Weak instrument bias in impulse response estimators - Daniel J. Lewis; Karel Mertens (Centre for Microdata Methods and Practice (cemmap), The Institute for Fiscal Studies (IFS))
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 firststage 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: C32; C36; 330; Local Projections; Vector Autoregressions; Instrumental Variables; Weak Instruments; Impulse Responses; Dynamic Causal Effects