Spurious regression under broken trend stationarity, Working paper

We study the phenomenon of spurious regression between two random vari-ables, when the generating mechanism of individual series is assumed to follow a stationary process around a trend with (possibly) multiple breaks in the level and slope of trend. We develop the relevant asymptotic theory and show that the phenomenon of spurious regression occurs independently of the structure assumed for the errors. In contrast to previous findings, the presence of a spu-rious relationship will be less severe when breaks are present in the generating mechanism of individual series. This is true whether the regression model in-cludes a linear trend or not. Simulations confirm our asymptotic results, and reveal that in finite samples, the phenomenon of spurious regression is sensitive to the presence of a linear trend in the regression model, and to the relative location of breaks within the sample