IDEAS home Printed from https://ideas.repec.org/p/anc/wgretl/9.html
   My bibliography  Save this paper

BACE: A gretl Package for Model Averaging in Limited Dependent Variable Models

Author

Listed:
  • Marcin Blazejowski

    (WSB University in Toruń)

  • Jacek Kwiatkowski

    (Nicolaus Copernicus University in Toru´n)

Abstract

This paper presents a software package called BACE (BayesianAveraging of Classical Estimates) which offers model-building strategy for various limited dependent variable models, including logit and probit models, ordered logit and probit models, multinomial logistic regression, Poisson regression, Tobit model, and interval regression. BACE strategy is a model selection method that incorporates both classical estimation and Bayesian techniques. It solves the problem of computation speed and model uncertainty that arise when dealing with a large number of competing advanced statistical models. Our BACE package is both fast and capable of delivering consistent results. The package also provides implementation of the latest proposals of BIC variants, and the latest measures of jointness. We use gretl, a popular, free, and open-source software for econometric analysis that features an easy-to-use graphical user interface.

Suggested Citation

  • Marcin Blazejowski & Jacek Kwiatkowski, 2023. "BACE: A gretl Package for Model Averaging in Limited Dependent Variable Models," gretl working papers 9, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wgretl:9
    as

    Download full text from publisher

    File URL: http://docs.dises.univpm.it/web/quaderni/pdfgretl/gretl009.pdf
    File Function: First version, 2023
    Download Restriction: no
    ---><---

    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. 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.
    3. Mark B. Stewart, 1983. "On Least Squares Estimation when the Dependent Variable is Grouped," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 50(4), pages 737-753.
    4. 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.
    5. Rodney W. Strachan, 2009. "Comment on ‘Jointness of growth determinants’ by Gernot Doppelhofer and Melvyn Weeks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 245-247, March.
    6. Ram Tripathi & Ramesh Gupta & John Gurland, 1994. "Estimation of parameters in the beta binomial model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 317-331, June.
    7. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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".
    2. 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.
    3. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    4. Hofmarcher, Paul & Crespo Cuaresma, Jesus & Grün, Bettina & Humer, Stefan & Moser, Mathias, 2018. "Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 150-165.
    5. Josef Bajzik & Jan Janku & Simona Malovana & Klara Moravcova & Ngoc Anh Ngo, 2023. "Monetary Policy Has a Long-Lasting Impact on Credit: Evidence from 91 VAR Studies," Working Papers 2023/19, Czech National Bank.
    6. Piotr Dybka & Bartosz Olesiński & Marek Rozkrut & Andrzej Torój, 2023. "Measuring the model uncertainty of shadow economy estimates," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(4), pages 1069-1106, August.
    7. Anna Sokolova, 2023. "Marginal Propensity to Consume and Unemployment: a Meta-analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 813-846, December.
    8. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2022. "Sampling properties of the Bayesian posterior mean with an application to WALS estimation," Journal of Econometrics, Elsevier, vol. 230(2), pages 299-317.
    9. Jesus Crespo Cuaresma & Bettina Grün & Paul Hofmarcher & Stefan Humer & Mathias Moser, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Papers wuwp193, Vienna University of Economics and Business, Department of Economics.
    10. Bajzik, Josef & Havranek, Tomas & Irsova, Zuzana & Novak, Jiri, 2023. "Does Shareholder Activism Create Value? A Meta-Analysis," EconStor Preprints 272232, ZBW - Leibniz Information Centre for Economics.
    11. Beata K. Bierut & Piot Dybka, 2019. "Institutional determinants of export competitiveness among the EU countries: evidence from Bayesian model averaging," KAE Working Papers 2019-043, Warsaw School of Economics, Collegium of Economic Analysis.
    12. Andros Kourtellos & Charalambos G. Tsangarides, 2022. "Robust Correlates of Growth Spells: Do Inequality and Redistribution Matter?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1302-1328, December.
    13. Roman Horvath & Ali Elminejad & Tomas Havranek, 2020. "Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply," Working Papers IES 2020/32, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2020.
    14. Joseph, Andreas & Osbat, Chiara, 2016. "How you export matters: the disassortative structure of international trade," Working Paper Series 1958, European Central Bank.
    15. Tomas Havranek & Dominik Herman & Zuzana Irsova, 2018. "Does Daylight Saving Save Electricity? A Meta-Analysis," The Energy Journal, , vol. 39(2), pages 35-61, March.
    16. Mandon, Pierre & Woldemichael, Martha Tesfaye, 2023. "Has Chinese aid benefited recipient countries? Evidence from a meta-regression analysis," World Development, Elsevier, vol. 166(C).
    17. Bruns, Stephan B. & Ioannidis, John P.A., 2020. "Determinants of economic growth: Different time different answer?," Journal of Macroeconomics, Elsevier, vol. 63(C).
    18. Havranek, Tomas & Irsova, Zuzana & Laslopova, Lubica & Zeynalova, Olesia, 2020. "The Elasticity of Substitution between Skilled and Unskilled Labor: A Meta-Analysis," MetaArXiv 7z2uh, Center for Open Science.
    19. Ali Elminejad & Tomas Havranek & Roman Horvath & Zuzana Irsova, 2023. "Intertemporal Substitution in Labor Supply: A Meta-Analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 1095-1113, December.
    20. Yin-Wong Cheung & Wenhao Wang, 2020. "A Jackknife Model Averaging Analysis of RMB Misalignment Estimates," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-45, June.

    More about this item

    Keywords

    BMA; model selection; BIC; gretl; Hansl;
    All these keywords.

    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:9. See general information about how to correct material in RePEc.

    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 bibliographic 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.

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

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.