Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target Zone Literature

Samples with overlapping observations are used for the study of uncovered interest rate parity, the predictability of long run stock returns, and the credibility of exchange rate target zones. This paper quantifies the biases in parameter estimation and size distortions of hypothesis tests of overlapping linear and polynomial autoregressions, which have been used in target zone applications. We show that both estimation bias and size distortions generally depend on the amount of overlap, the sample size, and the autoregressive root of the data generating process. In particular, the estimates are biased in a way that makes it more likely that the predictions of the Bertola-Svensson-model will be supported. Size distortions of various tests also turn out to be substantial even when using a heteroskedasticity and autocorrelation consistent covariance matrix.