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DSGE 模型全局识别问题的一种解法AI 解读

We develop an analytical framework to study global identification in structural models with forward-looking expectations. Our identification condition combines the similarity transformation linking the observationally equivalent state space systems with the constraints imposed on them by the model parameters. The key step of solving the identification problem then reduces to finding all roots of a system of polynomial equations. We show how it can be done using the concept…

线性化 DSGE 模型的全局识别AI 解读

This paper introduces a computational framework to analyze global identification of linearized DSGE models. A formal identification condition is established that relies on the restrictions linking the observationally equivalent state space representations and on the inherent constraints imposed by the model solution on the deep parameters. This condition is next used to develop an algorithm that checks global identification by searching for observationally equivalent model…

贝叶斯结构VAR模型中的脉冲响应先验AI 解读

This article develops a comprehensive approach to formally introduce the prior for impulse responses into the Bayesian structural VAR (SVAR) models, with considerable attention paid to careful prior specification. Furthermore, we present an efficient algorithm to estimate the SVAR with a Gaussian prior for impulse responses using simulation methods. We also illustrate our methodology using an example in which we verifiy some facts concerning the impact of monetary policy…