A Dynamic Index Model for Large Cross Sections
In: Business Cycles, Indicators and Forecasting
This paper shows how standard methods can be used to formulate and estimate a dynamic index model for random fields - stochastic processes indexed by time and cross section where the time-series and cross section dimensions are comparable in magnitude. We use these study dynamic co-movements of sectoral employment in the US 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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- Robert B. Litterman, 1985.
"Forecasting with Bayesian vector autoregressions five years of experience,"
274, Federal Reserve Bank of Minneapolis.
- 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.
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