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Are GDP Revisions Predictable? Evidence for Switzerland

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  • Boriss Siliverstovs

Abstract

This study presents a model that delivers more accurate forecasts of the revised rather initial estimates of the quarterly GDP growth rate in Switzerland during the period of the recent financial crisis. The key explanation to our findings is that our model, capitalizing on the information contained in the Business Tendency Surveys, is able to predict future revisions of the initial estimates. Our findings imply that there seems to be a scope for improvement of how preliminary estimates of the quarterly GDP growth rate are produced in Switzerland.A mixed-frequency small-scale dynamic factor model is used for forecasting. The model parameters are estimated by means of the Kalman filter. This model combines GDP growth available at a quarterly frequency and monthly indicators based on firms' surveys. The different publication lags are also taken into account.In this paper we constructed a small-scale mixed-frequency dynamic factor model using data for Switzerland. The factor model combines the quarterly GDP growth rate and the monthly survey indicators. We evaluate the forecasting performance of the model during the period of the recent financial crisis when accurate information on the current stance of the economy is especially in high demand. We demonstrate that this factor model produces more accurate forecasts than the alternative benchmark models such as a random-walk model and a first-order autoregressive model. More importantly, the factor model produces more accurate forecasts of the revised rather than first-published estimates of the GDP growth rate. We demonstrate that this remarkable finding could be explained by the fact that the factor model is useful for predicting not only directions of future GDP revisions but also their magnitude, at least during the period under scrutiny. We conclude that there seems to be a scope for improvement of how estimates of the GDP growth rate are produced in Switzerland: in particularly, in the direction of reducing volatility of subsequent revisions.

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  • Boriss Siliverstovs, 2012. "Are GDP Revisions Predictable? Evidence for Switzerland," EcoMod2012 4219, EcoMod.
    • Handle: RePEc:ekd:002672:4219
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    References listed on IDEAS

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    1. 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.
    2. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    3. 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.
    4. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    5. Nicolas Cuche-Curti & Pamela Hall & Attilio Zanetti, 2009. "Swiss GDP revisions: A monetary policy perspective," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(2), pages 183-213.
    6. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    7. Maximo Camacho & Gabriel Perez Quiros, 2011. "Spain‐Sting: Spain Short‐Term Indicator Of Growth," Manchester School, University of Manchester, vol. 79(s1), pages 594-616, June.
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    Cited by:

    1. Boriss Siliverstovs, 2016. "The franc shock and Swiss GDP: how long does it take to start feeling the pain?," Applied Economics, Taylor & Francis Journals, vol. 48(36), pages 3432-3441, August.
    2. 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.
    3. Katharina Glass, 2018. "Predictability of Euro Area Revisions," Macroeconomics and Finance Series 201801, University of Hamburg, Department of Socioeconomics.
    4. 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.
    5. Caroline Flodberg & Pär Österholm, 2017. "A Statistical Anaysis of Revisions in Swedish National Accounts Data," Finnish Economic Papers, Finnish Economic Association, vol. 28(1), pages 10-33, Autumn.

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    More about this item

    Keywords

    Switzerland; Business cycles; Forecasting; nowcasting;
    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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