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作者

来源

来源 JOURNAL OF URBAN ECONOMICS,Vol.94,P.90-108

语言

英文

关键字

Economic activity index;Metropolitan statistical area;Recession;Dynamic factor model;Latent variable;EM algorithm;Housing supply elasticity;Dynamic spillovers;C38;E32;R11;R31

作者单位

Research Division, Federal Reserve Bank of St. Louis, 1 Federal Reserve Bank Plaza, St. Louis, MO 63102, United States;Department of Economics, John Cook School of Business, Saint Louis University, 3674 Lindell Boulevard, St. Louis, MO 63108, United States;Research Division, Federal Reserve Bank of St. Louis, 1 Federal Reserve Bank Plaza, St. Louis, MO 63102, United States;Department of Economics, John Cook School of Business, Saint Louis University, 3674 Lindell Boulevard, St. Louis, MO 63108, United States

摘要

We construct monthly economic activity indices for the 50 largest U.S. metropolitan statistical areas (MSAs) beginning in 1990. Each index is derived from a dynamic factor model based on twelve underlying variables capturing various aspects of metro area economic activity. To accommodate mixed-frequency data and differences in data-publication lags, we estimate the dynamic factor model using a maximum-likelihood approach that allows for arbitrary patterns of missing data. Our indices highlight important similarities and differences in business cycles across MSAs. While a number of MSAs experience sizable recessions during the national recessions of the early 1990s and early 2000s, other MSAs escape recessions altogether during one or both of these periods. Nearly all MSAs suffer relatively deep recessions near the recent Great Recession, but we still find significant differences in the depth of recent metro recessions. We relate the severity of metro recessions to a variety of MSA characteristics and find that MSAs with less-educated populations and less elastic housing supplies experience significantly more severe recessions. After controlling for national economic activity, we also find significant evidence of dynamic spillover effects in economic activity across MSAs.