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Bayesian Methods in Econometrics

Author

Listed:
  • Aivazian, Sergei

    (CEMI RAS, Moscow, Russia)

Abstract

This consultation deals with the Bayesian approach to econometric analysis. It is based on subjective probability methods of maximizing utilization of both the prior information and observations of a given process. Bayesian methods are generally used in the theory and practice of econometrics and included in the curriculum of master programs of the leading world universities. The advantage of using the Bayesian approach (in comparison with the traditional one) may be particularly seen in a higher precision of statistical inference when dealing with small samples what is typical in econometric modeling

Suggested Citation

  • Aivazian, Sergei, 2008. "Bayesian Methods in Econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 9(1), pages 93-130.
    • Handle: RePEc:ris:apltrx:0062
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    Citations

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    Cited by:

    1. Shulgin, Andrei, 2014. "How much monetary policy rules do we need to estimate DSGE model for Russia?," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 36(4), pages 3-31.
    2. Slutskin, Lev, 2015. "Definition of a prior distribution in Bayesian analysis by minimizing Kullback–Leibler divergence under data availability," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 40(4), pages 129-141.
    3. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.
    4. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    5. Slutskin, L., 2017. "Graphical Statistical Methods for Studying Causal Effects. Bayesian Networks," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 12-30.
    6. Tsesliv Olga V., 2013. "Web-analytics for Increase of Efficiency of the Web-site of a Department of a Higher Educational Establishment," Business Inform, RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE), Kharkiv National University of Economics, issue 10, pages 161-167.
    7. Slutskin, Lev, 2010. "Bayesian analysis in the case of an estimated parameter following a stochastic process," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 20(4), pages 119-131.

    More about this item

    Keywords

    bayesian approach to econometric analysis; small samples; prior information;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • 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

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