Using generalized impulse response functions to estimate nonlinear dynamic models
提出用广义脉冲响应函数匹配法估计非线性动态一般均衡模型，通过蒙特卡洛模拟和基于美国数据的实证，证明该方法比传统脉冲响应匹配能得到更精确的参数估计，并支持名义工资向下刚性假说。
This article proposes the use of generalized impulse response functions as a natural solution to the issues that arise when estimating nonlinear dynamic general equilibrium models by impulse response matching. Using a small-scale New Keynesian model with downward nominal wage rigidity as a testing ground, Monte Carlo analysis shows that the proposed estimation strategy delivers sharper parameter estimates than the matching of traditional impulse responses currently employed in the literature. An empirical application using U.S. data illustrates the use of the proposed estimation strategy. Results support the view that nominal wages are downwardly rigid, predict a frequency of nominal wage cuts in line with the microdata, and quantify frictional adjustment costs as a proportion of total output.