Improved Inference in Regression with Overlapping Observations
提出一种通过简单聚合解释变量矩阵来转换回归的方法，消除重叠观测导致的误差自相关，使常规推断方法（如OLS、White、Newey-West标准误）在转换后回归中渐近有效，蒙特卡洛模拟显示有限样本表现优于现有方法。
Abstract: We present an improved method for inference in linear regressions with overlapping observations. By aggregating the matrix of explanatory variables in a simple way, our method transforms the original regression into an equivalent representation in which the dependent variables are non‐overlapping. This transformation removes that part of the autocorrelation in the error terms which is induced by the overlapping scheme. Our method can easily be applied within standard software packages since conventional inference procedures (OLS‐, White‐, Newey‐West‐ standard errors) are asymptotically valid when applied to the transformed regression. Through Monte Carlo analysis we show that it performs better in finite samples than the methods applied to the original regression that are in common usage. We illustrate the significance of our method with three empirical applications.