This paper compares neural network models with the standard logit and probit models, the most widely used choice/classi cation models in current empirical research, and explores the application of neural network models in analyzing political choice/classi cation problems. Political relationships are usually nonlinear and of unknown functional forms, and political data are likely noisy. The logi...

Pregibit: A Family of Discrete Choice Models The pregibit discrete choice model is built on a distribution that allows symmetry or asymmetry and thick tails, thin tails or no tails. Thus the model is much richer than the traditional models that are typically used to study behavior that generates discrete choice outcomes. Pregibit nests logit, approximately nests probit, loglog, cloglog and goss...

We discuss regression models for ordered responses, such as ratings of bonds, schooling attainment, or measures of subjective well-being. Commonly used models in this context are the ordered logit and ordered probit regression models. They are based on an underlying latent model with single index function and constant thresholds. We argue that these approaches are overly restrictive and preclud...

Elimination by aspects (EBA) is a random utility model that is considered to represent the choice process used by consumers more faithfully than logit and probit models. One limitation of the model is that it does not have a known error theory. We show that EBA can be derived by assuming that aspects have random utilities with independent, extreme value distributions. Multinomial logit and rank...

In this paper we investigate the value of travel time savings. Estimation of this is in most cases based on samples using a specific mode. We use a mixed logit model to estimate the VTTS together with an auxiliary probit equation to capture the fact that the sample is non random. The results show that the probit equation in some cases gives extra information that can be used to improve the VTTS...

This chapter examines different models commonly used to model probabilistic choice, such as eg the choice of one type of transportation from among many choices available to the consumer. Section 1 discusses derivation and limitations of conditional logit models. Section 2 discusses probit models and Section 3 discusses the nested logit (generalized extreme value models), which address some of t...

Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-lognormal random effects model, a two-part logit-truncated normal random effects model, a two-pa...

February 19, 2015 Type Package Title Nonparametric spatial data analysis Version 2.0 Date 2013-5-20 Author Daniel McMillen Maintainer Daniel McMillen Description Locally weighted regression, semiparametric and conditionally parametric regression, fourier and cubic spline functions, GMM and linearized spatial logit and probit, k-density functions and counterfactuals, nonp...