Identification and Estimation of Dynamic Stochastic General Equilibrium Models: Skewness Matters

本项目采用新的统计分布和计量经济学方法，研究偏度对线性和非线性动态随机一般均衡（DSGE）模型参数可辨识性和可估计性的影响。本项目的目的是分析DSGE模型，其中偏差不仅发生在误差项分布中，而且还发生在代理的决策规则中。这将使人们能够估计不对称的生产创新、向下的工资刚性和一个小但随时间变化的灾难可能性的宏观经济影响，并仔细地解开内源性和外源性偏度的传播渠道和影响。由于偏度是经济风险最重要的决定因素之一，为了缩小宏观经济和实证金融文献之间的差距，这些结果对下一代DSGE模型具有重要意义。

This project investigates the effect of skewness on identifiability and estimability of parameters in linear and nonlinear dynamic stochastic general equilibrium (DSGE) models using new statistical distributions and econometric methods. The objective of this project is to analyze DSGE models in which skewness occurs not only exogenously in the error term distribution, but also endogenously in the decision rules of agents. This will enable one to estimate the macroeconomic implications of asymmetric production innovations, downward wage rigidities and a small but time-varying probability of disaster, and to carefully disentangle the transmission channels and effects of endogenous and exogenous skewness. Since skewness is one of the most important determinants of economic risk, the results are significant for the next generation of DSGE models in order to narrow the gap between the macroeconomic and empirical financial literature.