This paper considers random coefficients binary choice models. The main goal is to estimate the density of the random coefficients nonparametrically. This is an ill-posed inverse problem characterized by an integral transform. A new density estimator for the random coefficients is developed,...

This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum...

Nonparametric additive modeling is a fundamental tool for statistical data analysis which allows flexible functional forms for conditional mean or quantile functions but avoids the curse of dimensionality for fully nonparametric methods induced by high-dimensional covariates. This paper proposes...

The most popular survey method used in contingent valuations asks "open-ended" dichotomous choice questions. This method generates grouped or interval-censored data on respondents' willingness to pay. This paper specifies the willingness to pay distribution using the proportional hazard...

The most familiar fixed effects (FE) and random effects (RE) panel data treatments for count data were proposed by Hausman, Hall and Griliches (HHG) (1984). The Poisson FE model is particularly simple and is one of a small few known models in which the incidental parameters problem is, in fact,...

We consider a bivariate Poisson model that is based on the lognormal heterogeneity model. Two recent applications have used this model. We suggest that the correlation estimated in their model frameworks is an ambiguous measure of the correlation of the variables of interest, and may...

This study presents several extensions of the most familiar models for count data, the Poisson and negative binomial models. We develop an encompassing model for two well known variants of the negative binomial model (the NB1 and NB2 forms). We then propose some alternative approaches to the...

This paper proposes a novel distribution-free maximum likelihood estimator for binary and ordered response models and evaluates its finite sample performance in a Monte Carlo simulation. In the simulation, where ordered response models are considered, the main focus is on estimating the effect...

We consider fixed‐effects binary choice models with a fixed number of periods T and regressors without a large support. If the time‐varying unobserved terms are i.i.d. with known distribution F, Chamberlain (2010) shows that the common slope parameter is point identified if and only if F is...

In this paper we study the identification and estimation of a class of binary regressions where conditional medians of additive disturbances are bounded between known or exogenously identified functions of regressors. This class includes several important microeconometric models, such as...