Baynesian Leading Indicators: Measuring and Predicting Economic Conditions
AbstractThis 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.
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Bibliographic InfoPaper provided by EconWPA in its series Macroeconomics with number 9610002.
Length: 26 pages
Date of creation: 22 Oct 1996
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- E - Macroeconomics and Monetary Economics
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