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Bayesian Model Averaging for Autoregressive Distributed Lag (BMA_ADL) in gretl

Author

Listed:
  • Blazejowski, Marcin
  • Kwiatkowski, Jacek

Abstract

This paper presents a software package that implements Bayesian Model Averaging for Autoregressive Distributed Lag models BMA_ADL ver.~0.9 in gretl. Gretl (the GNU regression, econometrics and time-series library) is an increasingly popular free, open-source software for econometric analysis with an easy-to-use graphical user interface. Bayesian Model Averaging (BMA) incorporates model uncertainty into conclusions about the estimated parameters. It is an efficient tool for discovering the most likely models and variables by obtaining estimates of their posterior characteristics.

Suggested Citation

  • Blazejowski, Marcin & Kwiatkowski, Jacek, 2020. "Bayesian Model Averaging for Autoregressive Distributed Lag (BMA_ADL) in gretl," MPRA Paper 98387, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:98387
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    References listed on IDEAS

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    1. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
    2. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    3. Gernot Doppelhofer & Melvyn Weeks, 2009. "Jointness of growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 209-244, March.
    4. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    5. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    6. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    7. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    8. Ley, Eduardo & Steel, Mark F.J., 2007. "Jointness in Bayesian variable selection with applications to growth regression," Journal of Macroeconomics, Elsevier, vol. 29(3), pages 476-493, September.
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    Cited by:

    1. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.

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    Keywords

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    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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