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Using BREL to nowcast the Belgian business cycle: the role of survey data

Author

Listed:
  • Ch. Piette

    (National Bank of Belgium)

  • G. Langenus

    (National Bank of Belgium)

Abstract

The article assesses the usefulness of indicators taken from surveys carried out by the National Bank of Belgium for predicting Belgian GDP and other important quarterly macroeconomic aggregates. To this end, the authors use the recently created BREL now-casting platform that consists of targeted bridge models for different data availability scenarios. BREL is based upon an elastic-net regression approach that takes into account the ragged-edge nature of the data set. The results of their empirical analysis suggest that survey data clearly help to predict Belgian (but also European) macroeconomic developments, in particular for earlier estimates, when the relevant hard data, notably firms’ turnover and industrial production, are not yet available. They also show that forecast accuracy is higher when using disaggregated survey results, rather than just the headline consumer confidence and business sentiment indicators. In this connection, demand expectations in the manufacturing industry and the unemployment expectations in the consumer survey consistently feature among the best predictors for real GDP growth.

Suggested Citation

  • Ch. Piette & G. Langenus, 2014. "Using BREL to nowcast the Belgian business cycle: the role of survey data," Economic Review, National Bank of Belgium, issue i, pages 75-98, June.
    • Handle: RePEc:nbb:ecrart:y:2014:m:june:i:i:p:75-98
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    3. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
    4. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    5. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    6. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    7. repec:dau:papers:123456789/10079 is not listed on IDEAS
    8. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
    9. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.
    10. Christophe Van Nieuwenhuyze, 2006. "A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and GDP growth forecasts," Working Paper Research 80, National Bank of Belgium.
    11. Vanhaelen, J.J. & Dresse, L. & de Mulder, J., 2000. "The Belgian Industrial Confidence Indicator: Leading Indicator of Economic Activity in the Euro Area?," Papers 12, Warwick - Development Economics Research Centre.
    12. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Christophe van Nieuwenhuyze, 2006. "A Generalized Dynamic Factor Model for the Belgian Economy: Identification of the Business Cycle and GDP Growth Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(2), pages 213-247.
    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 147-162, April.
    16. Jean-Jacques Vanhaelen & Luc Dresse & Jan De Mulder, 2000. "The Belgian industrial confidence indicator: leading indicator of economic activity in the euro area ?," Working Paper Document 12, National Bank of Belgium.
    17. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
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    Cited by:

    1. Ana Arencibia Pareja & Ana Gomez-Loscos & Mercedes de Luis López & Gabriel Perez-Quiros, 2020. "A Short Term Forecasting Model for the Spanish GDP and itsDemand Components," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 43(85), pages 1-30.
    2. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Goodness-of-fit test in high-dimensional linear sparse models," LIDAM Discussion Papers CORE 2023008, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.
    4. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
    5. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.

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    More about this item

    Keywords

    Now-casting; bridge models; Belgium; business cycle;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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