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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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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. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
  2. Carmen Fernandez & Eduardo Ley & Mark F.J. Steel, 1998. "Benchmark Priors for Bayesian Model Averaging," Econometrics 9804001, EconWPA, revised 31 Jul 1999.
  3. 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.
  4. 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.
  5. Enrique Moral-Benito, 2010. "Model Averaging In Economics," Working Papers wp2010_1008, CEMFI.
  6. 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.
  7. 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.
  8. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  9. Gernot Doppelhofer & Melvyn Weeks, 2009. "Jointness of growth determinants," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 209-244, 03.
  10. Jesus Crespo-Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2009. "The Determinants of Economic Growth in European Regions," wiiw Working Papers 57, The Vienna Institute for International Economic Studies, wiiw.
  11. Shahram Amini & Christopher F. Parmeter, 2011. "Bayesian Model Averaging in R," Working Papers 2011-9, University of Miami, Department of Economics.
  12. 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.
  13. Lucchetti, Riccardo, 2011. "State Space Methods in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i11).
  14. Yalta, A. Talha & Schreiber, Sven, 2012. "Random Number Generation in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(c01).
  15. 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.
  16. 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.
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