Research methods

This paper considers the identification and estimation of hedonic models.

Recent theoretical work has shown the importance of measuring microeconomic uncertainty for models of both general and partial equilibrium under imperfect insurance.

This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function which depends on a discrete endogenous variable and is nonseparable in a latent variate.

This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function which depends on a discrete endogenous variable and is nonseparable in a latent variate.

This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters.

In this paper I summarize some recent developments in the literature on the econometrics of program evaluation.

We survey microeconometric research on investment and emplyment that has used panel data on individual firms or plants.

We consider the estimation of parametric models for stationary spatial orspatio-temporal data on a d-dimensional lattice, for d ≥ 2.

There is renewed interest in why people of lower socio-economic status (SES) have worse health outcomes.

Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem.

For vectors x and w, let r(x;w) be a function that can be nonparametrically estimated consistently and asymptotically normally.

This paper concerns the identification and estimation of a shape-invariant Engel curve system with endogenous total expenditure.

Parents with higher education levels have children with higher education levels.

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.

We provide easy to verify suffcient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some preliminary nonparametric estimators.

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

Conditions are derived under which there is local nonparametric identification of values of structural functions and of their derivatives in potentially nonlinear nonseparable models.

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

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