[經(jīng)濟系學術(shù)報告]Local and Global Parameter Identification in DSGE Models Allowing for Indeterminacy
發(fā)文時間:2015-05-06
經(jīng)濟系學術(shù)報告



報告題目:Local and Global Parameter Identification in DSGE Models Allowing for Indeterminacy
報告人:曲中軍 教授
報告時間:2015年5月8日下午14:00-15:30
報告地點:明德主樓729
內(nèi)容簡介:This paper presents a unified framework for analyzing local and global identification in log linearized DSGE models that encompasses both determinacy and indeterminacy. The analysis is conducted from a frequency domain perspective. First, for local identification, it presents necessary and sufficient conditions for: (1) the identification of the structural parameters along with the sunspot parameters, (2) the identification of the former irrespective of the latter and (3) the identification of the former conditional on the latter. These conditions apply to both singular and nonsingular models and also permit checking whether a subset of frequencies can deliver identification. Second, for global identification, the paper considers a frequency domain expression for the Kullback-Leibler distance between two DSGE models and shows that global identification fails if and only if the minimized distance equals zero. As a by-product, it delivers parameter values that yield observational equivalence under identification failure. This condition requires nonsingularity but can be applied to nonsingular subsystems and across models with different structures. Third, to develop a further understanding of the strength of identification, the paper proposes a measure for the empirical closeness between two DSGE models. The measure gauges the feasibility of distinguishing one model from another using likelihood ratio tests based on a finite number of observations generated by the two models. The theory is illustrated using two small scale and one medium scale DSGE models. The results document that parameters can be identified under indeterminacy but not determinacy, that different monetary policy rules can be (nearly) observationally equivalent, and that identification properties can differ substantially between small and medium scale models.
報告人簡介:曲中軍,現(xiàn)波士頓大學經(jīng)濟系副教授。1998年南開大學數(shù)學系本科,之后于2003年和2005年分別獲波士頓大學碩士、博士學位。曾任教于美國伊利諾伊大學香檳分校經(jīng)濟系。在Econometrica,Journal of Econometrics,和Quantitative Economics等一流刊物上發(fā)表過多篇高質(zhì)量的文章。他的主要研究領(lǐng)域為計量經(jīng)濟理論,時間序列,宏觀計量以及實證金融等。






中國人民大學經(jīng)濟學院經(jīng)濟系 2015年05月