Bayesian leading indicators: Measuring and predicting economic conditions in Iowa

Christopher Otrok, Charles H. Whiteman

Research output: Contribution to journalArticle

74 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)997-1014
Number of pages18
JournalInternational Economic Review
Volume39
Issue number4
DOIs
StatePublished - Nov 1998

Fingerprint

Leading indicators
Latent factors
Economic conditions
Markov chain Monte Carlo methods
Predictive distribution
Posterior distribution
Latent factor models
Coincident indicators

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

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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.",
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Bayesian leading indicators : Measuring and predicting economic conditions in Iowa. / Otrok, Christopher; Whiteman, Charles H.

In: International Economic Review, Vol. 39, No. 4, 11.1998, p. 997-1014.

Research output: Contribution to journalArticle

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