Improved Inference in Regression with Overlapping Observations

27 Pages Posted: 7 Jul 2011

See all articles by Mark Britten-Jones

Mark Britten-Jones

London Business School - Institute of Finance and Accounting

Anthony Neuberger

City University London - Faculty of Finance

Ingmar Nolte

Lancaster University - Department of Accounting and Finance

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Date Written: June/July 2011

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.

Keywords: long horizon, stock return predictability, induced autocorrelation

Suggested Citation

Britten-Jones, Mark and Neuberger, Anthony and Nolte, Ingmar, Improved Inference in Regression with Overlapping Observations (June/July 2011). Journal of Business Finance & Accounting, Vol. 38, Issue 5‐6, pp. 657-683, 2011. Available at SSRN: https://ssrn.com/abstract=1880458 or http://dx.doi.org/10.1111/j.1468-5957.2011.02244.x

Mark Britten-Jones (Contact Author)

London Business School - Institute of Finance and Accounting ( email )

Sussex Place
Regent's Park
London NW1 4SA
United Kingdom

Anthony Neuberger

City University London - Faculty of Finance ( email )

London, EC2Y 8HB
Great Britain

Ingmar Nolte

Lancaster University - Department of Accounting and Finance ( email )

Lancaster, Lancashire LA1 4YX
United Kingdom

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