Improved Inference in Regression with Overlapping Observations
London Business School - Institute of Finance and Accounting
University of Warwick - Warwick Business School
Warwick Business School - Finance Group - Financial Econometrics Research Centre
March 3, 2010
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.
Number of Pages in PDF File: 40
Keywords: Long horizon, stock return predictability, induced autocorrelation
JEL Classification: C20, G12working papers series
Date posted: June 23, 2004 ; Last revised: November 16, 2011
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