Methods

Weak nonparametric restrictions are developed, sufficient to identify the values of derivatives of structural functions in which latent random variables are nonseparable.

This paper presents a method for estimating a class of panel data duration models, under which an unknown transformation of the duration variable is linearly related to the observed explanatory variables and the unobserved heterogeneity (or frailty) with completely known error distributions.

The principal purpose of this paper is to describe the performance of generalized

Missing values are endemic in the data sets available to econometricians.

In an effort to improve the small sample properties of generalized method of moments

In the last decade a growing body of research has studied inference on partially identified parameters (e.g., Manski, 1990, 2003). Here the authors introduce conceptually different interval estimates that asymptotically cover each element in the identification region with fixed probability (but not necessarily every element simultaneously).

Measurement errors in survey data on hourly pay may lead to serious upward bias in low pay

This article uses factor models to identify and estimate the distributions of counterfactuals.

An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments.

This paper considers data quality issues for the analysis of consumption inequality exploiting two complementary datasets from the Consumer Expenditure Survey for the United States.

We develop inference tools in a semiparametric partially linear regression model with missing response data.

This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors.

We suggest two nonparametric approaches, based on kernel methods and orthogonal series, respectively, to estimating regression functions in the presence of instrumental variables.

In this paper we show that the sharp RDD straightforwardly generalizes to the instances in which the eligibility for the program is established with respect to an observable pre-program measure with eligible individuals self-selecting into the treatment group according to an unknown process.

We develop a method for estimation of price reactions using unit value data which exploits the implicit links between quantity and unit value choices.

This paper studies some empirical implications of models with limited risk sharing due to the imperfect enforceability of contracts.

In this paper we build a general equilibrium OLG model of labor earnings and

This paper is concerned with estimating a conditional quantile function that is assumed to be partially linear.

Stata module to perform full Mahalanobis matching and a variety of propensity score matching to adjust for pre-treatment observable differences between two groups.