SGER- Estimation of Binary Choice and Nonparametric Censored Regression Models

University Of Rochester, Rochester NY

Investigators

Abstract

This exploratory research is comprised of two distinct parts. The first and main part considers estimation of a binary choice model. This is a statistical model where the dependent variable (the one researchers are trying to explain or predict using observed explanatory variables) can take only two values. For this model, there are three values applied researchers are interested in estimating. One is the set of the parameters of the prediction function, which can be used to predict outcomes. The other values of interest are choice probabilities and marginal effects. The former provides researchers with the probability of observing a value of the dependent variable as a function of observed explanatory variables, and the latter determines the effect of a change in the value of the explanatory variable on this probability. Existing estimation either cannot simultaneously estimate all values of interest, or they require very strict assumptions on the relationship between the dependent and explanatory variables. In contrast, the procedures developed in this research project do not impose strict assumptions, yet enable joint estimation of the three values of interest. The second part of this research involves the estimation of a censored regression model, which is a statistical model where the dependent variable is never observed to take a value exceeding a fixed constant, referred to here as the censoring point. Many data sets encountered in applied work exhibit this model's features. This research project develops a new procedure which enables estimation of the prediction function in the region beyond the censoring point. This cannot be done using existing methods without stronger assumptions.