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.

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Year of publication:	1996-10-22
Authors:	Otrok, Christopher ; Whiteman, Charles H.
Institutions:	EconWPA

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Extent:	application/pdf text/html application/postscript
Series:	Macroeconomics.
Type of publication:	Book / Working Paper
Notes:	Zipped using PKZIP v2.04, encoded using UUENCODE v5.15. Zipped file includes 1 file --ui9614.wpa (MS Word file 26 pages) 26 pages
Classification:	E - Macroeconomics and Monetary Economics
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005561358