The regression discontinuity (RD) data design is a quasi-experimental design with the defining characteristic that the probability of receiving treatment changes discontinuously as a function of one or more individual characteristics. This data design occasionally arises in economic and other applications but is only infrequently exploited in evaluating the effects of a treatment. We consider the problem of identification and estimation of treatment effects under a RD data design. We offer an interpretation of the IV or so-called Wald estimator as a regression discontinuity estimator. We propose nonparametric estimators of treatment effects and present their asymptotic distribution theory. Then we apply the estimation method to evaluate the effect of EEOC-coverage on minority employme...
We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast forecasts. Finally by recent advances in financial risk management, we provide a detailed application to multivariate high-frequency exchange rate density forecasts.
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