中文摘要：

传统识别SVAR模型的方法包括两类,一类是约束模型中的结构参数,另一类是约束脉冲响应函数,但多为严格的等式约束,符号约束则基于先验理论限定脉冲响应的方向,用较为宽松的不等式约束实现模型识别,能有效降低主观因素影响;同时随着经济结构的变化,SVAR模型的参数估计值有随时间变化的趋势,固定的参数估计值已不能有效刻画不同时期的经济发展状态。本文基于Gibbs抽样思想与贝叶斯统计推断理论,系统介绍符号约束下时变参数SVAR模型的贝叶斯估计方法,使用中国和美国数据,分别估计VAR模型、Sign-SVAR模型和Sign-TVP-SVAR模型。实证结果发现,符号约束能够有效避免脉冲响应的方向性偏误,时变参数能够更好刻画不同时期内经济变量的结构时变特征,在货币政策分析中具有明显优势。

英文摘要：

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.