Robust Inference in Models Identified via Heteroskedasticity

32 Pages Posted: 22 Dec 2018 Last revised: 14 Aug 2019

See all articles by Daniel J. Lewis

Daniel J. Lewis

Federal Reserve Banks - Federal Reserve Bank of New York

Date Written: December 1, 2018

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, weak identification, robust inference, pretesting, monetary policy, impulse response function

JEL Classification: C12, C32, C36, E43

Suggested Citation

Lewis, Daniel J., Robust Inference in Models Identified via Heteroskedasticity (December 1, 2018). FRB of New York Staff Report No. 876, Available at SSRN: https://ssrn.com/abstract=3305380 or http://dx.doi.org/10.2139/ssrn.3305380

Daniel J. Lewis (Contact Author)

Federal Reserve Banks - Federal Reserve Bank of New York ( email )

33 Liberty Street
New York, NY 10045
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
24
Abstract Views
248
PlumX Metrics