IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

The Importance Of Common Cyclical Features in VAR Analysis: A Monte-Carlo Study

Author Info

  • Vahid, F.

    ()

  • Issler, J.V.

Abstract

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models.

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://www.buseco.monash.edu.au/ebs/pubs/wpapers/2001/wp2-01.pdf
Download Restriction: no

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 2/01.

as
in new window

Length: 20 pages
Date of creation: Mar 2001
Date of revision:
Handle: RePEc:msh:ebswps:2001-2
Contact details of provider: Postal:
PO Box 11E, Monash University, Victoria 3800, Australia

Phone: +61 3 99052489
Fax: +61 3 99055474
Web page: http://business.monash.edu/econometrics-and-business-statistics
Email:


More information through EDIRC

Order Information: Web: http://business.monash.edu/econometrics-and-business-statistics Email:


Keywords: Reduced rank models; model selection criteria; forecasting; variance decomposition;

Other versions of this item:

Find related papers by JEL classification:
  • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
  • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

This paper has been announced in the following NEP Reports:

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. Gerald A. Carlino & Keith Sill, 2000. "Regional income fluctuations: common trends and common cycles," Working Papers 00-8, Federal Reserve Bank of Philadelphia.
  2. Clements, M.P. & Hendry, D.F., 1992. "Forecasting in Cointegrated Systems," Economics Series Working Papers 99139, University of Oxford, Department of Economics.
  3. Vahid, F. & Issler, J.V., 2001. "The Importance Of Common Cyclical Features in VAR Analysis: A Monte-Carlo Study," Monash Econometrics and Business Statistics Working Papers 2/01, Monash University, Department of Econometrics and Business Statistics.
  4. Zellner, Arnold & Hong, Chansik, 1989. "Forecasting international growth rates using Bayesian shrinkage and other procedures," Journal of Econometrics, Elsevier, vol. 40(1), pages 183-202, January.
  5. repec:fgv:epgrbe:v:47:n:2:a:1 is not listed on IDEAS
  6. Gerald A. Carlino & Keith Sill, 1996. "Common trends and common cycles in regional per capita incomes," Working Papers 96-13, Federal Reserve Bank of Philadelphia.
  7. John Y. Campbell & N. Gregory Mankiw, 1989. "Consumption, Income and Interest Rates: Reinterpreting the Time Series Evidence," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 185-246 National Bureau of Economic Research, Inc.
  8. Lucas, Robert E., 1977. "Understanding business cycles," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 5(1), pages 7-29, January.
  9. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : II. New directions," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 309-341.
  10. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
  11. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-95, October.
  12. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
  13. Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.
  14. repec:fth:harver:1435 is not listed on IDEAS
  15. Robert F. Engle & Sharon Kozicki, 1990. "Testing For Common Features," NBER Technical Working Papers 0091, National Bureau of Economic Research, Inc.
  16. Robert G. King & Charles I. Plosser & James H. Stock & Mark W. Watson, 1987. "Stochastic Trends and Economic Fluctuations," NBER Working Papers 2229, National Bureau of Economic Research, Inc.
  17. Engle, Robert F. & Issler, Joao Victor, 1995. "Estimating common sectoral cycles," Journal of Monetary Economics, Elsevier, vol. 35(1), pages 83-113, February.
  18. Vahid, F & Engle, Robert F, 1993. "Common Trends and Common Cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 341-60, Oct.-Dec..
  19. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  20. Issler, Joao Victor & Vahid, Farshid, 2001. "Common cycles and the importance of transitory shocks to macroeconomic aggregates," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 449-475, June.
  21. Issler, João Victor & Ferreira, Pedro Cavalcanti, 1998. "Time-series properties and empirical evidence of growth and infraestructure: revised version," Economics Working Papers (Ensaios Economicos da EPGE) 336, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
Full references (including those not matched with items on IDEAS)

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

as in new window


Cited by:
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.

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

Access and download statistics

When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:2001-2. 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: (Dr Xibin Zhang)

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.