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A simple parametric model selection test

Listed author(s):
  • Susanne M. Schennach

    ()

    (Institute for Fiscal Studies and Brown University)

  • Daniel Wilhelm

    ()

    (Institute for Fiscal Studies and cemmap and UCL)

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 ed or misspeci ed, 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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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP30/16.

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Date of creation: 02 Aug 2016
Handle: RePEc:ifs:cemmap:30/16
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