In this paper, we suggest a Stata routine for multinomial logit mod- els with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold. First, we describe the technical implementation of the estimation routine and discuss its...

We describe specification and estimation of a multinomial treatment effects negative binomial regression model. A latent factor structure is used to accommodate selection into treatment, and a simulated likelihood method is used for estimation. We describe its implementation via the mtreatnb...

This paper investigates using maximum simulated likelihood (MSL) estimation for random-effects dynamic probit models with autocorrelated errors. It presents and illustrates a new Stata command, redpace, for this estimator. The paper also compares using pseudorandom numbers and Halton sequences...

We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdraws- for deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()- for calculating the...

Received stochastic frontier analyses with panel data have relied on traditional fixed and random effects models. We propose extensions that circumvent two shortcomings of these approaches. The conventional panel data estimators assume that technical or cost inefficiency is time invariant....

This paper estimates land use responses of households to relative output prices and wages, using panel data from the Philippines. We present multi-output profit maximizing model to elicit the role of relative prices on land allocation between crops and fallowing. We estimate systems of random...

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component...

Fixed-effects models have become increasingly popular in social-science research. The possibility to control for unobserved heterogeneity makes these models a prime tool for causal analysis. Fixed-effects models have been derived and implemented for many statistical software packages for...

We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal data. The main assumption behind these models is that the response variables are conditionally independent given a latent process which follows a first-order Markov chain. We first illustrate the...