Local and Global Parameter Identification in DSGE Models Allowing for Indeterminacy

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• 研討會日期 : 2014-06-27
• 時間 : 14:00
• 主講人 : Professor Zhongjun Qu
• 地點 : Conference Room B110
• 演講者簡介 : Professor Qu received his Ph.D. in Economics from Boston University, at 2005. He is currently an Associate Professor at Department of Economics, Boston University. His major research fields are Econometrics and Time Series; and minor research fields are Quantitative Macroeconomics & Empirical Finance.
• 演講摘要 : 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 introduces 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 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. Although the paper focuses on DSGE models, the theory developed is applicable to other dynamic linear models with well defined spectra, such as the (factor augmented) vector autoregressive moving average model. The theory is illustrated using two small scale and one medium scale DSGE model.