Estimating Nonlinear DSGE Models with Moments Based Methods
Estimating Nonlinear DSGE Models with Moments Based Methods
摘要
This article suggests a new approach to approximating moments for nonlinear DSGE models. This approach is fast and sufficiently accurate to estimate nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several modifications of the suggested approach. Approximations of the moments are close to the results of the large sample Monte Carlo estimation. The quality of parameter estimation using our suggested approach is close to the Central Difference Kalman Filter （CDKF）; and our suggested approach is much faster.
This article suggests a new approach to approximating moments for nonlinear DSGE models. This approach is fast and sufficiently accurate to estimate nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several modifications of the suggested approach. Approximations of the moments are close to the results of the large sample Monte Carlo estimation. The quality of parameter estimation using our suggested approach is close to the Central Difference Kalman Filter （CDKF）; and our suggested approach is much faster.
参考文献18
-
1An S, Schorfheide F (2006). Bayesian analysis of DSGE models. Federal Reserve Bank of Philadelphia, Working Papers No. 06-5.
-
2Andreasen M M (2008). Non-linear DSGE models, the Central Difference Kalman Filter, and the Mean Shifted Particle Filter. CREATES Research Paper 2008-33. Available at SSRN: http://ssrn.com/abstract= 1148079.
-
3Canova F (2007). Methods for Applied Macroeconomic Research. Princeton, NJ: Princeton University Press.
-
4Collard F, Juillard M (2001). Accuracy of stochastic perturbation methods: The case of asset pricing models. Journal of Economic Dynamics and Control, 25(6-7): 979-999.
-
5Creel M, Kristensen D (2011). Indirect likelihood inference. Dynare Working Papers from CEPREMAP, No. 8.
-
6DeJong D N, Dave C (2007). Structural Macroeconometrics. Princeton, N J: Princeton University Press.
-
7Femandez-Villaverde J, Guerron P A, Rubio-Ramirez J F (2010). Reading the recent monetary history of the United States, 1959-2007. Review, issue May, 311-338.
-
8Hamilton J D (1994). Time Series Analysis. Princeton, N J: Princeton University Press.
-
9Ivashchenko S (2013). DSGE model estimation on the basis of second-order approximation. Computational Economics, DOI 10.1007/s10614-013-9363-1.
-
10Jinill K, Ruge-Murcia F J (2009). How much inflation is necessary to grease the wheels? Journal of Monetary Economics, 56(3): 365-377.
-
1匙玉娟,许成,许杰,于海涛,张俊丽.两两稳定性网络模型的构造[J].青岛大学学报（自然科学版）,2014,27(1):23-26.
-
2zhou Xiaoyan.Product Woes[J].Beijing Review,2013,56(13):32-35.
-
3董青利.DSG数字信号发生器[J].无线电,2002(11):36-36.
-
4陈晓威,张国毅,李彦志,刘歌.基于SRCDKF的相位差变化率无源定位算法[J].电子信息对抗技术,2016,31(1):8-11. 被引量：1
-
5余永健,肖琨,黄顺吉.基于矩方法的多视广义极化K-分布参数的估计[J].信号处理,2000,16(3):216-220.
-
6陈云峰,付永明,张炜.基于矩方法的通信信号信噪比估计算法研究[J].通信技术,2015,48(1):23-26. 被引量：3
-
7胡文琳,王永良,王首勇.基于矩方法的K分布杂波参数估计研究[J].雷达科学与技术,2007,5(3):194-198. 被引量：3
-
8Zhou Qun, Peng Yuhua, Wang Wenbing & Xiao Yanming The Microwave Engineering and Optical Communication Institute,Xi’an Jiaotong University,710049, P. R. China.On the Use of Wavelet MoM to Solve the EFIE[J].Journal of Systems Engineering and Electronics,1997,8(4):23-33.
-
9徐伟,陈永森.一种K-分布杂波参数估计方法[J].舰船电子对抗,2013,36(3):82-84. 被引量：6
-
10廖锋,张晓红,张劲,李增智.应用DSET工具开发TMN的Agent[J].数据通信,1999(2):8-11.
