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FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure

  • Cecilia Frale
  • Libero Monteforte

In this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, it is particularly suited for real time forecast as it reduces the problem of the unbalanced data set and of the revisions in preliminary data. In the empirical application we specify and estimate a FaMIDAS to forecast Italian quarterly GDP. The short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models.

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File URL: http://www.dt.tesoro.it/export/sites/sitodt/modules/documenti_it/analisi_progammazione/working_papers/WP_n.3_2010.pdf
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Paper provided by Department of the Treasury, Ministry of the Economy and of Finance in its series Working Papers with number 3.

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Handle: RePEc:itt:wpaper:wp2010-3
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  1. 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.
  2. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo Group Munich.
  3. 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.
  4. 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.
  5. Altissimo, Filippo & Cristadoro, Riccardo & Forni, Mario & Lippi, Marco & Veronese, Giovanni, 2006. "New EuroCOIN: Tracking Economic Growth in Real Time," CEPR Discussion Papers 5633, C.E.P.R. Discussion Papers.
  6. 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.
  7. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.
  8. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, 08.
  9. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, 07.
  10. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
  11. Libero Monteforte & Gianluca Moretti, 2010. "Real time forecasts of inflation: the role of financial variables," Temi di discussione (Economic working papers) 767, Bank of Italy, Economic Research and International Relations Area.
  12. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2008. "Short-Term Forecasting of GDP Using Large Monthly Datasets: A Pseudo Real-Time Forecast Evaluation Exercise," Bank of Lithuania Working Paper Series 1, Bank of Lithuania.
  13. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
  14. Tommaso Proietti, 2004. "Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited," Econometrics 0411011, EconWPA.
  15. 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.
  16. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2008. "A Monthly Indicator of the Euro Area GDP," Economics Working Papers ECO2008/32, European University Institute.
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