Quantifying the Federal Reserve’s Objectives Using a Structural Vector Autoregressive Model

33 Pages Posted: 13 Feb 2020 Last revised: 15 Mar 2021

See all articles by Shengliang Ou

Shengliang Ou

Shanghai University of Finance and Economics

Donghai Zhang

Institute for Macroeconomics and Econometrics - University of Bonn

Date Written: March 5, 2021

Abstract

The Federal Reserve’s (Fed’s) objective, namely, its dovish stance, is often blamed for the so-called Great Inflation. A popular proxy for the former is constructed using the inflation coefficients in estimated Taylor rules. However, for a welfare-optimizing central bank, the estimated Taylor coefficients are not sufficient for inferring its underlying preference. We quantify Fed’s objective—the targeting rule—relying on a conditional estimator (Galí and Gambetti 2018) that is free of the classical simultaneity problem. We discover that Fed’s targeting rule remained stable during the pre- and post-Volcker periods—the opposite of what is implied through a Taylor rule estimation.

Keywords: Targeting Rule, Central Bank Preference, Structural Vector Autoregressive (SVAR) Model, Sign Restrictions, Great Inflation, Great Moderation

JEL Classification: E31, E32, E52, E58, E65

Suggested Citation

Ou, Shengliang and Zhang, Donghai, Quantifying the Federal Reserve’s Objectives Using a Structural Vector Autoregressive Model (March 5, 2021). Available at SSRN: https://ssrn.com/abstract=3522455 or http://dx.doi.org/10.2139/ssrn.3522455

Shengliang Ou

Shanghai University of Finance and Economics ( email )

777 Guoding Road
Shanghai, AK Shanghai 200433
China

Donghai Zhang (Contact Author)

Institute for Macroeconomics and Econometrics - University of Bonn ( email )

Bonn
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
26
Abstract Views
323
PlumX Metrics