Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data

Yifei Lyu, Jun Nie, Shu-Kuei X. Yang
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Abstract

The Covid-19 pandemic has created tremendous downward pressure on economic activity and revived interest in forecasting economic growth during severe downturns However, most dynamic factor models used to forecast GDP growth include only domestic data We construct a large data set of 77 countries representing over 90 percent of global GDP and show that including cross-country data helps produce more accurate forecasts of US GDP growth during economic downturns, but is less helpful in normal times We provide explanations why this is the case
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利用跨国数据预测美国经济衰退中的增长
然而,大多数用于预测GDP增长的动态因子模型只包括国内数据。我们构建了77个国家的大型数据集,占全球GDP的90%以上,结果表明,包括跨国数据有助于更准确地预测经济衰退期间的美国GDP增长。但在正常情况下用处不大。我们将解释为什么会出现这种情况
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