全球智库信息集成服务系统(G2TT): DP9576 Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?

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来源类型	Discussion paper
规范类型	论文
来源ID	DP9576
	DP9576 Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?
	Barbara Rossi; Refet Gürkaynak; Burçin Kısacıkoğlu
发表日期	2013-07-28
出版年	2013
语种	英语
摘要	Recently, it has been suggested that macroeconomic forecasts from estimated DSGE models tend to be more accurate out-of-sample than random walk forecasts or Bayesian VAR forecasts. Del Negro and Schorfheide(2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse races. We compare the real-time forecasting accuracy of the Smets and Wouters DSGE model with that of several reduced form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed,low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.
主题	International Macroeconomics
关键词	Bayesian var Dsge Forecast comparison Forecast optimality Forecasting Real-time data
URL	https://cepr.org/publications/dp9576
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/538412
推荐引用方式 GB/T 7714	Barbara Rossi,Refet Gürkaynak,Burçin Kısacıkoğlu. DP9576 Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?. 2013.