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A simple solution of the spurious regression problem

Author

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  • Wang Cindy Shin-Huei

    (National Tsing Hua University, Department of Quantitative Finance, Hsinchu City, Taiwan)

  • Hafner Christian M.

    (Institut de statistique, biostatistique et sciences actuarielles, and CORE, Université catholique de Louvain, Voie du Roman Pays, 20, 1348Louvain-la-Neuve, Belgium)

Abstract

This paper develops a new estimator for cointegrating and spurious regressions by applying a two-stage generalized Cochrane-Orcutt transformation based on an autoregressive approximation framework, even though the exact form of the error term is unknown in practice. We prove that our estimator is consistent for a wide class of regressions. We further show that a convergent usual t-statistic based on our new estimator can be constructed for the spurious regression cases analyzed by (Granger, C. W. J., and P. Newbold. 1974. “Spurious Regressions in Econometrics.” Journal of Econometrics 74: 111–120) and (Granger, C. W. J., N. Hyung, and H. Jeon. 2001. “Spurious Regressions with Stationary Series.” Applied Economics 33: 899–904). The implementation of our estimator is easy since it does not necessitate estimation of the long-run variance. Simulation results indicate the good statistical properties of the new estimator in small and medium samples, and also consider a more general framework including multiple regressors and endogeneity.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sndecm:v:22:y:2018:i:3:p:14:n:1
    DOI: 10.1515/snde-2015-0040
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    More about this item

    Keywords

    autoregressive approximation; cointegration; generalized Cochrane-Orcutt estimation; spurious regression;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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