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Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

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  • Massimiliano Marcellino
  • Christian Schumacher

Abstract

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed-frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high- and low-frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low-frequency variables like gross domestic product (GDP) exploiting information in a large set of higher-frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real-time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state-space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed-frequency techniques that can exploit timely information from business cycle indicators. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2010.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics and Statistics.

Volume (Year): 72 (2010)
Issue (Month): 4 (08)
Pages: 518-550

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Handle: RePEc:bla:obuest:v:72:y:2010:i:4:p:518-550

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Cited by:
  1. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
  2. Steffen Henzel & Malte Rengel, 2013. "Dimensions of macroeconomic uncertainty: A common factor analysis," Ifo Working Paper Series Ifo Working Paper No. 167, Ifo Institute for Economic Research at the University of Munich.
  3. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
  4. Götz Thomas B. & Hecq Alain & Urbain Jean-Pierre, 2012. "Real-Time Forecast Density Combinations (Forecasting US GDP Growth Using Mixed-Frequency Data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  5. Cecilia Frale & Libero Monteforte, 2011. "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Temi di discussione (Economic working papers) 788, Bank of Italy, Economic Research and International Relations Area.
  6. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," EconomiX Working Papers 2013-19, University of Paris West - Nanterre la Défense, EconomiX.
  7. Jennifer Castle & David Hendry, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
  8. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
  9. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle: A comparative study for Germany and Switzerland," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.

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