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The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models

  • Darracq Pariès, Matthieu
  • Maurin, Laurent

Several factor-based models are estimated to investigate the role of country-specific trade and survey data in forecasting euro area manufacturing production. Following Boivin and Ng (2006), the emphasis is put on the role of dataset selection on the empirical performance of factor models. First, spectral analysis is used to assess the information content for euro area manufacturing production of external trade and surveys data of the three largest economies as well as two medium-sized highly opened economies. Second, common factors are estimated on four datasets, following two methodologies, Stock and Watson (2002a, 2002b) and Forni et al. (2005). Third, a rolling out of sample forecast comparison exercise is carried out on nine models. Compared to univariate benchmarks, our results are supportive of factor-based models up to two quarters. They show that incorporating survey and external trade information improves the forecast of manufacturing production. They also confirm the findings of Marcellino, Stock and Watson (2003) that, using country information, it is possible to improve forecasts for the euro area. Interesting, the medium-sized highly opened economies provide valuable information to monitor area wide developments, beyond their weight in the aggregate. Conversely, the large countries do not add much to the monitoring of the aggregate, when considered separately. JEL Classification: E37, C3, C53

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Paper provided by European Central Bank in its series Working Paper Series with number 0894.

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Date of creation: May 2008
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Handle: RePEc:ecb:ecbwps:20080894
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  1. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
  2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
  4. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  5. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  6. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  7. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A measure of co-movement for economic variables: theory and empirics," ULB Institutional Repository 2013/10139, ULB -- Universite Libre de Bruxelles.
  8. Bańbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346.
  9. Antonello D’ Agostino & Domenico Giannone, 2012. "Comparing Alternative Predictors Based on Large‐Panel Factor Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 306-326, 04.
  10. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  11. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary University of London, School of Economics and Finance.
  12. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  13. Angelini, Elena & Henry, Jérôme & Marcellino, Massimiliano, 2003. "Interpolation and backdating with a large information set," Working Paper Series 0252, European Central Bank.
  14. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Working Papers 07-8, Bank of Canada.
  15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  16. Schumacher, Christian & Breitung, Jörg, 2006. "Real-time forecasting of GDP based on a large factor model with monthly and quarterly data," Discussion Paper Series 1: Economic Studies 2006,33, Deutsche Bundesbank, Research Centre.
  17. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
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