Baynesian Leading Indicators: Measuring and Predicting Economic Conditions

Christopher Otrok and Charles Whiteman ()

Macroeconomics from University Library of Munich, Germany

Abstract: 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 given by posterior mean values of current and predictive distributions for the latent factor.

JEL-codes: E (search for similar items in EconPapers)
Pages: 26 pages
Date: 1996-10-22
Note: Zipped using PKZIP v2.04, encoded using UUENCODE v5.15. Zipped file includes 1 file --ui9614.wpa (MS Word file 26 pages)
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