The benefits of panel data are well-documented but missing data problems are often more severe. In particular, units that respond in the first wave may drop out of the panel after one or more periods of participation. This paper focuses on identification and Maximum Likelihood estimation of...

We study the effects of counterfactual teacher-to-classroom assignments on average student achievement in elementary and middle schools in the US. We use the Measures of Effective Teaching (MET) experiment to semiparametrically identify the average reallocation effects (AREs) of such...

We study the effects of counterfactual teacher-to-classroom assignments on average student achievement in elementary and middle schools in the US. We use the Measures of Effective Teaching (MET) experiment to semiparametrically identify the average reallocation effects (AREs) of such...

This paper considers identification of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Because treated and control units drop out at different rates, randomization only...

We propose a two-stage instrumental variable estimator that is consistent if there is selective compliance in the treatment group of a randomized experiment and the outcome variable is a censored duration. The estimator assumes full compliance in the control group. We use the estimator to...

We analyze the impact of unemployment benefits and minimum wagesusing an equilibrium search model which allows for dispersion ofbenefits and productivity levels, job-to-job transitions, andstructural and frictional unemployment. The estimation method usesreadily available aggregate data on...

This paper presents econometric methods for measuring the average output effect of reallocating an indivisible input across production units. A distinctive feature of reallocations is that, by definition, they involve no augmentation of resources and, as such, leave the marginal distribution of...

In a model with endogenous regressors, heteroskedastic and autocorrelated (HAC) errors and weak instruments, tests that depend on the data only through the Anderson-Rubin (AR) and Lagrange Multiplier (LM) statistics ignore important information on the regression coefficients. This is in contrast...