Weak identification robust tests in an instrumental quantile model

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We develop a testing procedure that is robust to identification quality in an instrumental quantile model. In order to reduce the computational burden, a multi-step approach is taken, and a two-step Anderson-Rubin (AR) statistic is considered. We then propose an orthogonal decomposition of the AR statistic, where the null distribution of each component does not depend on the assumption of a full rank of the Jacobian. Power experiments are conducted, and inferences on returns to schooling using the Angrist and Krueger data are considered as an empirical example.

Original languageEnglish (US)
Pages (from-to)118-138
Number of pages21
JournalJournal of Econometrics
Volume144
Issue number1
DOIs
StatePublished - May 1 2008

Fingerprint

Robust Tests
Quantile
Statistic
Orthogonal Decomposition
Null Distribution
Testing
Model
Experiment
Weak identification
Statistics
Decomposition
Inference
Returns to schooling
Burden

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Finance
  • Statistics and Probability

Cite this

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Weak identification robust tests in an instrumental quantile model. / Jun, Sung Jae.

In: Journal of Econometrics, Vol. 144, No. 1, 01.05.2008, p. 118-138.

Research output: Contribution to journalArticle

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