全球智库信息集成服务系统(G2TT): DP15388 Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP15388
	DP15388 Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints
	Borağan Aruoba; Pablo Cuba-Borda; Kenji Hilga-Flores; Frank Schorfheide; Sergio Villalvazo
发表日期	2020-10-19
出版年	2020
语种	英语
摘要	We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces the persistence of the Markov chain, improves the accuracy of Monte Carlo approximations of posterior moments, and drastically speeds up computations. We use the techniques to estimate a small-scale DSGE model to assess the effects of the government spending portion of the American Recovery and Reinvestment Act in 2009 when interest rates reached the zero lower bound.
主题	Monetary Economics and Fluctuations
关键词	Bayesian estimation Effective lower bound on nominal interest rates Nonlinear filtering Nonlinear solution methods Particle mcmc
URL	https://cepr.org/publications/dp15388
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/544373
推荐引用方式 GB/T 7714	Borağan Aruoba,Pablo Cuba-Borda,Kenji Hilga-Flores,et al. DP15388 Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints. 2020.