Optimal Monetary Policy in an Optimizing Stochastic Dynamic Model with Sticky Prices

MARKET FRICTIONS AND MACROECONOMIC DYNAMICS, Jean-Olivier Hairault, Hubert Kempf eds., Kluwer Academic Publishers, Pp. 131-160, 2002

Posted: 15 Jul 2002

See all articles by Michael Gail

Michael Gail

University of Siegen - School of Economic Disciplines

Multiple version iconThere are 2 versions of this paper

Abstract

Recently macroeconomic researchers have begun studying models of optimal monetary policy within the Real Business Cycle (RBC) framework. A standard RBC model is augmented by New Keynesian elements like sticky prices and monopolistically competitive firms. The monetary authority acts as a social planner maximizing the utility of a representative agent while at the same time taking care of the optimal price setting behavior of the firms via an implementation constraint. King/Wolman (1999) analyze the outcome of such a model with respect to the appropriate monetary policy of the central bank. They conclude that the central bank achieves a complete stabilization of the price level. Inflation is not only constant at the steady state but also through time. It is shown that this very special result does not hold under alternative preference specifications that allow for a richer set of substitution effects between consumption and labor.

Keywords: Monetary Policy Rules, New Neoclassical Synthesis, Sticky Prices, Real Business Cycle

JEL Classification: E52, E32

Suggested Citation

Gail, Michael, Optimal Monetary Policy in an Optimizing Stochastic Dynamic Model with Sticky Prices. MARKET FRICTIONS AND MACROECONOMIC DYNAMICS, Jean-Olivier Hairault, Hubert Kempf eds., Kluwer Academic Publishers, Pp. 131-160, 2002, Available at SSRN: https://ssrn.com/abstract=317259

Michael Gail (Contact Author)

University of Siegen - School of Economic Disciplines ( email )

Unteres Schloss 3
Siegen, 57072
Germany
+49 271 740 3217 (Phone)
+49 271 740 13217 (Fax)

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

Paper statistics

Abstract Views
566
PlumX Metrics