We propose a methodology for constructing confidence regions with partially identified models of general form. The region is obtained by inverting a test of internal consistency of the econometric structure. We develop a dilation bootstrap methodology to deal with sampling uncertainty without reference to the hypothesized economic structure, and apply a duality principle to reduce the dimension...

Random Coefficient Panel Data Models This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provid...

A Martingale Representation for Matching Estimators Matching estimators are widely used in statistical data analysis. However, the distribution of matching estimators has been derived only for particular cases (Abadie and Imbens, 2006). This article establishes a martingale representation for matching estimators. This representation allows the use of martingale limit theorems to derive the asym...

We consider a generalized regression model with a partially linear index. The index contains an additive nonparametric component in addition to the standard linear component, and the model’s dependent variable is transformed by a unknown monotone function. We propose weighted rank estimation procedures for estimating (i) the coe¢ cients for the linear component, (ii) the nonparametric component...

Implied volatilities of interest rate derivatives present some distinctive features, like the inverse relation with the underlying rates and the humped or decreasing shape of their term structure. The objective of this paper is to analyze and explain such features in a Gaussian framework. We will use an approximate relation which separates in a simple and natural way the effects on the implied ...

This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of the coefficients in a truncated regression model. The distribution of the errors is unknown and permits general forms of unknown heteroskedasticity. Also provided is an instrumental variables based two stage least squares estimator for this model, which can be used when some regressors are endogen...

Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate. We give corrected standard errors, an extension of Bekker (1994) to nonnormal disturbances, that adjust for many instruments. We find that this adujstment is useful in empirical work, simulations, and in the asymptotic theory. Use of the corrected standard errors ...

This paper proposes minimum distance estimation procedures for the slope coefficients and location parameter in randomly censored regression models that are used in duration and competing risk models. The proposed procedure generalizes existing work in terms of weakening the restrictions imposed on the distribution of the error term and the censoring variable. Examples of such generalizations i...

This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discrete observations. A numerical solution to the stochastic differential equation is obtained by higher order Wagner-Platen approximation and it is used to derive the first two conditional moments. Monte Carlo simulation shows that the proposed method has good finite sample property for both normal a...

I develop a new empirical model for discrete games and apply it to study the release date timing game played by distributors of movies. The results suggest that release dates of movies are too clustered around big holiday weekends and that box office revenues would increase if distributors shifted some holiday releases by one or two weeks. The proposed game structure could be applied more broad...