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

Changes in Predictive Ability with Mixed Frequency Data

  • Ana Beatriz Galvão

    (Queen Mary, University of London)

This paper proposes a new regression model - a smooth transition mixed data sampling (STMIDAS) approach - that captures recurrent changes in the ability of a high frequency variable in predicting a low frequency variable. The STMIDAS regression is employed for testing changes in the ability of financial variables in forecasting US output growth. The estimation of the optimal weights for aggregating weekly data inside the quarter improves the measurement of the predictive ability of the yield curve slope for output growth. Allowing for changes in the impact of the short-rate and the stock returns in future growth is decisive for finding in-sample and out-of-sample evidence of their predictive ability at horizons longer than one year.

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.econ.qmul.ac.uk/papers/doc/wp595.pdf
Download Restriction: no

Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 595.

as
in new window

Length:
Date of creation: May 2007
Date of revision:
Handle: RePEc:qmw:qmwecw:wp595
Contact details of provider: Postal:
London E1 4NS

Phone: +44 (0) 20 7882 5096
Fax: +44 (0) 20 8983 3580
Web page: http://www.econ.qmul.ac.uk

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. Andrew Ang & Monika Piazzesi & Min Wei, 2003. "What does the yield curve tell us about GDP growth?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  2. Ralf Becker & Denise R. Osborn, 2012. "Weighted Smooth Transition Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 795-811, 08.
  3. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 0214, European Central Bank.
  4. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  5. Jon Faust & John H. Rogers & Jonathan H. Wright, 2001. "Exchange rate forecasting: the errors we've really made," International Finance Discussion Papers 714, Board of Governors of the Federal Reserve System (U.S.).
  6. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-86.
  7. Rossi, Barbara & Giacomini, Raffaella, 2005. "How Stable is the Forecasting Performance of the Yield Curve for Outpot Growth?," Working Papers 05-08, Duke University, Department of Economics.
  8. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  9. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
  10. Carrasco, Marine, 2002. "Misspecified Structural Change, Threshold, and Markov-switching models," Journal of Econometrics, Elsevier, vol. 109(2), pages 239-273, August.
  11. Orphanides, Athanasios & van Norden, Simon, 2005. "The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 583-601, June.
  12. Inoue, Atsushi & Kilian, Lutz, 2002. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," CEPR Discussion Papers 3671, C.E.P.R. Discussion Papers.
  13. Andrew Ang & Geert Bekaert, 1998. "Regime Switches in Interest Rates," NBER Working Papers 6508, National Bureau of Economic Research, Inc.
  14. Zellner, Arnold & Hong, Chansik & Min, Chung-ki, 1991. "Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 275-304.
  15. Arturo Estrella & Frederic S. Mishkin, 1995. "Predicting U.S. Recessions: Financial Variables as Leading Indicators," NBER Working Papers 5379, National Bureau of Economic Research, Inc.
  16. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
  17. Allan Timmermann & M. Hashem Pesaran, 2003. "Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks," CESifo Working Paper Series 990, CESifo Group Munich.
  18. Anderson, Heather M. & Vahid, Farshid, 2001. "Predicting The Probability Of A Recession With Nonlinear Autoregressive Leading-Indicator Models," Macroeconomic Dynamics, Cambridge University Press, vol. 5(04), pages 482-505, September.
  19. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  20. Tatevik Sekhposyan & Barbara Rossi, 2009. "Has Economic Modelsí Forecasting Performance for US Output Growth and Inflation Changed Over Time, and When?," Working Papers 09-06, Duke University, Department of Economics.
  21. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  22. Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-76, June.
  23. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier.
  24. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
  25. Arturo Estrella & Anthony P. Rodrigues & Sebastian Schich, 2003. "How Stable is the Predictive Power of the Yield Curve? Evidence from Germany and the United States," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 629-644, August.
  26. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  27. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
  28. Hamilton, James Douglas & Kim, Dong Heon, 2000. "A Re-examination of the Predictability of Economic Activity Using the Yield Spread," University of California at San Diego, Economics Working Paper Series qt69v8p1m9, Department of Economics, UC San Diego.
  29. Galbraith, John W. & Tkacz, Greg, 2000. "Testing for asymmetry in the link between the yield spread and output in the G-7 countries," Journal of International Money and Finance, Elsevier, vol. 19(5), pages 657-672, October.
  30. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," NBER Working Papers 10913, National Bureau of Economic Research, Inc.
  31. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 521-536.
  32. Birgit Strikholm & Timo Teräsvirta, 2006. "A sequential procedure for determining the number of regimes in a threshold autoregressive model," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 472-491, November.
  33. Strikholm, Birgit & Teräsvirta, Timo, 2005. "Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions," SSE/EFI Working Paper Series in Economics and Finance 578, Stockholm School of Economics, revised 11 Feb 2005.
  34. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  35. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
  36. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," SSE/EFI Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
  37. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Paper 1120, Federal Reserve Bank of Cleveland.
  38. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
  39. Tatevik Sekhposyan & Barbara Rossi, 2008. "Has modelsí forecasting performance for US output growth and inflation changed over time, and when?," Working Papers 09-02, Duke University, Department of Economics.
  40. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2007. "Regression Models with Mixed Sampling Frequencies," University of Cyprus Working Papers in Economics 8-2007, University of Cyprus Department of Economics.
  41. 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.
  42. Ana Beatriz C. Galvao, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487.
  43. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  44. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  45. Terasvirta, Timo & Tjostheim, Dag & Granger, Clive W. J., 2010. "Modelling Nonlinear Economic Time Series," OUP Catalogue, Oxford University Press, number 9780199587155, December.
  46. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.
  47. Heather M. Anderson & George Athanasopoulos & Farshid Vahid, 2002. "Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries," Monash Econometrics and Business Statistics Working Papers 20/02, Monash University, Department of Econometrics and Business Statistics.
  48. Todd Clark & Michael McCracken, 2005. "Evaluating Direct Multistep Forecasts," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 369-404.
  49. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
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:qmw:qmwecw:wp595. 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: (Nick Vriend)

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.