Professor Jeffrey M. Wooldridge

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.

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

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.

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models.

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.

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

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.

I provide an overview of inverse probability weighted (IPW) M-estimators for cross section and two-period panel data applications

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models.

I provide an overview of inverse probability weighted (IPW) M-estimators for cross section and two-period panel data applications.