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Forecasting Mixed Frequency Time Series with ECM-MIDAS Models

  • Götz Thomas
  • Hecq Alain
  • Urbain Jean-Pierre

    (METEOR)

This paper proposes a mixed-frequency error-correction model in order to develop a regressionapproach for non-stationary variables sampled at different frequencies that are possiblycointegrated. We show that, at the model representation level, the choice of the timing betweenthe low-frequency ependent and the high-frequency explanatory variables to be included in thelong-run has an impact on the remaining dynamics and on the forecasting properties. Then, wecompare in a set of Monte Carlo experiments the forecasting performances of the low-frequencyaggregated model and several mixed-frequency regressions. In particular, we look at both theunrestricted mixed-frequency model and at a more parsimonious MIDAS regression. Whilst theexisting literature has only investigated the potential improvements of the MIDAS framework forstationary time series, our study emphasizes the need to include the relevant cointegratingvectors in the non-stationary case. Furthermore, it is illustrated that the exact timing of thelong-run relationship does notmatter as long as the short-run dynamics are adapted according to the composition of thedisequilibrium error. Finally, the unrestricted model is shown to suffer from parameterproliferation for small sample sizeswhereas MIDAS forecasts are robust to over-parameterization. Hence, the data-driven,low-dimensional and flexible weighting structure makes MIDAS a robust and parsimonious method tofollow when the true underlying DGP is unknown while still exploiting information present in thehigh-frequency. An empirical application illustrates the theoretical and the Monte Carlo results.

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File URL: http://digitalarchive.maastrichtuniversity.nl/fedora/objects/guid:c3d501be-442c-43cd-8757-6126cfb565b5/datastreams/ASSET1/content
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Paper provided by Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR) in its series Research Memorandum with number 012.

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Date of creation: 2012
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Handle: RePEc:unm:umamet:2012012
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  1. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  2. Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: A survey," Temi di discussione (Economic working papers) 685, Bank of Italy, Economic Research and International Relations Area.
  3. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank, Research Centre.
  4. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  5. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, November.
  6. 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.
  7. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
  8. J. Isaac Miller, 2011. "Cointegrating MiDaS Regressions and a MiDaS Test," Working Papers 1104, Department of Economics, University of Missouri.
  9. Pons, Gabriel & Sans , Andreu, 2005. "Estimation Of Cointegrating Vectors With Time Series Measured At Different Periodicity," Econometric Theory, Cambridge University Press, vol. 21(04), pages 735-756, August.
  10. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
  11. 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.
  12. J. Isaac Miller, 2011. "Conditionally Efficient Estimation of Long-run Relationships Using Mixed-frequency Time Series," Working Papers 1103, Department of Economics, University of Missouri, revised 30 May 2012.
  13. 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.
  14. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
  15. 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.
  16. 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.
  17. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
  18. Chambers, Marcus J., 2003. "The Asymptotic Efficiency Of Cointegration Estimators Under Temporal Aggregation," Econometric Theory, Cambridge University Press, vol. 19(01), pages 49-77, February.
  19. 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.
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