Top > JACIII > Solving the Dynamic Stochastic General Equilibrium Model with ...

single-jc.php

JACIII Vol.19 No.4 pp. 508-513
doi: 10.20965/jaciii.2015.p0508
(2015)

Paper:

Views over last 60 days: 1,114

Solving the Dynamic Stochastic General Equilibrium Model with Stochastic Volatility: An Application in China

Shangfeng Zhang*,**, Yuying Wang**, †, and Bing Xu**

*School of Economics, Zhejiang University
38 Zheda Road, Xihu District, Hangzhou, Zhejiang 310027, China

**Research Institute of Econometrics and Statistics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China

†Corresponding author

Received:
December 20, 2013
Accepted:
April 7, 2015
Published:
July 20, 2015
Creative Commons License
Keywords:
approximation, stochastic volatility, dynamic stochastic general equilibrium model
Abstract
A first-order approximation technique is not suited to handle issues such as welfare comparison, time-varying variance. Following Schmitt-Grohe and Uribe [1], in this paper, we derive a second-order approximation to estimate the dynamic stochastic equilibrium model with stochastic volatility, to capture the different impacts of the level shocks and the volatility shocks. Furthermore, the paper presents an application of standard quantitative New Keynesian business cycle model, and the results shows the negative effects of stochastic volatility shocks. Furthermore, the paper presents an application of standard quantitative New Keynesian business cycle model, and the empirical results find that the level shocks have positive effects on consumption, investment and output, while the volatility shocks have negative effects on consumption, investment and output.
Cite this article as:
S. Zhang, Y. Wang, and B. Xu, “Solving the Dynamic Stochastic General Equilibrium Model with Stochastic Volatility: An Application in China,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.4, pp. 508-513, 2015.
Data files:
References
  1. [1] S. Schmitt-Grohe and M. Uribe, “Solving dynamic general equilibrium models using a second-order approximation to the policy function,” J. of Economic Dynamics and Control, Vol.28, pp. 755-775, 2004.
  2. [2] B. Liu, “Dynamic Stochastic General Equilibrium Model and Its Application,” China Financial Publishing House, 2010.
  3. [3] F. Collard and M. Juillard, “Perturbation Methods for Rational Expectations Models,” Manuscript, CEPREMAP, Paris, February, 2001.
  4. [4] N. Shephard, “Stochastic Volatility: The New Palgrave Dictionary of Economics,” Palgrave MacMillan, 2008.
  5. [5] P. Townsend, “Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living,” University of California Press, 1979.
  6. [6] P. N. Ireland, “Sticky-price Models of the Business Cycle: Specification and Stability,” J. of Monetary Economic, Vol.47, No.1, pp. 3-18, 2001.
  7. [7] W. Ma, “Comparison between Quantity Instrument and Price Instrument in the Performance of Macroeconomic Control,” J. of Quantitative & Technical Economics, Vol.10, pp. 92-110, 2011.
  8. [8] S. Yuan et al., “Exchange Rate System, Financial Accelerator and Economic Undulation,” J. of Economic Research, Vol,1, pp. 57-70, 2011.
  9. [9] B. S. Bernanke, M. Gentler, and S. Gilchrist, “The Financial Accelerator in a Quantitative Business Cycle Framework,” Handbook of Macroeconomics, J. B. Taylor and M. Woodford (Ed.), Vol.1, pp. 1341-1393, 1999.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 01, 2024