計(jì)量經(jīng)濟(jì)學(xué)研討會(huì) （2016年第2期）

【時(shí)間】2016年11月11日（星期五）12：00－14：00 【地點(diǎn)】明德主樓734會(huì)議室 【主講】黃乃靜 中央財(cái)經(jīng)大學(xué)經(jīng)濟(jì)學(xué)院 【主題】Weak Inference for Dynamic Stochastic General Equilibrium Models with Time-varying Parameters 【點(diǎn)評(píng)】時(shí)文東 中國人民大學(xué)經(jīng)濟(jì)學(xué)院 【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. 作者簡(jiǎn)介：         黃乃靜，美國波士頓學(xué)院經(jīng)濟(jì)學(xué)博士，現(xiàn)任職于中央財(cái)經(jīng)大學(xué)經(jīng)濟(jì)學(xué)院。研究領(lǐng)域包括宏觀計(jì)量、金融計(jì)量、量化和應(yīng)用宏觀經(jīng)濟(jì)學(xué)、預(yù)測(cè)以及金融經(jīng)濟(jì)學(xué)。   計(jì)量經(jīng)濟(jì)學(xué)研討會(huì)的學(xué)術(shù)活動(dòng)有兩種形式：前沿文獻(xiàn)選讀和同行學(xué)術(shù)交流。前沿文獻(xiàn)選讀由計(jì)量經(jīng)濟(jì)學(xué)教師團(tuán)隊(duì)選擇本領(lǐng)域頂尖雜志的前沿文獻(xiàn)，采用學(xué)生宣講、老師點(diǎn)評(píng)的方式加強(qiáng)人才培養(yǎng)、促進(jìn)本領(lǐng)域內(nèi)師生的廣泛交流。同行學(xué)術(shù)交流將邀請(qǐng)相關(guān)領(lǐng)域知名學(xué)者或頂尖高校畢業(yè)的博士就已形成的高水平工作論文進(jìn)行深入討論。歡迎相關(guān)領(lǐng)域老師和同學(xué)關(guān)注并參加！如有意作為點(diǎn)評(píng)老師或宣講學(xué)生參加前沿文獻(xiàn)選讀活動(dòng)，可以聯(lián)系經(jīng)濟(jì)學(xué)院邱志明（nashqzm@ruc.edu.cn）  
 
中國人民大學(xué)經(jīng)濟(jì)學(xué)院                                                                                               2016年11月8日