A growing literature on inference in difference-in-differences (DiD) designs with grouped errors has been pessimistic about obtaining hypothesis tests of the correct size, particularly with few groups. We provide Monte Carlo evidence for three points: (i) it is possible to obtain tests of the...

We extend to score, Wald and difference test statistics the scaled and adjusted corrections to goodness-of-fit test statistics developed in Satorra and Bentler (1988a,b). The theory is framed in the general context of multisample analysis of moment structures, under general conditions on the...

We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM) test statistics for a variety of hypotheses within this model. We first construct...

We propose a bounds testing procedure (BTP) with a battery of tests for the existence of a non-degenerate co-integrating relationship in levels, for long panels. It is a natural extension to panel data of the respective approach in time series as described by Pesaran, Shin and Smith (2001) and...

We calculate the asymptotic sizes of the subvector Anderson and Rubin (1949, AR) and Lagrange Multiplier (LM) tests in a linear instrumental variables model with two right hand side endogenous variables when the reduced form coefficient matrix is unrestricted. Under the assumption of conditional...

We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null and alternative hypotheses regarding conditional treatment effects, including (i) the null hypothesis of the conditional stochastic dominance between treatment...

In this paper we show that the testable implications derived in Huber and Mellace (2013) are the best possible to detect invalid instruments, in the presence of heterogeneous treatment effects and endogeneity. We also provide a formal proof of the fact that those testable implications are only...

Economic theory often predicts that treatment responses may depend on individuals' characteristics and location on the outcome distribution. Policymakers need to account for such treatment effect heterogeneity in order to efficiently allocate resources to subgroups that can successfully be...

This paper discusses the empirical content of the exogeneity (zero-covariance) assumption, the key assumption for identification in the linear IV model. Contrary to the general belief, we show that whenever the outcome is bounded, the exogeneity assumption imposes some testable restrictions on...

We propose a test for the key identification and estimation conditions in Regression Discontinuity (RD) designs. We characterize the set of sharp testable implications of the RD assumptions, for which the proposed test is uniformly valid under a class of distributions, is consistent against all...