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Aggressive Monetary Policy and Inflation Persistence: An Adaptive Learning Approach


Jim Granato


National Science Foundation

M. C. Sunny Wong


University of San Francisco



Abstract:     
We investigate the effectiveness of an aggressive anti-inflation monetary policy on the ability of agents to achieve rational expectations equilibrium (REE) forecasts of inflation. An aggressive anti-inflation policy includes a willingness to respond more forcefully to deviations from an inflation target. Using an adaptive learning framework, we develop a model that uses a real contracting rigidity in conjunction with an interest rate rule and an IS curve. The model equilibrium indicates that only an aggressive anti-inflation policy enables agents to learn the REE inflation forecast. The model also shows that inflation persistence (volatility) has a negative relation with policy aggressiveness. Empirical tests confirm this negative relation but these tests also indicate there is a lag between aggressive policy shifts and effective changes in inflation persistence (volatility).

Number of Pages in PDF File: 51

Keywords: Taylor Rule, Taylor Principle, real wage contract, interest rate rule, aggressive monetary policy, adaptive learning, stability, determinacy, inflation persistence

JEL Classification: E4, E5, E31, E52, D84

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Date posted: March 9, 2003  

Suggested Citation

Granato, Jim and Wong, M. C. Sunny, Aggressive Monetary Policy and Inflation Persistence: An Adaptive Learning Approach. Available at SSRN: http://ssrn.com/abstract=349581 or http://dx.doi.org/10.2139/ssrn.349581

Contact Information

Jim Granato (Contact Author)
National Science Foundation ( email )
Political Science Program
4201 Wilson Boulevard, Suite 980
Arlington, VA 22230
United States
703-292-7284 (Phone)
703-292-9195 (Fax)
M. C. Sunny Wong
University of San Francisco ( email )
2130 Fulton Street
Department of Economics
San Francisco, CA 94117-1080
United States
(415) 422-6194 (Phone)
HOME PAGE: http://www.usfca.edu/fac-staff/mwong11/
Feedback to SSRN (Beta)


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