City Research Online

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.

Publication Type:	Article
Additional Information:	This is the peer reviewed version of the following article: Britten-Jones, M., Neuberger, A. and Nolte, I. (2011), Improved Inference in Regression with Overlapping Observations. Journal of Business Finance & Accounting, 38: 657–683., which has been published in final form at http://dx.doi.org/10.1111/j.1468-5957.2011.02244.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Publisher Keywords:	Long horizon, stock return predictability, induced autocorrelation
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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