Forecasting Output
Forecasting Output
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DOI：
10.1016/b978-0-444-53683-9.00003-7
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发表时间：
2013-01-01
期刊：
影响因子：
--
通讯作者：
Potter, Simon
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文献类型：
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作者：
Chauvet, Marcelle;Potter, Simon
本章回顾了最近关于产出预测的文献，并考察了几种模型对美国产出增长的实时预测能力。特别是，它评估的线性和非线性结构和简化形式模型的短期预测的准确性，并判断预测的产出增长。我们的重点是只使用预测时可用的信息，以便实时重现预测人员面临的预测问题。我们发现，有一个很大的差异，在不同的商业周期阶段的预测性能。特别是，预测衰退期间的产出增长比预测扩张期间的产出增长要困难得多。简单的线性和非线性自回归模型在预测扩张期间的产出增长方面具有最佳的准确性，尽管动态随机一般均衡模型和带有金融变量的向量自回归模型做得相对较好。另一方面，我们发现大多数模型在预测衰退期间的产出增长方面表现不佳。基于非线性动态因子模型的自回归模型考虑了扩张和衰退之间的不对称性，在衰退期间显示出最佳的真实的时间预测精度。尽管蓝筹的预测具有可比性，但动态因素马尔可夫转换模型具有更好的准确性，特别是在真实的时间内，在衰退期间产出下降的时间和深度方面。研究结果表明，在正常时期考虑单独的预测模型，以及特别为经济衰退和金融危机等突变时期设计的模型，会有很大的收益。
This chapter surveys the recent literature on output forecasting, and examines the real-time forecasting ability of several models for U.S. output growth. In particular, it evaluates the accuracy of short-term forecasts of linear and nonlinear structural and reduced-form models, and judgmental forecasts of output growth. Our emphasis is on using solely the information that was available at the time the forecast was being made, in order to reproduce the forecasting problem facing forecasters in real-time. We find that there is a large difference in forecast performance across business cycle phases. In particular, it is much harder to forecast output growth during recessions than during expansions. Simple linear and nonlinear autoregressive models have the best accuracy in forecasting output growth during expansions, although the dynamic stochastic general equilibrium model and the vector autoregressive model with financial variables do relatively well. On the other hand, we find that most models do poorly in forecasting output growth during recessions. The autoregressive model based on the nonlinear dynamic factor model that takes into account asymmetries between expansions and recessions displays the best real time forecast accuracy during recessions. Even though the Blue Chip forecasts are comparable, the dynamic factor Markov switching model has better accuracy, particularly with respect to the timing and depth of output fall during recessions in real time. The results suggest that there are large gains in considering separate forecasting models for normal times and models especially designed for periods of abrupt changes, such as during recessions and financial crises.