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

Forecasting with Mixed Frequency Samples: The Case of Common Trends

  • Peter Fuleky


    (UHERO and Department of Economics, University of Hawaii at Manoa)

  • Carl S. Bonham


    (Department of Economics, University of Hawaii at Manoa)

We analyze the forecasting performance of small mixed frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high frequency information is passed to low frequency series through the common factors. Differencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the transfer of information among indicators. We find that allowing for common trends improves forecasting performance over a stationary factor model based on differenced data. The "common-trends factor model" outperforms the stationary factor model at all analyzed forecast horizons. Our results demonstrate that when mixed frequency vari- ables are cointegrated, modeling common stochastic trends improves forecasts.

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:
File Function: First version, 2013
Download Restriction: no

Paper provided by University of Hawaii at Manoa, Department of Economics in its series Working Papers with number 201316.

in new window

Length: 44 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:hai:wpaper:201316
Contact details of provider: Postal: 2424 Maile Way, Honolulu, HI 96822
Phone: (808)956-8730
Fax: (808)956-4347
Web page:

More information through EDIRC

Order Information: Web: Email:

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. Milton Friedman, 1962. "Introduction to "The Interpolation of Time Series by Related Series"," NBER Chapters, in: The Interpolation of Time Series by Related Series, pages 1-3 National Bureau of Economic Research, Inc.
  2. Francis X. Diebold & Peter F. Christoffersen, 1997. "Cointegration and Long-Horizon Forecasting," IMF Working Papers 97/61, International Monetary Fund.
  3. Evans, Martin D, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," MPRA Paper 831, University Library of Munich, Germany.
  4. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  5. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, 04.
  6. Siem Jan Koopman & Andr� Lucas, 2003. "Business and Default Cycles for Credit Risk," Tinbergen Institute Discussion Papers 03-062/2, Tinbergen Institute, revised 09 Jan 2003.
  7. Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper 1998-141, Tilburg University, Center for Economic Research.
  8. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
  9. Milton Friedman, 1962. "The Interpolation of Time Series by Related Series," NBER Books, National Bureau of Economic Research, Inc, number frie62-1, July.
  10. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
  11. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
  12. Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non-linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300.
  13. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
  14. Valle e Azevedo, Joao & Koopman, Siem Jan & Rua, Antonio, 2006. "Tracking the Business Cycle of the Euro Area: A Multivariate Model-Based Bandpass Filter," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 278-290, July.
  15. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 0633, European Central Bank.
  16. Palm, F.C. & Nijman, Th., 1982. "Missing observations in the dynamic regression model," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  17. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
  18. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
  19. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  20. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-36, January.
  21. Byeongchan Seong & Sung K. Ahn & Peter A. Zadrozny, 2013. "Estimation of vector error correction models with mixed-frequency data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 194-205, 03.
  22. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
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:hai:wpaper:201316. 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: (Web Technician)

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.