利用庞大的调查预期面板预测欧元区通胀率

IF 6.9 2区 经济学 Q1 ECONOMICS
Florian Huber , Luca Onorante , Michael Pfarrhofer
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引用次数: 0

摘要

在本文中,我们利用从欧盟委员会商业和消费者调查中获得的大量有关调查预期的时间序列来预测欧元区的通货膨胀及其主要组成部分。为了使这样一个庞大模型的估算变得简单易行,我们利用计算统计的最新进展进行了后验模拟和推断。我们的研究结果表明,将企业和消费者对未来经济发展的广泛看法纳入模型,可为预测价格和评估通胀尾部风险提供有用信息。这些预测方面的改进来自于与预期通胀相关的调查以及与总体经济环境相关的其他问题。最后,我们发现企业对未来的预期似乎比消费者的预期更具预测性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting euro area inflation using a huge panel of survey expectations

In this paper, we forecast euro area inflation and its main components using a massive number of time series on survey expectations obtained from the European Commission’s Business and Consumer Survey. To make the estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posterior simulation and inference. Our findings suggest that including a wide range of firms’ and consumers’ opinions about future economic developments offers useful information to forecast prices and assess tail risks to inflation. These predictive improvements arise from surveys related to expected inflation and other questions related to the general economic environment. Finally, we find that firms’ expectations about the future seem to have more predictive content than consumer expectations.

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来源期刊
CiteScore
17.10
自引率
11.40%
发文量
189
审稿时长
77 days
期刊介绍: The International Journal of Forecasting is a leading journal in its field that publishes high quality refereed papers. It aims to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research, and improving the practice of forecasting. It welcomes various points of view and encourages debate to find solutions to field-related problems. The journal is the official publication of the International Institute of Forecasters (IIF) and is indexed in Sociological Abstracts, Journal of Economic Literature, Statistical Theory and Method Abstracts, INSPEC, Current Contents, UMI Data Courier, RePEc, Academic Journal Guide, CIS, IAOR, and Social Sciences Citation Index.
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