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High-frequency credit spread information and macroeconomic forecast revision

机译：高频信贷利差信息和宏观经济预测修正

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摘要

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during 'bad' economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：我们检查专业预测员在修订其经济预测时是否纳入有关信贷状况的高频信息。使用混合数据抽样回归方法，我们发现，在总体和单个预测水平上，每日信贷息差对产出增长的月度预测修订具有显着的预测能力。在“恶劣”的经济状况下，这种关系显示出显着的强势关系，这表明预测者预计，在经济下滑期间，信贷紧缩的影响将更为明显，这表明金融发展对宏观经济总量的放大作用。预测并未以均等的方式合并收到的所有财务信息，这意味着在合并信用利差信息时会出现信息僵化的情况。 （C）2019国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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