@inbook{10.2307/j.ctt9qf9km.12,
ISBN = {9780262028356},
URL = {http://www.jstor.org/stable/j.ctt9qf9km.12},
abstract = {Empirical models could be chosen according to many criteria, as this chapter discusses. Selection criteria can conflict, such as achieving empirical congruence may thwart theory consistency and vice versa, so we consider nine possible ways to judge the success of selection. Of these, four seem infeasible, two are widely used but seem suspect, so we focus on the remaining three practical criteria, namely a selection algorithmâ€™s ability to recover the local data-generating process (LDGP) starting from the general unrestricted model as often as when starting from the LDGP itself; whether the operating characteristics of the algorithm match their desired properties;},
author = {David F. Hendry and Jurgen A. Doornik},
booktitle = {Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics},
pages = {71--84},
publisher = {MIT Press},
title = {Evaluating Model Selection},
year = {2014}
}