identification inference moment conditions robust singular variance subvector test test weak identification weak instruments C10 C12 …This paper introduces a new identification- and singularity-robust conditional quasi-likelihood ratio (SR-CQLR) test … and a new identification- and singularity-robust Anderson and Rubin (1949) (SR-AR) test for linear and nonlinear moment …

estimated by instrumental variables. We show that writing all the test statistics -- Student's t, Anderson-Rubin, the LM … interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures … for bootstrapping the three non-exact test statistics and also a new conditional bootstrap version of the LR test. These …

limited-information maximum likelihood. We show that all the test statistics, like the ones used for inference on the … related to the well-known test statistic of Anderson and Rubin. The distributions of the overidentification statistics are … impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are …

variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The … plus or minus one. This makes it impossible to perform reliable inference near the point at which the limit is ill …-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are …

getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In …

heteroskedasticity-robust t tests, and to the Anderson-Rubin test. We provide simulation evidence that it works far better than older …

We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust …

clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are … often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for … avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator …

on an empirical likelihood ratio. The computations necessary for obtaining a test statistic also provide estimates of the … be used to perform bootstrap tests that can turn out to provide much improved reliability of inference compared with the …

In this article we introduce a new class of test statistics designed to detect the occurrence of abnormal observations … assumption of a normal distribution for the underlying data. Our novel tests can be applied to test for jumps and are found to be …