Non-Nested Models and the Likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap Based Tests

U of London Queen Mary Economics Working Paper No. 490

31 Pages Posted: 9 Jun 2003

See all articles by George Kapetanios

George Kapetanios

King's College, London

Melvyn Weeks

University of Cambridge - Faculty of Economics and Politics

Date Written: April 2003

Abstract

We consider an alternative use of simulation in the context of using the Likelihood-Ratio statistic to test non-nested models. To date simulation has been used to estimate the Kullback-Leibler measure of closeness between two densities, which in turn 'mean adjusts' the Likelihood-Ratio statistic. Given that this adjustment is still based upon asymptotic arguments, an alternative procedure is to utilise bootstrap procedures to construct the empirical density. To our knowledge this study represents the first comparison of the properties of bootstrap and simulation-based tests applied to non-nested tests. More specifically, the design of experiments allows us to comment on the relative performance of these two testing frameworks across models with varying degrees of nonlinearity. In this respect although the primary focus of the paper is upon the relative evaluation of simulationand bootstrap-based nonnested procedures in testing across a class of nonlinear threshold models, the inclusion of a similar analysis of the more standard linear/log-linear models provides a point of comparison.

JEL Classification: C15, C52

Suggested Citation

Kapetanios, George and Weeks, Melvyn, Non-Nested Models and the Likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap Based Tests (April 2003). U of London Queen Mary Economics Working Paper No. 490, Available at SSRN: https://ssrn.com/abstract=394341 or http://dx.doi.org/10.2139/ssrn.394341

George Kapetanios (Contact Author)

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Melvyn Weeks

University of Cambridge - Faculty of Economics and Politics ( email )

Austin Robinson Building
Sidgwick Avenue
Cambridge, CB3 9DD
United Kingdom

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