全球智库信息集成服务系统(G2TT): Approximately Normal Tests for Equal Predictive Accuracy in Nested Models

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/t0326
来源ID	Technical Working Paper 0326
	Approximately Normal Tests for Equal Predictive Accuracy in Nested Models
	Kenneth D. West; Todd Clark
发表日期	2006-08-15
出版年	2006
语种	英语
摘要	Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods (West (1996)) to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure.
主题	Econometrics ; Estimation Methods ; Macroeconomics ; Macroeconomic Models ; International Economics ; International Finance
URL	https://www.nber.org/papers/t0326
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/570087
推荐引用方式 GB/T 7714	Kenneth D. West,Todd Clark. Approximately Normal Tests for Equal Predictive Accuracy in Nested Models. 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
t0326.pdf（366KB）	智库出版物		限制开放	CC BY-NC-SA	浏览