@inbook{10.2307/j.ctt9qf9km.17,
ISBN = {9780262028356},
URL = {http://www.jstor.org/stable/j.ctt9qf9km.17},
abstract = {We develop approximate bias corrections for the conditional distributions of the estimated parameters of retained variables after model selection, such that approximately unbiased estimates of their coefficients are delivered. Such corrections also drive estimated coefficients of irrelevant variables towards the origin, substantially reducing their mean squared errors (MSEs). We illustrate the theory by simulating selection from N = 1000 variables, to examine the impacts of our approach on estimated coefficient MSE s for both relevant and irrelevant variables in their conditional and unconditional distributions.The estimates from the selected model do not have the same properties as if the LDGP},
author = {David F. Hendry and Jurgen A. Doornik},
booktitle = {Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics},
pages = {133--140},
publisher = {MIT Press},
title = {Bias Correcting Selection Effects},
year = {2014}
}