An unbiased model comparison test using cross-validation

Social scientists often consider multiple empirical models of the same process. When these models are parametric and non-nested, the null hypothesis that two models fit the data equally well is commonly tested using methods introduced by Vuong (Econometrica 57(2):307–333, <CitationRef CitationID="CR30">1989</CitationRef>) and Clarke (Am J Political Sci 45(3):724–744, <CitationRef CitationID="CR8">2001</CitationRef>; J Confl Resolut 47(1):72–93, <CitationRef CitationID="CR9">2003</CitationRef>; Political Anal 15(3):347–363, <CitationRef CitationID="CR10">2007</CitationRef>). The objective of each is to compare the Kullback–Leibler Divergence (KLD) of the two models from the true model that generated the data. Here we show that both of these tests are based upon a biased estimator of the KLD, the individual log-likelihood contributions, and that the Clarke test is not proven to be consistent for the difference in KLDs. As a solution, we derive a test based upon cross-validated log-likelihood contributions, which represent an unbiased KLD estimate. We demonstrate the CVDM test’s superior performance via simulation, then apply it to two empirical examples from political science. We find that the test’s selection can diverge from those of the Vuong and Clarke tests and that this can ultimately lead to differences in substantive conclusions. Copyright Springer Science+Business Media Dordrecht 2014