Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target‐Zone Literature*
量化了重叠观测数据下线性与多项式自回归模型的参数估计偏差和假设检验的规模扭曲，发现重叠越多、样本越小、自回归根越接近1时偏差和扭曲越大，且估计偏差倾向于支持Bertola-Svensson模型。
Abstract 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 of hypothesis tests are generally larger, if the amount of overlap is larger, the sample size is smaller, and autoregressive root of the data‐generating process is closer to unity. 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.