Baynesian Leading Indicators: Measuring and Predicting Economic Conditions

Christopher Otrok () and Charles H. Whiteman ()

Macroeconomics from EconWPA

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)
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)
View list of references

Downloads: (external link)
http://129.3.20.41/eps/mac/papers/9610/9610002.pdf (application/pdf)
http://129.3.20.41/eps/mac/papers/9610/9610002.html (text/html)
http://129.3.20.41/eps/mac/papers/9610/9610002.ps.gz (application/postscript)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpma:9610002

Access Statistics for this paper

More papers in Macroeconomics from EconWPA
Series data maintained by EconWPA ().