与计量、金融与大数据工作坊联合举办

【主讲人】谈非 副教授
【主持人】高明 长聘副教授
【报告题目】DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors

【报告摘要】Presently there is growing interest in dynamic stochastic general equilibrium (DSGE) models that have more parameters, endogenous variables, exogenous shocks, and observables than the Smets and Wouters (2007) model, and substantial additional complexities from non-Gaussian distributions and the incorporation of time-varying volatility. The popular DYNARE software package, which has proved useful for small and medium-scale models is, however, not capable of handling such models, thus inhibiting the formulation and estimation of more realistic DSGE models. A primary goal of this paper is to introduce a user-friendly MATLAB software program designed to reliably estimate high-dimensional DSGE models. It simulates the posterior distribution by the tailored random block Metropolis-Hastings (TaRB-MH) algorithm of Chib and Ramamurthy (2010), calculates the marginal likelihood by the method of Chib (1995) and Chib and Jeliazkov (2001), and includes various post-estimation tools that are important for policy analysis, for example, functions for generating point and density forecasts. Another goal is to provide pointers on the prior, estimation, and comparison of these DSGE models. An extended version of the new Keynesian model of Leeper, Traum and Walker (2017) that has 51 parameters, 21 endogenous variables, 8 exogenous shocks, 8 observables, and 1,494 non-Gaussian and nonlinear latent variables is considered in detail.

【主讲人介绍】Dr. Tan's research agenda is organized around three areas: macroeconomics, Bayesian statistics, and evolutionary dynamics. One line of his current research develops time and frequency-domain approaches to dynamic equilibrium models of expectations formation. Another line develops Markov chain Monte Carlo methods for estimating large-scale structural models. These tools are applied to study monetary and fiscal policy, asset prices, and a variety of related topics in macroeconomics and finance. Recently, he likes to train Bayesian neural nets regularized by economic theory. His previous research studies the evolution of cooperative and altruistic human behavior. Currently, Dr. Tan teaches macroeconomics and econometrics at Saint Louis University. Meanwhile, he works on competitive programming projects of data science. He is also a thematic investor for space economy and has rich experience in derivative trading, publishing papers on Top journals such as JET.