When instruments are weakly correlated with endogenous regressors, conventional
methods for instrumental variables (IV) estimation and inference become unreliable. A large …

The instrument-free approach adopts flexible bounds on the correlation between regressors
and disturbances, instead of exploiting instruments presupposing their asymptotic …

Many empirical applications of regression discontinuity (RD) models use a running variable
that is rounded and hence discrete, eg age in years, or birth weight in ounces. This paper …

To address the well-established large-sample invalidity of the+/-1.96 critical values for the t-
ratio in the single variable just-identified IV model, applied research typically qualifies the …

This paper introduces a new identification‐and singularity‐robust conditional quasi‐
likelihood ratio (SR‐CQLR) test and a new identification‐and singularity‐robust Anderson …

This paper provides a set of results that can be used to establish the asymptotic size and/or
similarity in a uniform sense of confidence sets and tests. The results are generic in that they …

This paper proposes novel inference procedures for instrumental variable models in the
presence of many, potentially weak instruments that are robust to the presence of …

We study subvector inference in the linear instrumental variables model assuming
homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin …

Instrumental variable (IV) methods for regression are well established. More recently,
methods have been developed for statistical inference when the instruments are weakly …

Data clustering reduces the effective sample size down from the number of observations
towards the number of clusters. For instrumental variable models this implies more restrictive …