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1 Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches(More)
Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement. There is therefore good reason to consider weaker but more credible assumptions. To this end, we introduce monotone instrumental variable (MIV)(More)
and Harvard/MIT are appreciated. Abstract Fixed effects estimator of panel models can be severely biased because of the well-known incidental parameter problems. It is shown that such bias can be reduced as T grows with n by using an analytical bias correction or by using a panel jacknife. We describe both of these approaches. We consider asymptotics where(More)
The fixed effects estimator of panel models can be severely biased because of well-known incidental parameter problems. It is shown that this bias can be reduced as T grows with n. We consider asymptotics where n and T grow at the same rate as an approximation that allows us to compare bias properties. Under these asymptotics, bias corrected estimators we(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)
Becker's theory of human capital predicts that minimum wages should reduce training investments for affected workers because they prevent these workers from taking wage cuts necessary to finance training. In contrast, in noncompetitive labor markets, minimum wages tend to increase training of affected workers because they induce firms to train their(More)