Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data
Jun Nie () and
Shu-Kuei X. Yang
No RWP 20-09, Research Working Paper from Federal Reserve Bank of Kansas City
To examine whether including economic data on other countries could improve the forecast of U.S. GDP growth, we construct a large data set of 77 countries representing over 90 percent of global GDP. Our benchmark model is a dynamic factor model using U.S. data only, which we extend to include data from other countries. We show that using cross-country data produces more accurate forecasts during the global financial crisis period. Based on the latest vintage data on August 6, 2020, the benchmark model forecasts U.S. real GDP growth in 2020:Q3 to be −6.9 percent (year-over-year rate) or 14.9 percent (quarter-over-quarter annualized rate), whereas the forecast is revised upward to −6.1 percent (year-over-year) or 19.1 percent (quarter-over-quarter) when cross-country data are used. These examples suggest that U.S. data alone may fail to capture the spillover effects of other countries in downturns. However, we find that foreign variables are much less useful in normal times.
Keywords: Forecasting; Dynamic factor model; GDP growth; Cross-country data; Global financial crisis; COVID-19 (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 C55 E32 E37 (search for similar items in EconPapers)
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