全球智库信息集成服务系统(G2TT): DP9312 Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP9312
	DP9312 Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility
	Massimiliano Marcellino; Andrea Carriero; Todd Clark
发表日期	2013-01-27
出版年	2013
语种	英语
摘要	This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to GDP, we consider versions of the model with both constant variances and stochastic volatility. We also evaluate models with either constant or time-varying regression coefficients. We use Bayesian methods to estimate the model, in order to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of real-time GDP growth in the U.S. from 1985 through 2011. In terms of point forecasts, our proposal is comparable to alternative econometric methods and survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful, while parameter time-variation does not seem to matter.
主题	International Macroeconomics
关键词	Bayesian methods Forecasting Mixed frequency models Prediction
URL	https://cepr.org/publications/dp9312
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/538148
推荐引用方式 GB/T 7714	Massimiliano Marcellino,Andrea Carriero,Todd Clark. DP9312 Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility. 2013.