Goodness-of-Fit Test for Model Specification

Qian (Michelle) Zhou (Simon Fraser University)
Wednesday, December 17, 2014 - 10:00am
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

In this talk, I will introduce information ratio (IR) statistic to test for model misspecification in various models. The IR test was first proposed in my Ph.D. thesis to test for model misspecification of variance/covariance structure in quasi-likelihood inference for cross-sectional data or longitudinal data. The statistic is constructed via a contrast between two forms of information matrix: the negative sensitivity matrix and variability matrix. Under the null hypothesis that the variance/covariance structure is correctly specified, we show that the proposed test statistic is asymptotically distributed as a normal random variable with mean equal to the dimension of the parameter space. Later, this test was further developed to test for model misspecification on parametric structures in stochastic diffuse models. Afterwards, we extend our method to test for model misspecification in parametric copula functions of semi-parametric copula models. We propose a new test constructed via the contrast between "in-sample" and "out-of-sample" pseudo-likelihoods.