Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors...

This paper makes several contributions to the literature on the important yet difficult problem of estimating functions nonparametrically using instrumental variables. First, we derive the minimax optimal sup-norm convergence rates for nonparametric instrumental variables (NPIV) estimation of...

Across the social sciences, lagged explanatory variables are a common strategy to confront challenges to causal identification using observational data. We show that “lag identification” — the use of lagged explanatory variables to solve endogeneity problems — is an illusion: lagging...

In FTC v. Actavis, Inc., the Supreme Court considered "reverse payment" settlements of patent infringement litigation. In such a settlement, a patentee pays the alleged infringer to settle, and the alleged infringer agrees not to enter the market for a period of time. The Court held that a...

We propose a procedure for testing simple hypotheses on a subset of the structural parameters in linear instrumental variables models. Our test is valid uniformly over a large class of distributions allowing for identification failure and heteroskedasticity. The large-sample distribution of our...

We show that in misspecified models with useless factors (for example, factors that are independent of the returns on the test assets), the standard inference procedures tend to erroneously conclude, with high probability, that these irrelevant factors are priced and the restrictions of the...

This paper studies some seemingly anomalous results that arise in possibly misspecified and unidentified linear asset-pricing models estimated by maximum likelihood and one-step generalized method of moments (GMM). Strikingly, when useless factors (that is, factors that are independent of the...