This paper presents a new approach to estimation and inference in panel data models with unobserved common factors possibly correlated with exogenously given individual-specific regressors and/or the observed common effects. The basic idea behind the proposed estimation procedure is to filter...

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set. We show that...

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set. Our approach is...

Microeconomic data often have within-cluster dependence. This dependence affects standard error estimation and inference in regression models, including the instrumental variables model. Standard corrections assume that the number of clusters is large, but when this is not the case, Wald and...

We study inference for the local innovations of It\^o semimartingales. Specifically, we construct a resampling procedure for the empirical CDF of high-frequency innovations that have been standardized using a nonparametric estimate of its stochastic scale (volatility) and truncated to rid the...

We discuss statistical inference problems associated with identification and testability in econometrics. We consider inference in non-parametric models and weakly identified structural models (weak instruments). We point out that many ill-defined statistical problems, such as non-testable...

Reject inference is a method for inferring how a rejected credit applicant would have behaved had credit been granted. Credit-quality data on rejected applicants are usually missing not at random (MNAR). In order to infer credit-quality data MNAR, we propose a flexible method to generate the...

Statistical inference can be described as the process of drawing conclusions about a population or process based on sample data. This chapter outlines the logic of “classical” or “frequentist” methods for such inference. Three commonly used concepts for assessing statistical error are...

This paper explores the uniformity of inference for parameters of interest in nonlinear models with endogeneity. The notion of uniformity is fundamental in these models because due to potential endogeneity, the behavior of standard estimators of these parameters is shown to vary with where they...

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite...