This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of...

In this paper we examine the properties of a simple criterion-based, likelihood ratio type test of parameter restrictions for standard GMM estimators in autoregressive panel data models. A comparison is made with recent test proposals based on the continuously-updated GMM criterion (Hansen,...

In the paper, we consider generalized method of moments (GMM) and propose statistics to test relevance, conditional relevance and redundancy of moment conditions, as well as they modifications to check if moment conditions help with identification of a particular set of unknown parameters. The...

We propose non-nested tests for competing conditional moment restriction models using a method of empirical likelihood. Our tests are based on the method of conditional empirical likelihood developed by Kitamura, Tripathi and Ahn (2004) and Zhang and Gijbels (2003). By using the conditional...

This paper proposes a new test of the null hypothesis that a generalized method of moments model is identified. The test can detect local or global underidentification, and underidentification in some or all directions. The idea of the test is to compare the volume of two confidence sets - one...

The paper provides a new class of over-identification tests that are robust to heteroscedasticity and autocorrelation of unknown forms. The tests are based on the series long run variance estimator that is designed to pivotalize the moment restrictions. We show that when the number of terms used...