Download this Paper Open PDF in Browser

Monetary Policy Rules and Business Cycle in China: Bayesian DSGE Model Simulation

40 Pages Posted: 11 Apr 2011 Last revised: 24 Nov 2011

Lixin Sun

Center for Economic Research, Shandong University

Somnath Sen

University of Birmingham - Department of Economics

Date Written: April 9, 2011

Abstract

In this paper, using a benchmark Bayesian Dynamic Stochastic General Equilibrium (Bayesian DSGE) model (Smets-Wouters Model) with Taylor’s rule and a modified Smets-Wouters model with a money growth rule, we have simulated China’s monetary policy transmission process and the roles of monetary variables and non monetary variables in China’s business cycle by incorporating many so-called New Keynesian Macroeconomic (NKM) approaches such as nominal stickiness and market imperfections in the model. The estimated values of the parameters in the model by Bayesian approach based on China’s quarterly time series data feature the unique characters of China’s economy compared with that in the US and the Euro area. The simulation results in terms of the Taylor’s rule and money growth rule (MacCullum Rule) highlight the monetary transmission mechanisms of China’s monetary policy and the diverse contributions of monetary shocks and non-monetary shocks to China’s business cycle.

Keywords: DSGE Model, Monetary Policy, China’s Business Cycle, Bayesian Approach, Taylor’s Rule, Money Growth Rule

JEL Classification: E3, E5, C5

Suggested Citation

Sun, Lixin and Sen, Somnath, Monetary Policy Rules and Business Cycle in China: Bayesian DSGE Model Simulation (April 9, 2011). Available at SSRN: https://ssrn.com/abstract=1806347 or http://dx.doi.org/10.2139/ssrn.1806347

Lixin Sun (Contact Author)

Center for Economic Research, Shandong University ( email )

Shanda Nanlu 27#
Jinan, Shandong 250100
China

HOME PAGE: http://www.cer.sdu.edu.cn/

Somnath Sen

University of Birmingham - Department of Economics ( email )

Economics Department
Birmingham, B15 2TT
United Kingdom

Paper statistics

Downloads
480
Rank
47,934
Abstract Views
1,723