Forecast uncertainties in macroeconomic modeling: An application to the UK economy
成果类型：
Article
署名作者：
Garratt, A; Lee, K; Pesaran, MH; Shin, Y
署名单位：
University of Leicester; University of Cambridge; University of Edinburgh
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/016214503000000765
发表日期：
2003
页码：
829-838
关键词：
摘要：
We argue that probability forecasts convey information on the uncertainties that surround macroeconomic forecasts in a straightforward manner that is preferable to other alternatives, including the use of confidence intervals. Probability forecasts obtained using a small benchmark macroeconometric model and a number of other alternatives are presented and evaluated using recursive forecasts generated over the period 1999q1-2001q1. Out-of-sample probability forecasts of inflation and output growth are also provided over the period 2001q2-2003q1, and their implications are discussed in relation to the Bank of England's inflation target and the need to avoid recessions, both as separate events and jointly. The robustness of the results to parameter and model uncertainties is also investigated using Bayesian model-averaging techniques.
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