Weak Instrument Bias in Impulse Response Estimators

Daniel Lewis and Karel Mertens

No 2601, Working Papers from Federal Reserve Bank of Dallas

Abstract: We approximate the finite-sample distribution of impulse response function (IRF) estimators that are just-identified with a weak instrument using the conventional local-to-zero asymptotic framework. Since the distribution lacks a mean, we assess bias using the mode and conclude that researchers prioritizing robustness against weak instrument bias should favor vector autoregressions (VARs) over local projections (LPs). Existing testing procedures are ill-suited for assessing weak instrument bias in IRF estimates, and we propose a novel simple test based on the usual first-stage F-statistic. We investigate instrument strength in several applications from the literature, and discuss to what extent structural parameters must be restricted ex-ante to reject meaningful bias due to weak identification.

Keywords: local projections; vector autoregressions; instrumental variables; weak instruments; impulse responses; dynamic causal effects (search for similar items in EconPapers)
JEL-codes: C32 C36 (search for similar items in EconPapers)
Pages: 63
Date: 2026-01-12
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Working Paper: Weak instrument bias in impulse response estimators (2026)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:feddwp:102343

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DOI: 10.24149/wp2601

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