Popular perception holds that employment stability has decreased in recent decades. However, no conclusive evidence exists on secular declines in the length of jobs held. Furthermore, most studies conclude that the proportion of long term jobs has remained remarkably stable over the last few...

Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying,...

Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying,...

In this note we propose a general testing procedure for parametric models based on Bartlett Identities. A well-know example is the Information Matrix test, which is based on the Bartlett Identity of order 1. The Identities are shown to induce a sequence of testable restrictions on the data...

In this note we propose a general testing procedure for parametric models based on Bartlett Identities. A well-known example is the Information Matrix test, which is based on the Bartlett Identity of order 1. The Identities are shown to induce a sequenceof testable restrictions on the data...

In this paper, we run a Monte Carlo analysis of the finite-sample performance of an Information Matrix Test put forward by Smith (1985) for bivariate censored models. We use the bivariate probit model and Heckman selection model as examples.;Approximating the finite-sample distribution of this...

This paper addresses the issue of designing finite-sample corrections to information matrix tests. We review a Cornish-Fisher correction that has been proposed elsewhere and propose an alternative, Bartlett-type correction. Simulation results for skewness, excess kurtosis, normality and...