We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a k-dimensional random vector X if and only if the slope from an LAD regression of Y on X is zero. Using this simple definition, we...

We introduce a notion of median uncorrelation that is a natural extension of mean(linear) uncorrelation. A scalar random variable Y is median uncorrelated with a kdimensionalrandom vector X if and only if the slope from an LAD regression of Yon X is zero. Using this simple definition, we...

This paper examines inference on regressions when interval data are available on one variable, the other variables being measured precisely. Let a population be characterized by a distribution "P"("y", "x", "v", "v"-sub-0, "v"-sub-1), where "y" is an element of "R"-super-1, "x" is an element of...

This paper analyzes the linear regression model y = x&bgr;+ε with a conditional median assumption med (ε| z) = 0, where z is a vector of exogenous instrument random variables. We study inference on the parameter &bgr; when y is censored and x is endogenous. We treat the censored model as a model with...

We provide a practical method to estimate the payoff functions of players in complete information, static, discrete games. With respect to the empirical literature on entry games originated by Bresnahan and Reiss (1990) and Berry (1992), the main novelty of our framework is to allow for general...

We provide methods for inference on a finite dimensional parameter of interest, theta in Re^{d_theta}, in a semiparametric probability model when an infinite dimensional nuisance parameter, g, is present. We depart from the semiparametric literature in that we do not require that the pair...