Robust inference in models identified via heteroskedasticity - Daniel J. Lewis (Federal Reserve Bank of New York, 2018)
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, propose two tests to detect weak identification, and develop nonconservative methods for robust inference on a 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: C12; C32; E43; 330; heteroskedasticity; weak identification; robust inference; pretesting; monetary policy; impulse response function