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Federal Reserve Bank of New York
Staff Reports
Robust inference in models identified via heteroskedasticity
Daniel J. Lewis
Abstract

Identification via heteroskedasticity exploits differences in variances across regimes to identify parameters in simultaneous equations. I study weak identification in such models, which arises when variances change very little or the variances of multiple shocks change close to proportionally. I show that this causes standard inference to become unreliable, outline two tests to detect weak identification, and establish conditions for the validity of nonconservative methods for robust inference on an empirically relevant subset of the parameter vector. I apply these tools to monetary policy shocks, identified using heteroskedasticity in high frequency data. I detect weak identification in daily data, causing standard inference methods to be invalid. However, using intraday data instead allows the shocks to be strongly identified.


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Daniel J. Lewis, Robust inference in models identified via heteroskedasticity, Federal Reserve Bank of New York, Staff Reports 876, 01 Dec 2018, revised 01 Aug 2019.
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Keywords: heteroskedasticity; weak identification; robust inference; pretesting; monetary policy; impulse response function
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