• 论文 •

### 符号约束与时变参数SVAR模型的贝叶斯估计实现

• 出版日期:2016-10-15 发布日期:2016-10-18

### Bayesian Estimation of the SVAR Model with Sign Restrictions and Time-varying Parameters

Su Zhi et al.

• Online:2016-10-15 Published:2016-10-18

Abstract:

Traditionally, SVAR model is often identified through two methods: one is imposing constraints on structure parameters, the other is on the impulse response functions, most of which are strict equalities. The sign restrictions just defines the impulse response functions’ direction based on the priori theory, and identifies the model with a more relaxed inequality constraints, which can effectively reduce the influence of subjective factors. With the changes of the economic structure, the SVAR model’s parameters are changing over time, and the fixed parameters can’t effectively portray the economic development characteristics in different periods. Basing on the Gibbs sampling and the Bayesian inference theory, this paper introduces the detailed process of parameters’ estimation of the SVAR model with the time varying parameters. It respectively estimates VAR model, Sign-SVAR model and Sign-TVP-SVAR model by using Chinese and US data. The results show that sign constraints can effectively avoid the impulse response functions’ directional bias, and the time varying parameters can better characterize the structural variation of economic variables in different periods. It also proves that the Sign-TVP-SVAR model has obvious advantages in monetary policy analysis.