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A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables

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  • Claudia FORONI
  • Massimiliano MARCELLINO

Abstract

Forecast models that take into account unbalanced datasets have recently attracted substantial attention. In this paper, we focus on different methods pro- posed so far in the literature to deal with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency (MF) models. We discuss their performance on now- and forecasting the quarterly growth rate of Euro area GDP and its components, using a very large set of monthly indicators taken from Eurostat dataset of Principal European Economic Indicators (PEEI). We both investigate the behavior of single indicator models and combine first the forecasts within each class of models and then the information in the dataset by means of factor models, in a pseudo real-time framework. Anticipating some of the results, MIDAS without an AR component performs worse than the corresponding approach which incorporates it, and MF-VAR seems to outperform the MIDAS approach only at longer horizons. Bridge equations have overall a good performance. Pooling many indicators within each class of models is overall superior to most of the single indicator models. Pooling information with the use of factor models gives even better results, at least at short horizons. A battery of robustness checks high- lights the importance of monthly information during the crisis more than in stable periods. Extending the analysis to a real-time context highlights that revisions do not influence substantially the results.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2012/07.

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Date of creation: 2012
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Handle: RePEc:eui:euiwps:eco2012/07

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Keywords: mixed-frequency data; mixed-frequency VAR; MIDAS; factor models; nowcasting; forecasting;

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References

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  1. Mario Forni & Filippo Altissimo & Riccardo Cristadoro & Marco Lippi & Giovanni Veronese., 2008. "New Eurocoin: Tracking Economic Growth in Real Time," Center for Economic Research (RECent), University of Modena and Reggio E., Dept. of Economics 020, University of Modena and Reggio E., Dept. of Economics.
  2. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, Elsevier, vol. 131(1-2), pages 59-95.
  3. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: An application to German GDP," CEPR Discussion Papers 7197, C.E.P.R. Discussion Papers.
  4. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank, Research Centre.
  5. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 529-542.
  6. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, Elsevier, vol. 164(1), pages 188-205, September.
  7. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier, Elsevier.
  8. 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.
  9. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, Elsevier, vol. 20(3), pages 447-460.
  10. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(3), pages 386-398.
  11. Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April.
  12. 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., John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  13. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
  14. 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.
  15. Michael P. Clements & Ana Beatriz Galvão, 2013. "Real‐Time Forecasting Of Inflation And Output Growth With Autoregressive Models In The Presence Of Data Revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 458-477, 04.
  16. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 546-554.
  17. Diron, Marie, 2006. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Working Paper Series 0622, European Central Bank.
  18. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, Elsevier, vol. 135(1-2), pages 31-53.
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Citations

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Cited by:
  1. Marcellino, Massimiliano & Porqueddu, Mario & Venditti, Fabrizio, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," CEPR Discussion Papers 9334, C.E.P.R. Discussion Papers.
  2. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers, European University Institute ECO2013/02, European University Institute.
  3. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
  4. 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.

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