Methods

We examine the contribution of human capital to economy-wide technological improvements through the two channels of innovation and imitation.

This lecture explores conditions under which there is identification of the impact on an outcome of exogenous variation in a variable which is endogenous when data are gathered.

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

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

This paper develops and implements semiparametric methods for estimating binary response (binary choice) models withcontinuous endogenous regressors.

Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes.

Friedman’s book on the consumption function is one of the great works of Economics demonstrating how the interplay between theoretical ideas and data analysis can lead to major policy implications.

I show that a class of fixed effects estimators is reasonably robust for estimating the population-averaged slope coefficients in panel data models with individual-specific slopes, where the slopes are allowed to be correlated with the covariates.

This paper extends the nonparametric methods developed by Samuelson (1948), Houthakker (1950), Afriat (1973), Diewert (1973) and Varian (1982, 1983) to latently separable models.

Stata module to perform 'fully interacted linear matching', that is a fully interacted linear regression model in which the treatment dummy is interacted with each one of the other regressors.

Estimation of heteroskedasticity and autocorrelation consistent covariance matrices (HACs) is a well established problem in time series.

This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model.

I study inverse probability weighted M-estimation under a general missing data scheme.

This paper considers a panel duration model that has a proportional hazards specification with fixed effects.

This paper considers a panel duration model that has a proportional hazards specification with fixed effects.

I show how to identify and estimate the average partial effect of explanatory variables in a model where unobserved heterogeneity interacts with the explanatory variables and may be unconditionally correlated with the explanatory variables.

Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated.

This paper extends the nonparametric methods developed by Samuelson (1948), Houthakker (1950), Afriat (1973), Diewert (1973) and Varian (1982, 1983) to latently separable models.