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Metro Business Cycles

Author

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  • Arias, Maria A.

    (Federal Reserve Bank of St. Louis)

  • Gascon, Charles S.

    () (Federal Reserve Bank of St. Louis)

  • Rapach, David E.

    () (Saint Louis University)

Abstract

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.

Suggested Citation

  • Arias, Maria A. & Gascon, Charles S. & Rapach, David E., 2014. "Metro Business Cycles," Working Papers 2014-46, Federal Reserve Bank of St. Louis, revised 09 May 2016.
  • Handle: RePEc:fip:fedlwp:2014-046
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    References listed on IDEAS

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    1. Measuring and comparing local economies: Memphis vs. Nashville
      by ? in FRED blog on 2016-01-21 20:00:23

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    1. repec:fip:fedpei:00018 is not listed on IDEAS
    2. Cohen, Jeffrey P. & Coughlin, Cletus C. & Yao, Vincent W., 2016. "Sales of Distressed Residential Property: What Have We Learned from Recent Research?," Review, Federal Reserve Bank of St. Louis, vol. 98(3), pages 159-188.
    3. repec:fip:fedprs:00005 is not listed on IDEAS
    4. Andrii Parkhomenko, 2018. "The Rise of Housing Supply Regulation in the US: Local Causes and Aggregate Implications," 2018 Meeting Papers 275, Society for Economic Dynamics.
    5. repec:eee:jmacro:v:57:y:2018:i:c:p:317-337 is not listed on IDEAS
    6. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.

    More about this item

    Keywords

    Economic activity index; Metropolitan statistical area; Recession; Dynamic factor model; Latent variable; EM algorithm; Mixed regressive; spatial autoregressive model; Dynamic spillovers;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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