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We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates.(More)
We study the asymptotic distribution of three-step estimators of a …nite dimensional parameter vector where the second step consists of one or more nonparametric regressions on a regressor that is estimated in the …rst step. The …rst step estimator is either parametric or non-parametric. Using Newey’s (1994) path-derivative method we derive the contribution(More)
The purpose of this paper is to review recently developed bias-adjusted methods of estimation of nonlinear panel data models with fixed effects. Standard estimators such as maximum likelihood estimators are usually inconsistent if the number of individuals n goes to infinity while the number of time periods T is held fixed. For some models, like static(More)
A sizable volume of literature on the study of income and consumption dynamics has developed through the application of panel data surveys. Very few researchers, however, have provided solutions to the measurement error bias generated by surveyed income and consumption, although the presence of such bias has been widely acknowledged. This paper uses data(More)
Quantile regression (QR) fits a linear model for conditional quantiles, just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean square error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using(More)
The effect of government programs on the distribution of participants' earnings is important for program evaluation and welfare comparisons. This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on the quantiles of(More)
This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normally distributed(More)
Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to di erent values of the individual e ects. Fixed e ects, random e ects, and Bayesian approaches all fall in this category. We provide a characterization of the class of weights (or priors) that produce estimators that are rst-order unbiased.(More)