Improved inference in regression with overlapping observations

Britten-Jones, Mark and Neuberger, Anthony and Nolte, Ingmar (2011) Improved inference in regression with overlapping observations. Journal of Business Finance and Accounting, 38 (5-6). pp. 657-683. ISSN 0306-686X

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Business Finance and Accounting
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1402
Subjects:
ID Code:
64691
Deposited By:
Deposited On:
21 May 2013 08:37
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 12:34