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Ñ-STING: España Short Term INdicator of Growth

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Author Info

  • Maximo Camacho

    ()
    (Universidad de Murcia)

  • Gabriel Perez-Quiros

    ()
    (Banco de España)

Abstract

We develop a dynamic factor model to compute short term forecasts of the Spanish GDP growth in real time. With this model, we compute a business cycle index which works well as an indicator of the business cycle conditions in Spain. To examine its real time forecasting accuracy, we use real-time data vintages from 2008.02 through 2009.01. We conclude that the model exhibits good forecasting performance in anticipating the recent and sudden downturn.

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File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/09/Fic/dt0912e.pdf
File Function: First version, June 2009
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Bibliographic Info

Paper provided by Banco de Espa�a in its series Banco de Espa�a Working Papers with number 0912.

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Length: 28 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:bde:wpaper:0912

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Related research

Keywords: Business Cycles; Output Growth; Time Series;

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References

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  1. Camacho, Maximo & Pérez-Quirós, Gabriel, 2009. "Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth," CEPR Discussion Papers 7343, C.E.P.R. Discussion Papers.
  2. Tom Stark and Dean Croushore, 2001. "Forecasting with a Real-Time Data Set for Macroeconomists," Computing in Economics and Finance 2001 258, Society for Computational Economics.
  3. 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.
  4. 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.
  5. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-time measurement of business conditions," Working Papers 08-19, Federal Reserve Bank of Philadelphia.
  6. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  7. Banbura, 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, April.
  8. Tommaso Proietti & Filippo Moauro, 2004. "Dynamic Factor Analysis with Nonlinear Temporal Aggregation Constraints," Econometrics 0401003, EconWPA.
  9. Israel Sancho & maximo Camacho, 2002. "Spanish diffusion indexes," Computing in Economics and Finance 2002 276, Society for Computational Economics.
  10. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
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Citations

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Cited by:
  1. Gonzalo Echavarría M. & Wildo González P, 2011. "Un Modelo de Factores Dinámicos de Pequeña Escala para el Imacec," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 14(2), pages 109-118, August.
  2. Stéphanie Guichard & Elena Rusticelli, 2011. "A Dynamic Factor Model for World Trade Growth," OECD Economics Department Working Papers 874, OECD Publishing.
  3. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2010. "Business Survey Data in Forecasting Macroeconomic Indicators with Combined Forecasts," Contemporary Economics, University of Finance and Management in Warsaw, vol. 4(4), December.
  4. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.

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