| Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa (1999) | |||||||||||||||||
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| This paper designs and implements a Bayesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latent factor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are computed by using posterior mean values of current and predictive distributions for the latent factor. JEL Codes: C11, C32, E32. Keywords: Markov chain, Monte Carlo, index model, latent dynamic factor Running Head: Bayesian Leading Indicators * Manuscript received September 1996; revised December 1997. 1 This paper was initially prepared for presentation at the July 1996 NBER/NSF Seminar on Forecasting and Empirical Methods in Macroeconomics. We thank Francis Diebold, Robert Engle, John Geweke, Beth Ingram, Thomas Sargent, Christopher Sims, James Stock, Ruey Tsay, Mark Watson, and three anonymous referees for helpful comments. Sid Chib graciously supplied us with GAUSS code for posterior analysis of regression mo... | |||||||||||||||||
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