Baynesian Leading Indicators: Measuring and Predicting Economic Conditions

Posted: 11 Nov 1996

See all articles by Chris Otrok

Chris Otrok

University of Missouri; Federal Reserve Banks - Federal Reserve Bank of St. Louis

Charles H. Whiteman

Pennsylvania State University - Smeal College of Business

Date Written: October 1996

Abstract

This paper designs and implements a Baynesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latentfactor 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 Classification: C11, C15, C43, C53

Suggested Citation

Otrok, Christopher and Whiteman, Charles H., Baynesian Leading Indicators: Measuring and Predicting Economic Conditions (October 1996). Available at SSRN: https://ssrn.com/abstract=3482

Christopher Otrok

University of Missouri ( email )

118 Professional Building
Columbia, MO 65211
United States

Federal Reserve Banks - Federal Reserve Bank of St. Louis ( email )

411 Locust St
Saint Louis, MO 63011
United States

Charles H. Whiteman (Contact Author)

Pennsylvania State University - Smeal College of Business

University Park, PA 16802
United States
814-863-0448 (Phone)

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