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The Bayesian Modelling Of Inflation Rate In Romania

Author

Listed:
  • Mihaela Simionescu (Bratu)

    (Institute for Economic Forecasting of the Romanian Academy)

Abstract

Bayesian econometrics knew a considerable increase in popularity in the last years, joining the interests of various groups of researchers in economic sciences and additional ones as specialists in econometrics, commerce, industry, marketing, finance, micro-economy, macro-economy and other domains. The purpose of this research is to achieve an introduction in Bayesian approach applied in economics, starting with Bayes theorem. For the Bayesian linear regression models the methodology of estimation was presented, realizing two empirical studies for data taken from the Romanian economy. Thus, an autoregressive model of order 2 and a multiple regression model were built for the index of consumer prices. The Gibbs sampling algorithm was used for estimation in R software, computing the posterior means and the standard deviations. The parameters’ stability proved to be greater than in the case of estimations based on the methods of classical Econometrics.

Suggested Citation

  • Mihaela Simionescu (Bratu), 2014. "The Bayesian Modelling Of Inflation Rate In Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 147-160, June.
  • Handle: RePEc:rsr:journl:v:62:y:2014:i:2:p:147-160
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    References listed on IDEAS

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    1. Geweke, John & Koop, Gary & van Dijk, Herman (ed.), 2011. "The Oxford Handbook of Bayesian Econometrics," OUP Catalogue, Oxford University Press, number 9780199559084.
    2. Andrew Blake & Haroon Mumtaz, 2015. "Applied Bayesian Econometrics for central bankers," Handbooks, Centre for Central Banking Studies, Bank of England, number 36, April.
    3. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    4. Heij, Christiaan & de Boer, Paul & Franses, Philip Hans & Kloek, Teun & van Dijk, Herman K., 2004. "Econometric Methods with Applications in Business and Economics," OUP Catalogue, Oxford University Press, number 9780199268016.
    5. Chan,Joshua & Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2019. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9781108437493, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Constatin MITRUȚ & Mihaela GRUIESCU & Roxana Cristina VÎLCU, 2017. "Modeling the Causes of Inflation in Romania," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 21-38.
    2. Mihaela Simionescu & Svitlana Bilan & Beata Gavurova & Elena-Nicoleta Bordea, 2019. "Health Policies in Romania to Reduce the Mortality Caused by Cardiovascular Diseases," IJERPH, MDPI, vol. 16(17), pages 1-9, August.

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    More about this item

    Keywords

    Bayesian econometrics; Bayesian regression; Bayes’ theorem; Gibbs sampling algorithm; posterior mean;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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