I expose the risk of false discoveries in the context of multiple treatment effects. A false discovery is a nonexistent effect that is falsely labeled as statistically significant by its individual t-value. Labeling nonexistent effects as statistically significant has wide-ranging academic and...

Often, a treatment and the outcome of interest are characterized by the moment they occur, and these moments are realizations of stochastic processes with dependent unobserved determinants. We develop a simple and intuitive method for inference on the treatment effect. The method can be...

I expose the risk of false discoveries in the context of multiple treatment effects. A false discovery is a nonexistent effect that is falsely labeled as statistically significant by its individual t-value. Labeling nonexistent effects as statistically significant has wide-ranging academic and...

Often, a treatment and the outcome of interest are characterized by the moment they occur, and these moments are realizations of stochastic processes with dependent unobserved determinants. We develop a simple and intuitive method for inference on the treatment effect. The method can be...

Often, a treatment and the outcome of interest are characterized by the moment they occur, and these moments are realizations of stochastic processes with dependent unobserved determinants. We develop a simple and intuitive method for inference on the treatment effect. The method can be...

This paper shows how to increase the power of Hausman's (1978) specification test as well as the difference test in a large class of models. The idea is to impose the restrictions of the null and the alternative hypotheses when estimating the covariance matrix. If the null hypothesis is true...

This paper introduces an alternative testing procedure to test the distribution of the error term in the Autoregressive Conditional Duration (ACD) class of models. In these models, the error term is normally interpreted as the standardized duration by which its probability distribution may have...

Inference using difference-in-differences with clustered data requires care. Previous research has shown that t tests based on a cluster-robust variance estimator (CRVE) severely over-reject when there are few treated clusters, that different variants of the wild cluster bootstrap can...