全球智库信息集成服务系统(G2TT): Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w29535
来源ID	Working Paper 29535
	Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic
	Frank Schorfheide; Dongho Song
发表日期	2021-12-06
出版年	2021
语种	英语
摘要	We resuscitated the mixed-frequency vector autoregression (MF-VAR) developed in Schorfheide and Song (2015, JBES) to generate macroeconomic forecasts for the U.S. during the COVID-19 pandemic in real time. The model combines eleven time series observed at two frequencies: quarterly and monthly. We deliberately did not modify the model specification in view of the COVID-19 outbreak, except for the exclusion of crisis observations from the estimation sample. We compare the MF-VAR forecasts to the median forecast from the Survey of Professional Forecasters (SPF). While the MF-VAR performed poorly during 2020:Q2, subsequent forecasts were at par with the SPF forecasts. We show that excluding a few months of extreme observations is a promising way of handling VAR estimation going forward, as an alternative of a sophisticated modeling of outliers.
主题	Econometrics ; Estimation Methods ; COVID-19
URL	https://www.nber.org/papers/w29535
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/587209
推荐引用方式 GB/T 7714	Frank Schorfheide,Dongho Song. Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic. 2021.

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
w29535.pdf（731KB）	智库出版物		限制开放	CC BY-NC-SA	浏览