Premium
Improved Inference in Regression with Overlapping Observations
Author(s) -
BrittenJones Mark,
Neuberger Anthony,
Nolte Ingmar
Publication year - 2011
Publication title -
journal of business finance and accounting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.282
H-Index - 77
eISSN - 1468-5957
pISSN - 0306-686X
DOI - 10.1111/j.1468-5957.2011.02244.x
Subject(s) - inference , regression analysis , mathematics , monte carlo method , linear regression , transformation (genetics) , regression , design matrix , statistical inference , standard error , statistics , computer science , econometrics , algorithm , artificial intelligence , biochemistry , chemistry , gene
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.