IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Are spectral estimators useful for long-run restrictions in SVARs?

  • Mertens, Elmar

No, not really. In response to concerns about the reliability of SVARs, one proposal has been to combine OLS estimates of a VAR with non-parametric estimates of the spectral density. But as shown here, spectral estimators are no panacea for implementing long-run restrictions. They can suffer from small sample and misspecification biases just as VARs do. As a novelty, this paper uses a spectral factorization to ensure a correct representation of the data's variance. But this cannot overcome the basic small sample issues, which arise when trying to estimate long-run properties from relatively short samples of time-series data.

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: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 36 (2012)
Issue (Month): 12 ()
Pages: 1831-1844

in new window

Handle: RePEc:eee:dyncon:v:36:y:2012:i:12:p:1831-1844
DOI: 10.1016/j.jedc.2012.06.007
Contact details of provider: Web page:

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. Peter N. Ireland, 2004. "Technology Shocks in the New Keynesian Model," NBER Working Papers 10309, National Bureau of Economic Research, Inc.
  2. Timothy Cogley & James M. Nason, 1993. "Output dynamics in real business cycle models," Working Papers in Applied Economic Theory 93-10, Federal Reserve Bank of San Francisco.
  3. Federico Ravenna, 2005. "Vector Autoregressions and Reduced Form Representations of DSGE Models," 2005 Meeting Papers 841, Society for Economic Dynamics.
  4. Smets, Frank & Wouters, Rafael, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," CEPR Discussion Papers 6112, C.E.P.R. Discussion Papers.
  5. V V Chari & Patrick J Kehoe & Ellen R. McGrattan, 2003. "Business Cycle Accounting," Levine's Bibliography 506439000000000421, UCLA Department of Economics.
  6. Christian Kascha & Karel Mertens, 2008. "Business cycle analysis and VARMA models," Working Paper 2008/05, Norges Bank.
  7. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2008. "Are Structural VARs with Long-Run Restrictions Useful in Developing Business Cycle Theory?," NBER Working Papers 14430, National Bureau of Economic Research, Inc.
  8. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
  9. Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Thomas J. Sargent, 2005. "A, B, C's (and D)'s for Understanding VARs," NBER Technical Working Papers 0308, National Bureau of Economic Research, Inc.
  10. Dupaigne, Martial & Fève, Patrick, 2005. "Technology Shocks around the World," IDEI Working Papers 346, Institut d'Économie Industrielle (IDEI), Toulouse.
  11. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2004. "The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology," NBER Working Papers 10254, National Bureau of Economic Research, Inc.
  12. Olivier Jean Blanchard & Danny Quah, 1988. "The Dynamic Effects of Aggregate Demand and Supply Disturbance," Working papers 497, Massachusetts Institute of Technology (MIT), Department of Economics.
  13. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2005. "A critique of structural VARs using real business cycle theory," Working Papers 631, Federal Reserve Bank of Minneapolis.
  14. Newey, W.K. & West, K.D., 1992. "Automatic Lag Selection in Covariance Matrix Estimation," Working papers 9220, Wisconsin Madison - Social Systems.
  15. Lawrence J. Christiano & Martin Eichenbaum & Robert J. Vigfusson, 2005. "Alternative procedures for estimating vector autoregressions identified with long-run restrictions," International Finance Discussion Papers 842, Board of Governors of the Federal Reserve System (U.S.).
  16. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
  17. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
  18. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  19. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-66, July.
  20. Lawrence J. Christiano & Martin Eichenbaum & Robert J. Vigfusson, 2006. "Assessing structural VARs," International Finance Discussion Papers 866, Board of Governors of the Federal Reserve System (U.S.).
    • Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106 National Bureau of Economic Research, Inc.
  21. Peter C. B. Phillips & Yixiao Sun & Sainan Jin, 2006. "Spectral Density Estimation And Robust Hypothesis Testing Using Steep Origin Kernels Without Truncation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(3), pages 837-894, 08.
  22. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  23. Elmar Mertens, 2010. "Are spectral estimators useful for implementing long-run restrictions in SVARs?," Finance and Economics Discussion Series 2010-09, Board of Governors of the Federal Reserve System (U.S.).
  24. Yixiao Sun & Peter C. B. Phillips & Sainan Jin, 2006. "Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing," Cowles Foundation Discussion Papers 1545, Cowles Foundation for Research in Economics, Yale University.
  25. Martial Dupaigne & Patrick Feve, 2008. "Online Appendix to "Technology shocks around the world"," Technical Appendices 08-23, Review of Economic Dynamics.
  26. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, September.
  27. Lars Peter Hansen & Thomas J. Sargent, 2007. "Introduction to Robustness," Introductory Chapters, in: Robustness Princeton University Press.
  28. Li, Lei M., 2005. "Factorization of moving-average spectral densities by state-space representations and stacking," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 425-438, October.
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:eee:dyncon:v:36:y:2012:i:12:p:1831-1844. 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: (Shamier, Wendy)

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.