Robust inference in models identified via heteroskedasticity

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.

Keywords: heteroskedasticity; pretesting; weak identification; monetary policy; robust inference; impulse response function;

JEL Classification: C32; E43; C12; C36;

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Bibliographic Information

Provider: Federal Reserve Bank of New York

Part of Series: Staff Reports

Publication Date: 2019-08-01

Number: 876

Pages: 32 pages