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A simple parametric model selection test

Authors: Susanne M. Schennach and Daniel Wilhelm
Date: 14 March 2014
Type: cemmap Working Paper, CWP10/14
DOI: 10.1920/wp.cem.2014.1014


We propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly speci fied or misspeci fied, they may be nested, non-nested, strictly non-nested or overlapping. Unlike in previous testing approaches, no pre-testing is needed, since in each case, the same test statistic together with a standard normal critical value can be used. The new procedure controls asymptotic size uniformly over a large class of data generating processes. We demonstrate its finite sample properties in a Monte Carlo experiment and its practical relevance in an empirical application comparing Keynesian versus new classical macroeconomic models.

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Susanne M. Schennach and Daniel Wilhelm August 2016, A simple parametric model selection test, cemmap Working Paper, CWP30/16, IFS

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