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Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland

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  • Boriss Siliverstovs

    ()
    (ETH Zuerich)

  • Konstantin A. Kholodilin

    ()
    (DIW Berlin)

Abstract

This study utilizes the dynamic factor model of Giannone et al. (2008) in order to make now-/ forecasts of GDP quarter-on-quarter growth rate in Switzerland. It also assesses the informational content of macroeconomic data releases for forecasting of the Swiss GDP. We find that the factor model offers a substantial improvement in forecast accuracy of GDP growth rate compared to a benchmark naive constant-growth model at all forecast horizons and at all data vintages. The largest forecast accuracy is achieved whenGDP nowcasts for an actual quarter are made about three months ahead of the official data release.We also document that both business tendency surveys as well as stock market indices possess the largest informational content for GDP forecasting, although their ranking depends on the underlying transformation of monthly indicators from which the common factors are extracted.

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Bibliographic Info

Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

Volume (Year): 232 (2012)
Issue (Month): 4 (July)
Pages: 429-444

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Handle: RePEc:jns:jbstat:v:232:y:2012:i:4:p:429-444

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Keywords: Business tendency surveys; forecasting; nowcasting; real-time data; dynamic factor model;

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References

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Citations

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Cited by:
  1. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2007. "A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering," CEPR Discussion Papers 6043, C.E.P.R. Discussion Papers.
  2. Christian Schumacher, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 28-49, February.
  3. Antonello D'Agostino & Kieran McQuinn & Derry O’Brien, 2012. "Nowcasting Irish GDP," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2012(2), pages 21-31.
  4. Klaus Abberger & Boriss Siliverstovs & Jan-Egbert Sturm & Michael Graff, 2014. "The KOF Economic Barometer, Version 2014: A Composite Leading Indicator for the Swiss Business Cycle," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
  5. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.

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