The properties of automatic model selection are discussed, focusing on PcGets. We explain the background concepts and why automatic methods can perform well. Criticisms of model selection procedures are noted and rebutted. The algorithm is sketched, distinguishing between costs of search and...

After reviewing the simulation performance of general-to-specific automatic regression-model selection, as embodied in "PcGets", we show how model selection can be non-distortionary: approximately unbiased 'selection estimates' are derived, with reported standard errors close to the sampling...

The controversy over the selection of 'growth regressions' was precipitated by some remarkably numerous 'estimation' strategies, including two million regressions by Sala-i-Martin ["American Economic Review" (1997b) Vol. 87, pp. 178-183]. Only one regression is really needed, namely the general...

We establish the consistency of the selection procedures embodied in "PcGets", and compare their performance with other model selection criteria in linear regressions. The significance levels embedded in the "PcGets" Liberal and Conservative algorithms coincide in very large samples with those...

When the DGP is nested in the model, PcGets delivers high performance selection across different (unknown) states of nature. One of its steps involves sub-sample post-selection assessment, and here we consider its properties and investigate its practical application. The simulation results show...

The recent controversy over model selection in the context of `growth regressions' has led to some remarkably numerous `estimation' strategies, including 4 million regressions by Sala-i-Martin (1997b). Only one regression is really needed, namely the general unrestricted model, appropriately...