A simple solution of the spurious regression problem
Wang Cindy Shin-Huei and
Christian Hafner ()
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Wang Cindy Shin-Huei: National Tsing Hua University, Department of Quantitative Finance, Hsinchu City, Taiwan
Studies in Nonlinear Dynamics & Econometrics, 2018, vol. 22, issue 3, 14
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.
Keywords: autoregressive approximation; cointegration; generalized Cochrane-Orcutt estimation; spurious regression (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
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