Inference in instrumental variable models with heteroskedasticity and many instruments

Federico Crudu, Giovanni Mellace*, Zsolt Sándor

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

110 Downloads (Pure)


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 heteroskedasticity. First, we provide an Anderson-Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson-Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the parameters not under test exists. We show that under the null, the proposed statistics have Gaussian limiting distributions and derive alternative chi-square approximations. An extensive simulation study shows the competitive finite sample properties in terms of size and power of our procedures. Finally, we provide an empirical application using college proximity instruments to estimate the returns to education.

Original languageEnglish
JournalEconometric Theory
Issue number2
Pages (from-to)281-310
Publication statusPublished - Apr 2021


Dive into the research topics of 'Inference in instrumental variable models with heteroskedasticity and many instruments'. Together they form a unique fingerprint.

Cite this