We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters.
The multinomial logit model with random coefficients is widely used in applied research. This paper is concerned with estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters.
We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.
This paper proposes a simple nonparametric test of the hypothesis of no measurement error in explanatory variables and of the hypothesis that measurement error, if there is any, does not distort a given object of interest.
The idea of summarizing the information contained in a large number of variables by a small number of “factors” or “principal components” has been broadly adopted in economics and statistics.
We propose a demand model where consumers simultaneously choose a few different goods from a large menu of available goods, and choose how much to consume of each good.
We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable.
Economists are often interested in estimating averages with respect to distributions of unobservables. Examples are moments of individual fixed-effects, average effects in discrete choice models, or counterfactual simulations in structural models.
