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Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland

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  • Boriss Siliverstovs

Abstract

This study investigates the usefulness of business tendency surveys collected at the KOF Swiss Economic Institute and aggregated in the form of the KOF Employment Indicator for short-term forecasting of employment in Switzerland. We use a real-time dataset in order to simulate the actual predictive process using only information that was available at the time when predictions were made. We evaluate the predictive content of the KOF Employment Indicator both for nowcasts that are published two months before the first official release, and for one-quarter ahead forecasts published five months before the first official release. We find that inclusion of the KOF Employment Indicator leads to a substantial improvement in prediction accuracy of both point and density forecasts compared to the performance of a benchmark autoregressive model.

Suggested Citation

  • Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    • Handle: RePEc:oec:stdkab:5k4bxlxjkd32
    DOI: 10.1787/jbcma-2013-5k4bxlxjkd32
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    Cited by:

    1. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    2. Raoufina, Karine, 2016. "Forecasting Employment Growth in Sweden Using a Bayesian VAR Model," Working Papers 144, National Institute of Economic Research.
    3. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity

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