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Bayesian Model Averaging in R

Author

Listed:
  • Shahram Amini

    (Department of Economics, Virginia Polytechnic Institute and State University)

  • Christopher F. Parmeter

    (Department of Economics, University of Miami)

Abstract

Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own code, which has obvious appeal. However, canned statistical software can ameliorate one's own analysis if they are not intimately familiar with the nuances of computer coding. Moreover, many researchers would prefer user ready software to mitigate the inevitable time costs that arise when hard coding an econometric estimator. To that end, this paper describes the relative merits and attractiveness of several competing packages in the statistical environment R to implement a Bayesian model averaging exercise.

Suggested Citation

  • Shahram Amini & Christopher F. Parmeter, 2011. "Bayesian Model Averaging in R," Working Papers 2011-9, University of Miami, Department of Economics.
    • Handle: RePEc:mia:wpaper:2011-9
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    File URL: https://www.herbert.miami.edu/_assets/files/repec/wp2011-9.pdf
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    References listed on IDEAS

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    Cited by:

    1. Leroi RAPUTSOANE, 2015. "Alternative Measures of Credit Extension for Countercyclical Buffer Decisions in South Africa," Turkish Economic Review, KSP Journals, vol. 2(4), pages 210-221, December.
    2. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    3. Walid Oueslati & Julien Salanié & JunJie Wu, 2014. "Urbanization and Agricultural Structural Adjustments: Some Lessons from European Cities," Working Papers 1442, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    4. Leroi RAPUTSOANE, 2016. "Disaggregated Credit Extension and Financial Distress in South Africa," Journal of Economics Library, KSP Journals, vol. 3(2), pages 226-240, June.
    5. D'Andrea, Sara, 2022. "Are there any robust determinants of growth in Europe? A Bayesian Model Averaging approach," International Economics, Elsevier, vol. 171(C), pages 143-173.
    6. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.
    7. Leroi RAPUTSOANE, 2016. "Real Effective Exchange Rates Comovements and the South African Currency," Journal of Economics Library, KSP Journals, vol. 3(1), pages 57-68, March.
    8. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    9. Błażejowski, Marcin & Kwiatkowski, Jacek, 2015. "Bayesian Model Averaging and Jointness Measures for gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i05).
    10. Man, Georg, 2015. "Competition and the growth of nations: International evidence from Bayesian model averaging," Economic Modelling, Elsevier, vol. 51(C), pages 491-501.
    11. Poudineh, Rahmatallah & Jamasb, Tooraj, 2016. "Determinants of investment under incentive regulation: The case of the Norwegian electricity distribution networks," Energy Economics, Elsevier, vol. 53(C), pages 193-202.
    12. Boonman, Tjeerd M., 2023. "Portfolio capital flows before and after the Global Financial Crisis," Economic Modelling, Elsevier, vol. 127(C).
    13. Yin‐Wong Cheung & Shi He, 2022. "RMB misalignment: What does a meta‐analysis tell us?," Review of International Economics, Wiley Blackwell, vol. 30(4), pages 1038-1086, September.
    14. Zeugner, Stefan & Feldkircher, Martin, 2015. "Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i04).
    15. María Victoria Landaberry & Kenji Nakasone & Johann Pérez & María del Pilar Posada, 2022. "A predictive model of sovereign investment grade using machine learning and natural language processing," Documentos de trabajo 2022005, Banco Central del Uruguay.
    16. Shahram Amini & Christopher F. Parmeter, 2020. "A Review of the ‘BMS’ Package for R with Focus on Jointness," Econometrics, MDPI, vol. 8(1), pages 1-21, February.
    17. Boonman, Tjeerd, 2023. "Have drivers of portfolio capital flows changed since the Global Financial Crisis?," MPRA Paper 116507, University Library of Munich, Germany.
    18. repec:cam:camdae:1324 is not listed on IDEAS

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    More about this item

    Keywords

    Model Averaging; Zellner's g Prior; BMS;
    All these keywords.

    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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