Advanced Search
MyIDEAS: Login to save this paper or follow this series

Priors about Observables in Vector Autoregressions

Contents:

Author Info

  • Marek Jarocinski

    ()

  • Albert Marcet

Abstract

Standard practice in Bayesian VARs is to formulate priors on the autoregres- sive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how this kind of prior can be used in a VAR under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a very large number of parameters. We prove various convergence theorems for the algorithm. As an application, we first show that the results in Christiano et al. (1999) are very sensitive to the introduction of various priors that are widely used. These priors turn out to be associated with undesirable priors on observables. But an empirical prior on observables helps clarify the relevance of these estimates: we find much higher persistence of out- put responses to monetary policy shocks than the one reported in Christiano et al. (1999) and a significantly larger total effect.

Download Info

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: http://pareto.uab.es/wp/2013/92913.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 929.13.

as in new window
Length: 36
Date of creation: 18 Mar 2013
Date of revision:
Handle: RePEc:aub:autbar:929.13

Contact details of provider:
Postal: 08193, Bellaterra, Barcelona
Phone: 34 93 592 1203
Fax: +34 93 542-1223
Email:
Web page: http://pareto.uab.cat
More information through EDIRC

Related research

Keywords: Vector Autoregression; Bayesian Estimation; Prior about Observables; Inverse Problem; Monetary Policy Shocks;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

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. Mathias Trabandt & Karl Walentin & Lawrence J. Christiano, 2008. "Introducing Financial Frictions and Unemployment into a Small Open Economy Model," 2008 Meeting Papers 423, Society for Economic Dynamics.
  2. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  3. Adjemian, Stéphane & Bastani, Houtan & Karamé, Fréderic & Juillard, Michel & Maih, Junior & Mihoubi, Ferhat & Perendia, George & Pfeifer, Johannes & Ratto, Marco & Villemot, Sébastien, 2011. "Dynare: Reference Manual Version 4," Dynare Working Papers 1, CEPREMAP, revised Jul 2014.
  4. Stéphane Bonhomme & Jean-Marc Robin, 2008. "Generalized nonparametric deconvolution with an application to earnings dynamics," CeMMAP working papers CWP03/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
  6. Ivan Canay & Andres Santos & Azeem Shaikh, 2012. "On the testability of identification in some nonparametric models with endogeneity," CeMMAP working papers CWP18/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
  8. Kadane, Joseph B. & Chan, Ngai Hang & Wolfson, Lara J., 1996. "Priors for unit root models," Journal of Econometrics, Elsevier, vol. 75(1), pages 99-111, November.
  9. Marek Jarocinski & Albert Marcet, 2011. "Autoregressions in Small Samples, Priors about Observables and Initial Conditions," CEP Discussion Papers dp1061, Centre for Economic Performance, LSE.
  10. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
  11. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.
Full references (including those not matched with items on IDEAS)

Citations

Lists

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

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:aub:autbar:929.13. 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: (Xavier Vila).

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.