.png){kind=icon}
返回
Forecasting German GDP using alternative factor models based on large datasets
.png){kind=icon}
PRE
AI
DOI：10.1002/for.1026.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the German economy. One model extracts factors by static principal components analysis; the second model is based on dynamic principal components obtained using frequency domain methods; the third model is based on subspace algorithms for state-space models. Out-of-sample forecasts show that the forecast errors of the factor models are on average smaller than the errors of a simple autoregressive benchmark model. Among the factor models, the dynamic principal component model and the subspace factor model outperform the static factor model in most cases in terms of mean-squared forecast error. However, the forecast performance depends crucially on the choice of appropriate information criteria for the auxiliary parameters of the models. In the case of misspecification, rankings of forecast performance can change severely. Copyright (c) 2007 John Wiley & Sons, Ltd.
Keyword:
factor models
static and dynamic factors
principal components
forecasting accuracy
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
2.3K
被引数:
3.0K
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
分享收藏分享收藏
查看更多内容