In this paper, we use Monte Carlo methods to examine the small sample bias in the tobit, truncated regression and Weibull survival models as well as the binary probit and logit and ordered probit …

Mar 20, 2018 · With binary dependent variables, this can be done via the use of conditional logit/fixed effects logit models. With panel data we can control for stable characteristics (i.e. characteristics that …

INo study has compared the relative performance of such methods with small and large samples, and with low and high correlation between unobservables affecting initial conditions and dynamic …

In this article, we present the user-written commands probitfe and logitfe, which fit probit and logit panel-data models with individual and time unobserved effects.

Abstract: ects, like teacher effects or location effects. In the context of binary response variables, pre-vious studies have been limited to the linear probability model, citing perfect predic ion and incidental …

The model described by (1) and (2) is a dynamic ordered panel logit model, where an arbitrary function γ of the lagged depend variable Yi,t−1 is allowed enter additively into

In this section, we briefly discuss some other fixed effects panel data models with discrete outcomes, for which it is possible to use the approach outlined in Section 2.2 to derive moment conditions.

Heckman suggested using dynamic binary panels to discriminate between spurious state dependence and true state dependence.

Nov 16, 2022 · Panel-data models with random effects can be fit with Stata's me commands for multilevel modeling. And the metobit command can fit panel-data tobit models to censored outcomes.

Oct 16, 2024 · We describe how to systematically explore the existence of moment conditions that do not depend on the fixed effects, and we demonstrate how to construct them when they exist.