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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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  • Siliverstovs Boriss

    (KOF Swiss Economic Institute, ETH Zurich, Weinbergstrasse 35, 8092 Zurich, Switzerland, Germany)

  • Kholodilin Konstantin A.

    (DIW Berlin, Mohrenstraße 58, 10117 Berlin, Germany)

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 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.

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  • Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    • Handle: RePEc:jns:jbstat:v:232:y:2012:i:4:p:429-444
    DOI: 10.1515/jbnst-2012-0404
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    Cited by:

    1. 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.
    2. 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.
    3. Caruso, Alberto, 2018. "Nowcasting with the help of foreign indicators: The case of Mexico," Economic Modelling, Elsevier, vol. 69(C), pages 160-168.
    4. 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.
    5. Evžen Kočenda & Karen Poghosyan, 2020. "Nowcasting Real GDP Growth: Comparison between Old and New EU Countries," Eastern European Economics, Taylor & Francis Journals, vol. 58(3), pages 197-220, May.
    6. 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.
    7. 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.
    8. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    9. 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.
    10. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    11. 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.
    12. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
    13. Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    14. Ronald Indergand & Stefan Leist, 2014. "A Real-Time Data Set for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(IV), pages 331-352, December.
    15. Antonello D'Agostino & Kieran McQuinn & Derry O’Brien, 2012. "Nowcasting Irish GDP," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(2), pages 21-31.
    16. 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.
    17. Мекенбаева Камила // Mekenbayeva Kamila & Karel Musil, 2017. "Система прогнозирования в Национальном Банке Казахстана: наукаст на основа опросов // Forecasting system at the National Bank of Kazakhstan: survey-based nowcasting," Working Papers #2017-1, National Bank of Kazakhstan.
    18. Siliverstovs, Boriss, 2017. "Dissecting models' forecasting performance," Economic Modelling, Elsevier, vol. 67(C), pages 294-299.

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    More about this item

    Keywords

    Business tendency surveys; forecasting; nowcasting; real-time data; dynamic factor model; Business tendency surveys; forecasting; nowcasting; real-time data; dynamic factor model;
    All these keywords.

    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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