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Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market

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  • Radchenko, Stanislav
  • Tsurumi, Hiroki

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.
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  • Radchenko, Stanislav & Tsurumi, Hiroki, 2006. "Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market," Journal of Econometrics, Elsevier, vol. 133(1), pages 31-49, July.
  • Handle: RePEc:eee:econom:v:133:y:2006:i:1:p:31-49
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    5. Chen, Chaoyi & Polemis, Michael & Stengos, Thanasis, 2018. "On the Examination of Competition in the Petroleum Industry: A Pooled Panel Threshold Analysis," MPRA Paper 89671, University Library of Munich, Germany.
    6. Bruzikas, Tadas & Soetevent, Adriaan, 2014. "Detailed data and changes in market structure," Research Report 14027-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Tadas Bruzikas & Adriaan R. Soetevent, 2014. "Detailed Data and Changes in Market Structure: The Move to Unmanned Gasoline Service Stations," Tinbergen Institute Discussion Papers 14-123/VII, Tinbergen Institute.
    8. Deltas, George & Polemis, Michael, 2020. "Estimating retail gasoline price dynamics: The effects of sample characteristics and research design," Energy Economics, Elsevier, vol. 92(C).
    9. Perdiguero-García, Jordi, 2013. "Symmetric or asymmetric oil prices? A meta-analysis approach," Energy Policy, Elsevier, vol. 57(C), pages 389-397.
    10. Jordi Perdiguero-García, 2010. "“Symmetric or asymmetric gasoline prices? A metaanalysis approach”," IREA Working Papers 201013, University of Barcelona, Research Institute of Applied Economics, revised Nov 2010.
    11. Chuang, Chia-Hung & Zhao, Yabing, 2019. "Demand stimulation in finished-goods inventory management: Empirical evidence from General Motors dealerships," International Journal of Production Economics, Elsevier, vol. 208(C), pages 208-220.
    12. Wlaslowski, Szymon & Binner, Jane & Guiletti, Monica & Joseph, Nathan & Nilsson, Birger, 2007. "New York mark-ups on petroleum products," Working Papers 2008:2, Lund University, Department of Economics.
    13. Stanislav Radchenko, 2004. "Lags in the response of gasoline prices to changes in crude oil," Econometrics 0406001, University Library of Munich, Germany.
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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