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Real-time forecasting of GDP based on a large factor model with monthly and quarterly data

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Author Info
Schumacher, Christian
Breitung, Jörg

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Abstract

This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm combined with a principal components estimator. We discuss the in-sample properties of the estimator in real-time environments and methods for out-of-sample forecasting. As an empirical application, we estimate monthly German GDP in real-time, discuss the nowcast and forecast accuracy of the model and the role of revisions. Furthermore, we assess the contribution of timely monthly data to the forecast performance. --

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Publisher Info
Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2006,33.

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Date of creation: 2006
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Handle: RePEc:zbw:bubdp1:5097

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Related research
Keywords: monthly GDP; EM algorithm; principal components; factor models;

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Find related papers by JEL classification:
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  1. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
    Other versions:
  2. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Documents de Travail 222, Banque de France. [Downloadable!]
  3. De Graeve, Ferre & Kick, Thomas, 2008. "Monetary policy and bank distress: an integrated micro-macro approach," Discussion Paper Series 2: Banking and Financial Studies 2008,03, Deutsche Bundesbank, Research Centre. [Downloadable!]
  4. Marta Banbura & Gerhard Rünstler, 2007. "A look into the factor model black box - publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 751, European Central Bank. [Downloadable!]
  5. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, 09. [Downloadable!]
  6. Jonas Dovern & Christina Ziegler, 2008. "Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators Under Real-Time Conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy. [Downloadable!]
    Other versions:
  7. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393. [Downloadable!]
  8. Laurent Maurin & Matthieu Darracq Pariès, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production. Perspective from Large Panel factor models," Working Paper Series 894, European Central Bank. [Downloadable!]
  9. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany. [Downloadable!]
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