Using Arbitrary Precision Arithmetic to Sharpen Identification Analysis for DSGE Models
2018/05/29
研討會日期 : 2018-05-29
時間 : 14:30
主講人 : Professor Denis Tkachenko
主持人 : Professor Chu-An Liu
地點 : Conference Room B110
演講者簡介 : Professor Tkachenko received his Ph.D. in Economics at Boston University in 2012. He is currently an Assistant Professor at Department of Economics, National University of Singapore. His research interests are Econometric Theory, Macroeconometrics, Financial Econometrics.
演講摘要 : This paper is at the intersection of macroeconomics and modern computer arithmetic. It seeks to apply arbitrary-precision arithmetic to resolve practical difficulties arising in the identification analysis of log linearized DSGE models. The main focus is on methods in Qu and Tkachenko (2012, 2017) since the framework appears to be the most comprehensive to date. Working with this arithmetic, we develop the following three-step procedure for analyzing local and global identification. (1) The DSGE model solution algorithm is modified so that all the relevant objects are computed as multiprecision entities allowing for indeterminacy. (2) The rank condition and the Kullback-Leibler distance are computed using arbitrary-precision Gauss-Legendre quadrature. (3) Minimization is carried out by combining double-precision global search algorithms and arbitrary-precision local search algorithms, where the criterion for convergence is set based on the chosen precision level, so that whether the minimized value is zero can be effectively examined. In an application to a model featuring monetary and policy interactions (Leeper, 1991 and Tan and Walker, 2015), we find that the arithmetic removes all ambiguity in the analysis. As a result, we reach clear conclusions showing observational equivalence both within the same policy regime and across different policy regimes. We further illustrate the feasibility and informativeness of the approach using the medium scale model of Schmitt-Grohe and Uribe (2012).
