Improved Inference and Estimation in Regression With Overlapping Observations

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-Weststandard errors) are asymptotically valid when applied to the transformed regression. Through Monte Carlo analysis we show that they perform 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 two empirical applications. JEL classification: C20, G12

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