摘要
Spurious regression has been extensively studied in time series econometrics since Granger and Newbold’s seminal paper. Recently, it has been advanced that this phenomenon is due to a mistreatment of short-range autocorrelation in the residuals of the regression when at least one of the variables in a bivariate regression is stationary. HAC errors, feasible GLS and Cochrane-Orcutt-type procedures are then proposed to draw correct inference. Such a proposal should be cautiously considered, since nonsense inference might also be due to deterministic trend mechanisms, structural breaks, and long range dependence. In these cases, standard autocorrelation correction procedures would not solve the problem of spurious regression. We aim to make the later argument clear.
Spurious regression has been extensively studied in time series econometrics since Granger and Newbold’s seminal paper. Recently, it has been advanced that this phenomenon is due to a mistreatment of short-range autocorrelation in the residuals of the regression when at least one of the variables in a bivariate regression is stationary. HAC errors, feasible GLS and Cochrane-Orcutt-type procedures are then proposed to draw correct inference. Such a proposal should be cautiously considered, since nonsense inference might also be due to deterministic trend mechanisms, structural breaks, and long range dependence. In these cases, standard autocorrelation correction procedures would not solve the problem of spurious regression. We aim to make the later argument clear.
