Forecasting US Output Growth with Non-Linear Models in the Presence of Data Uncertainty

被引：0

作者：

Clements, Michael P. ^{[1
]}

机构：

[1] Univ Warwick, Coventry CV4 7AL, W Midlands, England

来源：

STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS | 2012年 / 16卷 / 01期

关键词：

REAL-TIME DATA; DATA REVISIONS; DATA SET; INFLATION; SERIES; ERRORS;

D O I：

10.1515/1558-3708.1865

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

引用

页数：27

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