Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Improved inference in regression with overlapping observations
AU - Britten-Jones, Mark
AU - Neuberger, Anthony
AU - Nolte, Ingmar
PY - 2011/6
Y1 - 2011/6
N2 - 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.
AB - 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.
KW - long horizon
KW - stock return predictability
KW - induced autocorrelation
U2 - 10.1111/j.1468-5957.2011.02244.x
DO - 10.1111/j.1468-5957.2011.02244.x
M3 - Journal article
VL - 38
SP - 657
EP - 683
JO - Journal of Business Finance and Accounting
JF - Journal of Business Finance and Accounting
SN - 0306-686X
IS - 5-6
ER -