Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum...

Marketing outlets choice is one of the most important farm household decisions to sell their produce and has a great impact on household income. Though the study area has great potential of honey production, the farmers in the study area faced the marketing problem in choice of appropriate honey...

We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK (Geweke-Hajivassiliou-Keene) algorithm to perform maximum simulated likelihood estimation. However, using the GHK for large sample sizes has been...

Based on data from broadly representative surveys among more than 1,400 citizens in Germany and Sweden, this paper empirically examines the support of different groups of climate-related (passenger) transport policy measures targeting vehicle use, public transport, air travel, and bicycle use....

This paper empirically examines the determinants of the demand for alternative energy sources and propulsion technologies in vehicles. The data stem from a stated preference discrete choice experiment with 598 potential car buyers. In order to simulate a realistic automobile purchase situation,...

In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models. It is shown in particular that the standard autoregressive specification used in the literature can be interpreted as a latent common factor model. However, this specification...

In this paper, we discuss the derivation and application of a bivariate ordered probit model with mixed effects. Our approach allows one to estimate the distribution of the effect (gamma) of an endogenous ordered variable on an ordered explanatory variable. By allowing gamma to vary over the...

Modeling and estimating autocorrelated discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an...