全球智库信息集成服务系统(G2TT): DSGE Models in a Data-Rich Environment

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w12772
来源ID	Working Paper 12772
	DSGE Models in a Data-Rich Environment
	Jean Boivin; Marc Giannoni
发表日期	2006-12-22
出版年	2006
语种	英语
摘要	Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.
主题	Econometrics ; Estimation Methods ; Macroeconomics ; Macroeconomic Models ; Business Cycles
URL	https://www.nber.org/papers/w12772
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/570450
推荐引用方式 GB/T 7714	Jean Boivin,Marc Giannoni. DSGE Models in a Data-Rich Environment. 2006.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
w12772.pdf（639KB）	智库出版物		限制开放	CC BY-NC-SA	浏览