计量经济学研讨会 （2016年第2期）

【时间】2016年11月11日（星期五）12：00－14：00 【地点】明德主楼734会议室 【主讲】黄乃静 中央财经大学bat365在线官网登录 【主题】Weak Inference for Dynamic Stochastic General Equilibrium Models with Time-varying Parameters 【点评】时文东 bat365在线官网登录 【摘要】This paper studies proper inference and asymptotically accurate structural break tests for parameters in Dynamic Stochastic General Equilibrium (DSGE) models in a maximum likelihood framework. Two empirically relevant issues may invalidate the conventional inference procedures and structural break tests for parameters in DSGE models: (i) weak identification and (ii) moderate parameter instability. DSGE literatures focus on dealing with weak identification issue, but ignore the impact of moderate parameter instability. This paper contributes to the literature via considering the joint impact of two issues in DSGE framework. The main results are: in a weakly identified DSGE model, (i) moderate instability from weakly identified parameters would not affect the validity of standard inference procedures or structural break tests; (ii) however, if strongly identified parameters are featured with moderate time-variation, the asymptotic distributions of test statistics would deviate from standard ones and would no longer be nuisance parameter free, which renders standard inference procedures and structural break tests invalid and provides practitioners misleading inference results; (iii) as long as I concentrate out strongly identified parameters, the instability impact of them would disappear as the sample size goes to infinity, which recovers the power of conventional inference procedure and structural break tests for weakly identified parameters. To illustrate my results, I simulate and estimate a modified version of the Hansen (1985) Real Business Cycle model and find that my theoretical results provide reasonable guidance for finite sample inference of the parameters in the model. I show that confidence intervals that incorporate weak identification and moderate parameter instability reduce the biases of confidence intervals that ignore those effects. While I focus on DSGE models in this paper, all of my theoretical results could be applied to any linear dynamic models or nonlinear GMM models. 作者简介：         黄乃静，美国波士顿学院经济学博士，现任职于中央财经大学bat365在线官网登录。研究领域包括宏观计量、金融计量、量化和应用宏观经济学、预测以及金融经济学。   计量经济学研讨会的学术活动有两种形式：前沿文献选读和同行学术交流。前沿文献选读由计量经济学教师团队选择本领域顶尖杂志的前沿文献，采用学生宣讲、老师点评的方式加强人才培养、促进本领域内师生的广泛交流。同行学术交流将邀请相关领域知名学者或顶尖高校毕业的博士就已形成的高水平工作论文进行深入讨论。欢迎相关领域老师和同学关注并参加！如有意作为点评老师或宣讲学生参加前沿文献选读活动，可以联系bat365在线官网登录邱志明（nashqzm@ruc.edu.cn）  
 
bat365在线官网登录                                                                                               2016年11月8日