Baynesian Leading Indicators : Measuring and Predicting Economic Conditions

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

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Year of publication:	1998
Authors:	Otrok, Chris ; Whiteman, Charles H.
Publisher:	[S.l.] : SSRN
Subject:	Theorie \| Theory \| Wirtschaftsindikator \| Economic indicator \| Prognoseverfahren \| Forecasting model \| Frühindikator \| Leading indicator \| Markov-Kette \| Markov chain
Description of contents:	Abstract [papers.ssrn.com]

Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 1996 erstellt Volltext nicht verfügbar
Classification:	C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C43 - Index Numbers and Aggregation ; C53 - Forecasting and Other Model Applications
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014103328