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Bayesian Model Averaging and Jointness Measures for gretl

  • Blazejowski, Marcin
  • Kwiatkowski, Jacek

This paper presents a software package that implements Bayesian model averaging for Gnu Regression, Econometrics and Time-series Library - gretl. The Bayesian Model Averaging (BMA) is a model-building strategy that takes account of model uncertainty into 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 package devoted to BMA for different statistical and econometric software. In this paper, we propose BMA package for gretl, which is more and more popular free, open-source software for econometric analysis with easy-to-use GUI. We introduce BMA package for the linear regression models with jointness measures proposed by Ley and Steel (2007) and Doppelhofer and Weeks (2009).

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File URL: https://mpra.ub.uni-muenchen.de/44322/1/MPRA_paper_44322.pdf
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File URL: https://mpra.ub.uni-muenchen.de/61431/8/MPRA_paper_61431.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 44322.

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Date of creation: 10 Feb 2013
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Handle: RePEc:pra:mprapa:44322
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  1. Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998. "Benchmark priors for Bayesian model averaging," ESE Discussion Papers 66, Edinburgh School of Economics, University of Edinburgh.
  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. Jesús Crespo-Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2009. "The Determinants of Economic Growth in European Regions," CESifo Working Paper Series 2519, CESifo Group Munich.
  4. Ley, Eduardo & Steel, Mark F. J., 2006. "Jointness in Bayesian variable selection with applications to growth regression," Policy Research Working Paper Series 4063, The World Bank.
  5. Alex Lenkoski & Theo S. Eicher & Adrian E. Raftery, 2014. "Two-Stage Bayesian Model Averaging in Endogenous Variable Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 122-151, June.
  6. 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.
  7. Enrique Moral-Benito, 2011. "Model averaging in economics," Banco de Espa�a Working Papers 1123, Banco de Espa�a.
  8. Lee C. Adkins, 2011. "Using gretl for Monte Carlo experiments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 880-885, 08.
  9. 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.
  10. Riccardo Lucchetti, . "State Space Methods in gretl," Journal of Statistical Software, American Statistical Association, vol. 41(i11).
  11. Shahram Amini & Christopher F. Parmeter, 2011. "Bayesian Model Averaging in R," Working Papers 2011-9, University of Miami, Department of Economics.
  12. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  13. Gernot Doppelhofer & Melvyn Weeks, 2009. "Jointness of growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 209-244, 03.
  14. 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.
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