WALD. LIKELIHOOD RATIO, AND LAGRANGE MULTiPLIER TESTS IN ECONOMETRICS

The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note

Regression spline bivariate probit models: A practical approach to testing for exogeneity

Bivariate probit models can deal with a problem usually known as endogeneity. This issue is likely to arise in observational studies when confounders are unobserved. We are concerned with testing the hypothesis of exogeneity (or absence of endogeneity) when using regression spline recursive and sample selection bivariate probit models. Likelihood ratio and gradient tests are discussed in this c...

Generalized measurement invariance tests with application to factor analysis

The issue of measurement invariance commonly arises in factor-analytic contexts, with methods for assessment including likelihood ratio tests, Lagrange multiplier tests, and Wald tests. These tests all require advance definition of the number of groups, group membership, and offending model parameters. In this paper, we construct tests of measurement invariance based on stochastic processes of ...

Nonparametric Bootstrap for Quasi-Likelihood Ratio Tests∗

We introduce a nonparametric bootstrap approach for Quasi-Likelihood Ratio type tests of nonlinear restrictions. Our method applies to extremum estimators, such as quasimaximum likelihood and generalized method of moments estimators. Unlike existing parametric bootstrap procedures for Quasi-Likelihood Ratio type tests, our procedure constructs bootstrap samples in a fully nonparametric way. We ...

On the Behrens–fisher Problem: a Globally Convergent Algorithm and a Finite-sample Study of the Wald, Lr and Lm Tests

In this paper we provide a provably convergent algorithm for the multivariate Gaussian Maximum Likelihood version of the Behrens– Fisher Problem. Our work builds upon a formulation of the loglikelihood function proposed by Buot and Richards [5]. Instead of focusing on the first order optimality conditions, the algorithm aims directly for the maximization of the log-likelihood function itself to...