IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

A Bayesian DSGE Model with Infinite-Horizon Learning: Do "Mechanical" Sources of Persistence Become Superfluous?

  • Fabio Milani

    ()

    (Department of Economics, University of California-Irvine)

This paper estimates a monetary DSGE model with learning introduced from the primitive assumptions. The model nests infinite-horizon learning and features, such as habit formation in consumption and inflation indexation, that are essential for the model fit under rational expectations. I estimate the DSGE model by Bayesian methods, obtaining estimates of the main learning parameter, the constant gain, jointly with the deep parameters of the economy. The results show that relaxing the assumption of rational expectations in favor of learning may render mechanical sources of persistence superfluous. In particular, learning appears a crucial determinant of inflation inertia.

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://www.economics.uci.edu/files/docs/workingpapers/2006-07/Milani-03.pdf
Download Restriction: no

Paper provided by University of California-Irvine, Department of Economics in its series Working Papers with number 060703.

as
in new window

Length: 19 pages
Date of creation: Dec 2005
Date of revision:
Handle: RePEc:irv:wpaper:060703
Contact details of provider: Postal: Irvine, CA 92697-3125
Phone: (949) 824-5788
Web page: http://www.economics.uci.edu/

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. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper 0107, Federal Reserve Bank of Cleveland.
  2. Raf Wouters & Frank Smets, 2005. "Comparing shocks and frictions in US and euro area business cycles: a Bayesian DSGE Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 161-183.
  3. Bullard, James & Mitra, Kaushik, 2002. "Learning about monetary policy rules," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1105-1129, September.
  4. Milani, Fabio, 2008. "Learning, monetary policy rules, and macroeconomic stability," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3148-3165, October.
  5. Athanasios Orphanides & John Williams, 2004. "Imperfect Knowledge, Inflation Expectations, and Monetary Policy," NBER Chapters, in: The Inflation-Targeting Debate, pages 201-246 National Bureau of Economic Research, Inc.
  6. Preston, Bruce, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," MPRA Paper 830, University Library of Munich, Germany.
  7. Frank Smets & Raf Wouters, 2004. "Forecasting with a Bayesian DSGE Model: An Application to the Euro Area," Journal of Common Market Studies, Wiley Blackwell, vol. 42(4), pages 841-867, November.
  8. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
  9. Athanasios Orphanides & John C. Williams, 2003. "Inflation scares and forecast-based monetary policy," Finance and Economics Discussion Series 2003-41, Board of Governors of the Federal Reserve System (U.S.).
  10. Marc Giannoni & Michael Woodford, 2004. "Optimal Inflation-Targeting Rules," NBER Chapters, in: The Inflation-Targeting Debate, pages 93-172 National Bureau of Economic Research, Inc.
  11. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
  12. Seppo Honkapohja & Kaushik Mitra & George W. Evans, 2011. "Notes on Agents¡¯ Behavioral Rules Under Adaptive Learning and Studies of Monetary Policy," CDMA Working Paper Series 201102, Centre for Dynamic Macroeconomic Analysis.
  13. Preston, Bruce, 2008. "Adaptive learning and the use of forecasts in monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 32(11), pages 3661-3681, November.
  14. Preston, Bruce, 2006. "Adaptive learning, forecast-based instrument rules and monetary policy," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 507-535, April.
  15. Fabio Milani, 2005. "Adaptive Learning and Inflation Persistence," Working Papers 050607, University of California-Irvine, Department of Economics.
  16. Frank Smets & Raf Wouters, 2002. "Monetary policy in an estimated stochastic dynamic general equilibrium model of the Euro area," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
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:irv:wpaper:060703. 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: (Jennifer dos Santos)

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.