Econometric Theory, Cambridge University Press, vol. 30(2), pages 357-371, April.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Georgiev, Iliyan & Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2018. "Testing for parameter instability in predictive regression models," Journal of Econometrics, Elsevier, vol. 204(1), pages 101-118.
    6. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    7. Fukang Zhu & Zongwu Cai & Liang Peng, 2014. "Predictive regressions for macroeconomic data," Papers 1404.7642, arXiv.org.
    8. Peter C. B. Phillips, 2015. "Pitfalls and Possibilities in Predictive Regression," Cowles Foundation Discussion Papers 2003, Cowles Foundation for Research in Economics, Yale University.
    9. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    10. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    11. Iliyan Georgiev & David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2019. "A Bootstrap Stationarity Test for Predictive Regression Invalidity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 528-541, July.
    12. So, Beong Soo & Shin, Dong Wan, 1999. "Cauchy Estimators For Autoregressive Processes With Applications To Unit Root Tests And Confidence Intervals," Econometric Theory, Cambridge University Press, vol. 15(2), pages 165-176, April.
    13. José Angel Roldán Casas & Rafaela Dios-Palomares, 2004. "A Strategy for Testing the Unit Root in AR(1) Model with Intercept. A Monte Carlo Experiment," Economic Working Papers at Centro de Estudios Andaluces E2004/37, Centro de Estudios Andaluces.
    14. Michael Lemmon & Evgenia Portniaguina, 2006. "Consumer Confidence and Asset Prices: Some Empirical Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 19(4), pages 1499-1529.
    15. Anna Mikusheva, 2007. "Uniform Inference in Autoregressive Models," Econometrica, Econometric Society, vol. 75(5), pages 1411-1452, September.
    Full references (including those not matched with items on IDEAS)

    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. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    2. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    3. 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).
    4. 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.
    5. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    6. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.
    7. Sayim, Mustafa & Rahman, Hamid, 2015. "An examination of U.S. institutional and individual investor sentiment effect on the Turkish stock market," Global Finance Journal, Elsevier, vol. 26(C), pages 1-17.
    8. Zhou, Weilun & Gao, Jiti & Harris, David & Kew, Hsein, 2024. "Semi-parametric single-index predictive regression models with cointegrated regressors," Journal of Econometrics, Elsevier, vol. 238(1).
    9. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
    10. Tu, Yundong & Xie, Xinling, 2023. "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, vol. 237(1).
    11. Massimo Guidolin & Erwin Hansen & Gabriel Cabrera, 2023. "Time-Varying Risk Aversion and International Stock Returns," BAFFI CAREFIN Working Papers 23203, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    12. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    13. Fukang Zhu & Mengya Liu & Shiqing Ling & Zongwu Cai, 2020. "Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202021, University of Kansas, Department of Economics, revised Dec 2020.
    14. Kadilli, Anjeza, 2015. "Predictability of stock returns of financial companies and the role of investor sentiment: A multi-country analysis," Journal of Financial Stability, Elsevier, vol. 21(C), pages 26-45.
    15. Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.
    16. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    17. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    18. ALAJEKWU, Udoka Bernard & OBIALOR, Michael Chukwumee & OKORO, Cyprian Okey, 2017. "Ffect Of Investor Sentiment On Future Returns In The Nigerian Stock Market," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 17(2), pages 103-126.
    19. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2014. "Facebook's daily sentiment and international stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 730-743.
    20. Lutz, Chandler, 2015. "The impact of conventional and unconventional monetary policy on investor sentiment," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 89-105.

    More about this item

    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:bla:obuest:v:83:y:2021:i:2:p:571-588. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sfeixuk.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.