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Sectoral Survey-based Confidence Indicators for Europe

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  • Andrea Carriero
  • Massimiliano Marcellino

Abstract

In this paper we analyze a novel dataset of Business and Consumer Surveys, using dynamic factor techniques, to produce composite coincident indices (CCIs) at the sectoral level for the European countries and for Europe as a whole. Few CCIs are available for Europe compared to the US, and most of them use macroeconomic variables and focus on aggregate activity. However, there are often delays in the release of macroeconomic data, later revisions, and differences in the definition of the variables across countries, while the surveys are timely available, not subject to revision, and fully comparable across countries. Moreover, there are substantial discrepancies in activity at the sectoral level, which justifies the interest in a sectoral disaggregation. Compared to the Confidence Indicators produced by the European Commission, which are based on a simple average of the aggregate survey answers, we show that factor based CCIs, using survey answers at a more disaggregate level, produce higher correlation with the reference series for the majority of sectors and countries.

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Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 320.

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Date of creation: 2007
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Handle: RePEc:igi:igierp:320

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  1. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers, C.E.P.R. Discussion Papers 3108, C.E.P.R. Discussion Papers.
  2. 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.
  3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
  4. Artis, M. & Krolzig, H.-M. & Toro, J., 1999. "The European Business Cycle," Economics Working Papers, European University Institute eco99/24, European University Institute.
  5. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2003. "Dating the Euro Area Business Cycle," Working Papers, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University 237, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  6. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 12(3), pages 299-308, July.
  7. Christian Gayer & Julien Genet, 2006. "Using factor models to construct composite indicators from BCS data - a comparison with European Commission confidence indicators," European Economy - Economic Papers, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission 240, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission.
  8. Michael Artis, 2003. "Is there a European Business Cycle?," CESifo Working Paper Series 1053, CESifo Group Munich.
  9. Diebold & Rudebusch, . "Measuring Business Cycle: A Modern Perspective," Home Pages, University of Pennsylvania _061, University of Pennsylvania.
  10. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  11. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  12. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, Elsevier, vol. 5(4), pages 559-583.
  13. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers, York (Canada) - Department of Economics 91-8, York (Canada) - Department of Economics.
  14. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 111(471), pages C62-85, May.
  15. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 357-84, March.
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Cited by:
  1. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(4), pages 644-658.
  2. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  3. Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gian Luigi & Proietti, Tommaso, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers, C.E.P.R. Discussion Papers 7007, C.E.P.R. Discussion Papers.
  4. Nicoletta Pashourtidou & Andreas Tsiaklis, 2011. "An Analysis of Firms’ Expectations about Activity and Employment," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, University of Cyprus, Economics Research Centre, vol. 5(1), pages 71-85, June.

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