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A Dynamic Index Model for Large Cross Sections

In: Business Cycles, Indicators, and Forecasting

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  • Danny Quah
  • Thomas J. Sargent

Abstract

This paper shows how standard methods can be used to formulate and estimate a dynamic index model for random fieldsstochastic processes indexed by time and cross section where the time-series and cross-section dimensions are comparable in magnitude. We use these to study dynamic comovements of sectoral employment in the U.S. economy. The dynamics of employment in sixty sectors is well explained using only two unobservable factors; those factors are also strongly correlated with GNP growth.
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Suggested Citation

  • Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 285-310, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:7195
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    References listed on IDEAS

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    1. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    2. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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