IDEAS home Printed from
MyIDEAS: Login to save this paper or follow this series

Methods for Computing Marginal Data Densities from the Gibbs Output

  • Cristina Fuentes-Albero


    (Rutgers, The State University of New Jersey)

  • Leonardo Melosi

    (London Business School)

We introduce two new methods for estimating the Marginal Data Density (MDD) from the Gibbs output, which are based on exploiting the analytical tractability condition. Such a condition requires that some parameter blocks can be analytically integrated out from the conditional posterior densities. Our estimators are applicable to densely parameterized time series models such as VARs or DFMs. An empirical application to six-variate VAR models shows that the bias of a fully computational estimator is sufficiently large to distort the implied model rankings. One estimator is fast enough to make multiple computations of MDDs in densely parameterized models feasible.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201131.

in new window

Length: 20 pages
Date of creation: 17 Oct 2011
Date of revision:
Handle: RePEc:rut:rutres:201131
Contact details of provider: Postal: New Jersey Hall - 75 Hamilton Street, New Brunswick, NJ 08901-1248
Phone: (732) 932-7363
Fax: (732) 932-7416
Web page:

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
  2. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
  3. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
  4. Korobilis, Dimitris, 2008. "Forecasting in vector autoregressions with many predictors," MPRA Paper 21122, University Library of Munich, Germany.
  5. Domenico Giannone & Michèle Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," Working Papers ECARES ECARES 2012-002, ULB -- Universite Libre de Bruxelles.
  6. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
  7. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
  8. M. Ayhan Kose & Christopher Otrok & Charles H. Whiteman, 2003. "International Business Cycles: World, Region, and Country-Specific Factors," American Economic Review, American Economic Association, vol. 93(4), pages 1216-1239, September.
  9. Chiara Scotti & S.Boragan Aruoba & Francis X. Diebold & University of Maryland, 2006. "Real-Time Measurement of Business Conditions," Computing in Economics and Finance 2006 387, Society for Computational Economics.
  10. Gary Koop, 2010. "Forecasting with Medium and Large Bayesian VARs," Working Paper Series 43_10, The Rimini Centre for Economic Analysis.
  11. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," FRB Atlanta Working Paper 96-13, Federal Reserve Bank of Atlanta.
  12. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  13. Christopher A. Sims & Daniel F. Waggoner & Tao Zha, 2006. "Methods for inference in large multiple-equation Markov-switching models," FRB Atlanta Working Paper 2006-22, Federal Reserve Bank of Atlanta.
  14. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  15. Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December.
  16. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  17. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, June.
  18. Koop, G. & Strachan, R.W. & van Dijk, H.K. & Villani, M., 2005. "Bayesian approaches to cointegratrion," Econometric Institute Research Papers EI 2005-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  19. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
  20. John Geweke & Guofu Zhou, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," CEMA Working Papers 276, China Economics and Management Academy, Central University of Finance and Economics.
  21. Fiorentini, G. & Planas, C. & Rossi, A., 2012. "The marginal likelihood of dynamic mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2650-2662.
  22. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," Working Papers 662, Queen Mary University of London, School of Economics and Finance.
  23. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  24. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
  25. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
  26. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, 05.
  27. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
  28. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, June.
  29. Timothy Cogley & Thomas Sargent, . "Evolving Post-World War II U.S. Inflation Dynamics," Working Papers 2132872, Department of Economics, W. P. Carey School of Business, Arizona State University.
  30. Christopher Otrok & Charles H. Whiteman, 1996. "Baynesian Leading Indicators: Measuring and Predicting Economic Conditions," Macroeconomics 9610002, EconWPA.
  31. repec:oup:restud:v:72:y:2005:i:3:p:821-852 is not listed on IDEAS
  32. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?," CEPR Discussion Papers 3146, C.E.P.R. Discussion Papers.
  33. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:rut:rutres:201131. 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: ()

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.