全球智库信息集成服务系统(G2TT): DP7677 Forecasting with Factor-augmented Error Correction Models

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP7677
	DP7677 Forecasting with Factor-augmented Error Correction Models
	Anindya Banerjee; Massimiliano Marcellino; Igor Masten
发表日期	2010-02-07
出版年	2010
语种	英语
摘要	As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.
主题	International Macroeconomics
关键词	Cointegration Dynamic factor models Error correction models Factor-augmented error correction models Favar Forecasting
URL	https://cepr.org/publications/dp7677
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/536514
推荐引用方式 GB/T 7714	Anindya Banerjee,Massimiliano Marcellino,Igor Masten. DP7677 Forecasting with Factor-augmented Error Correction Models. 2010.