Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions
Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions
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在存在数据修正的情况下，使用自回归模型实时预测通货膨胀和产出增长
DOI：
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发表时间：
2013
期刊：
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通讯作者：
A. Galvão
中科院分区：
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作者：
Michael P. Clements;A. Galvão
我们研究如何从模型，包括自回归项的实时预测的准确性，可以通过估计模型的“轻微修订”的数据，而不是使用数据从最新可用的年份。在轻微修订的数据上估计自回归模型的好处与数据修订过程的性质和真实值的基本过程有关。从经验上讲，我们发现改进的均方根预测误差为2-4%时，预测产出增长和通货膨胀的单变量模型和8%的多变量模型。我们发现，多年份模型，明确的模型数据修订，需要大的估计样本，以提供有竞争力的预测。版权所有© 2012约翰威利父子有限公司.
We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.