Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa

Christopher Otrok and Charles Whiteman ()

International Economic Review, 1998, vol. 39, issue 4, 997-1014

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 computed by using posterior mean values of current and predictive distributions for the latent factor. Copyright 1998 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (148)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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: https://EconPapers.repec.org/RePEc:ier:iecrev:v:39:y:1998:i:4:p:997-1014

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0020-6598

Access Statistics for this article

International Economic Review is currently edited by Harold L. Cole

More articles in International Economic Review from Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297. Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and ().