Baynesian Leading Indicators: Measuring and Predicting Economic Conditions
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.
|Date of creation:||22 Oct 1996|
|Date of revision:|
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