We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. We derive novel asymptotic properties for several of these models. To improve out-of-sample prediction accuracy and obtain parametric rates of convergence, we propose...

We propose an estimator for discrete choice models, such as the logit, with a nonparametric distribution of random coefficients. The estimator is linear regression subject to linear inequality constraints and is robust, simple to program and quick to compute compared to alternative estimators...

We propose a new strategy for a pervasive problem in the hedonics literature: recovering hedonic prices in the presence of time-varying correlated unobservables. Our approach relies on an assumption about home buyer rationality, under which prior sales prices can be used to control for...

We propose a new strategy for a pervasive problem in the hedonics literature--recovering hedonic prices in the presence of time-varying correlated unobservables. Our approach relies on an assumption about homebuyer rationality, under which prior sales prices can be used to control for...

We study a variant of a random utility model that takes a probability distribution over preference relations as its primitive. We do not model products using a space of observed characteristics. The distribution of preferences is only partially identified using cross-sectional data on varying...

We propose a new strategy for a pervasive problem in the hedonics literature--recovering hedonic prices in the presence of time-varying correlated unobservables. Our approach relies on an assumption about homebuyer rationality, under which prior sales prices can be used to control for...