IDEAS home Printed from https://ideas.repec.org/p/anc/wgretl/2.html
   My bibliography  Save this paper

Bayesian Model Averaging and Jointness Measures for gretl

Author

Listed:
  • Marcin Blazejowski

    () (Torun School of Banking, Department of Quantitative Methods)

  • Jacek Kwiatkowski

    () (Nicolaus Copernicus University in Torun, Faculty of Economics and Management)

Abstract

This paper presents a software package that implements Bayesian model averaging for gretl, the GNU regression, econometrics and time-series library. Bayesian model averaging is a model-building strategy that takes account of model uncertainty in conclusions about estimated parameters. It is an efficient tool for discovering the most probable models and obtaining estimates of their posterior characteristics. In recent years we have observed an increasing number of software packages devoted to Bayesian model averaging for different statistical and econometric software. In this paper, we propose the BMA package for gretl, which is an increasingly popular free, open-source software for econometric analysis with an easy-to-use graphical user interface. We introduce the BMA package for linear regression models with jointness measures proposed by Ley and Steel (2007) and Doppelhofer and Weeks (2009).

Suggested Citation

  • Marcin Blazejowski & Jacek Kwiatkowski, 2015. "Bayesian Model Averaging and Jointness Measures for gretl," gretl working papers 2, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  • Handle: RePEc:anc:wgretl:2
    as

    Download full text from publisher

    File URL: http://docs.dises.univpm.it/web/quaderni/pdfgretl/gretl002.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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).
    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. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    6. Yalta, A. Talha & Schreiber, Sven, 2012. "Random Number Generation in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(c01).
    7. De Luca, Giuseppe & Magnus, Jan R., 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 0(Number 4), pages 1-29.
    8. Jesús Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2014. "The Determinants of Economic Growth in European Regions," Regional Studies, Taylor & Francis Journals, vol. 48(1), pages 44-67, January.
    9. 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.
    10. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    11. Shahram Amini & Christopher F. Parmeter, 2011. "Bayesian Model Averaging in R," Working Papers 2011-9, University of Miami, Department of Economics.
    12. Giovanni Baiocchi & Walter Distaso, 2003. "GRETL: Econometric software for the GNU generation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 105-110.
    13. Alex Lenkoski & Theo Eicher & Adrian Raftery, 2014. "Two-Stage Bayesian Model Averaging in Endogenous Variable Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 122-151.
    14. Baran, Sándor, 2014. "Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 227-238.
    15. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Equity Premia using Bayesian Dynamic Model Averaging," CQE Working Papers 2914, Center for Quantitative Economics (CQE), University of Muenster.
    16. Lucchetti, Riccardo, 2011. "State Space Methods in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i11).
    17. Lee C. Adkins, 2011. "Using gretl for Monte Carlo experiments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 880-885, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Błażejowski, Marcin & Gazda, Jakub & Kwiatkowski, Jacek, 2016. "Bayesian Model Averaging in the Studies on Economic Growth in the EU Regions – Application of the gretl BMA package," MPRA Paper 89366, University Library of Munich, Germany, revised Oct 2016.
    2. repec:exl:29stat:v:18:y:2017:i:3:p:393-412 is not listed on IDEAS

    More about this item

    Keywords

    Bayesian model averaging; jointness measures; gretl; Hansl;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anc:wgretl:2. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Maurizio Mariotti). General contact details of provider: http://edirc.repec.org/data/deancit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.