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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
  • Konstantin A. Kholodilin

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 rates 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 rates compared to a benchmark naive constant-growth model at all forecast horizons and at all data vintages. The largest forecast accuracy is achieved when GDP 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.

Suggested Citation

  • Boriss Siliverstovs & Konstantin A. Kholodilin, 2010. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Discussion Papers of DIW Berlin 970, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp970
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    Cited by:

    1. 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.
    2. repec:eee:ecmode:v:69:y:2018:i:c:p:160-168 is not listed on IDEAS
    3. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, Elsevier.
    4. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    5. repec:eee:intfor:v:33:y:2017:i:4:p:915-935 is not listed on IDEAS
    6. Schumacher Christian, 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), De Gruyter, vol. 231(1), pages 28-49, February.
    7. Klaus Abberger & Michael Graff & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "The KOF Economic Barometer, Version 2014," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
    8. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    9. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    10. D'Agostino, Antonello & McQuinn, Kieran & O'Brien, Derry, 2008. "Now-casting Irish GDP," Research Technical Papers 9/RT/08, Central Bank of Ireland.
    11. repec:eee:ecmode:v:67:y:2017:i:c:p:294-299 is not listed on IDEAS
    12. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.

    More about this item

    Keywords

    Business tendency surveys; Forecasting; Nowcasting; Real-time data; Dynamic factor model;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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