Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa
AbstractThis 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.
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Bibliographic InfoArticle provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.
Volume (Year): 39 (1998)
Issue (Month): 4 (November)
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Web page: http://www.econ.upenn.edu/ier
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Other versions of this item:
- Otrok, C. & Whiteman, C.H., 1996. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," Working Papers 96-14, University of Iowa, Department of Economics.
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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