Limited Information Bayesian Analysis of a Simultaneous Equation with an Autocorrelated Error Term and its Application to the U.S. Gasoline Market

Stanislav Radchenko ()

Econometrics from University Library of Munich, Germany

Abstract: Using Markov Chain Monte Carlo algorithms within the limited information Bayesian framework, we estimate the parameters of the structural equation of interest and test weak exogeneity in a simultaneous equation model with white noise as well as autocorrelated error terms. A numerical example and an estimation of the supply and demand equations of the U.S. gasoline market show that if we ignore autocorrelation we obtain unreasonable posterior distributions of the parameters of interest. Also we find that the hypothesis of the asymmetric effect of the changes in oil price on the changes in gasoline price is rejected. Oil inventory has a significant negative effect on the gasoline price.

Keywords: limited information Bayesian estimation; exogeneity; identifying restrictions; MCMC algorithms; U.S. gasoline market (search for similar items in EconPapers)
JEL-codes: C11 C30 C32 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2004-08-02
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: Type of Document - pdf; pages: 41
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0408001

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