On the robustness of fixed effects and related estimators in correlated random coefficient panel data 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. In addition to including the usual fixed effects estimator, the results apply to estimators that eliminate individual-specific trends. Further, asymptotic variance matrices are straightforward to estimate. I apply the results, and propose alternative estimators, to estimation of average treatment in a general class of unobserved effects models.

Estimating average partial effects under conditional moment independence assumptions

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. To identify the populationaveraged effects, I use extensions of ignorability assumptions that are used for estimating linear models with additive heterogeneity and for estimating average treatment effects. New stimators are obtained for estimating the unconditional average partial effect as well as the average partial effect conditional on functions of observed covariates.

Inverse probability weighted estimation for general missing data problems

I study inverse probability weighted M-estimation under a general missing data scheme. The cases covered that do not previously appear in the literature include M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect for linear exponential family quasi-log-likelihood functions, and variable probability sampling with observed retainment frequencies. I extend an important result known to hold in special cases: estimating the selection probabilities is generally more efficient than if the known selection probabilities could be used in estimation. For the treatment effect case, the setup allows for a simple characterization of a “double robustness” result due to Scharfstein, Rotnitzky, and Robins (1999): given appropriate choices for the conditional mean function and quasi-log-likelihood function, only one of the conditional mean or selection probability needs to be correctly specified in order to consistently estimate the average treatment effect.

Recent Developments in the Econometrics of Program Evaluation

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades much research has been done on the econometric and statistical analysis of the effects of such programs or treatments. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization and other areas of empirical micro-economics. 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.program evaluation, causality, unconfoundedness, Rubin Causal Model, potential outcomes, instrumental variables

Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies for at least three leading dynamic, nonlinear models: probit, Tobit, and Poisson regression. I treat the general problem of estimating average partial effects, and show that simple estimators exist for important special cases.

Fractional response models with endogeneous explanatory variables and heterogeneity

In this talk, I will discuss ways of using Stata to fit fractional response models when explanatory variables are not exogenous. Two questions are of primary concern: First, how does one account for endogenous explanatory variables, both continuous and discrete, when the response variable is fractional and may take values at the corners? Second, how can we incorporate unobserved heterogeneity in panel-data fractional models when the panel might be unbalanced? I will draw on Papke and Wooldridge (2008, Journal of Econometrics 145: 121–133) and two unpublished papers of mine, "Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables" and "Correlated random effects models with unbalanced panels". One practically important conclusion is that by expanding the scope of existing Stata commands to allow fractional responses—in particular, the ivprobit, biprobit, hetprob, and (user-written) gllamm commands—flexible fractional response models can easily be fit.

A General Double Robustness Result for Estimating Average Treatment Effects

In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family