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Modelling industrial new orders for the euro area

Author

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  • de Bondt, Gabe
  • Dieden, Heinz Christian
  • Muzikarova, Sona
  • Vincze, Istvan

Abstract

This paper models industrial new orders across European Union (EU) Member States for various breakdowns. A common modelling framework exploits both soft data (business opinion surveys) and hard data (industrial turnover). The estimates show for about 200 cases that the model determinants significantly help in explaining monthly growth rates for new orders. An alternative estimation method, different model specifications and out-of-sample and real-time forecasting all show that the model results are robust. We present real-time outcomes of a European Central Bank (ECB) indicator on industrial new orders at an aggregated euro area level. This indicator is largely based on national new orders data and on estimates yielded by the model for those countries that no longer report new orders at the national level. Finally, we demonstrate the leading nature of the ECB indicator on euro area new orders in relation to industrial production. JEL Classification: C22, C52, E32

Suggested Citation

  • de Bondt, Gabe & Dieden, Heinz Christian & Muzikarova, Sona & Vincze, Istvan, 2014. "Modelling industrial new orders for the euro area," Statistics Paper Series 6, European Central Bank.
    • Handle: RePEc:ecb:ecbsps:20146
    Note: 337418
    as

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    File URL: https://www.ecb.europa.eu//pub/pdf/scpsps/ecbsp6.en.pdf
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    References listed on IDEAS

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    2. Richard Etter & Michael Graff, 2003. "Estimating and Forecasting Production and Orders in Manufacturing Industry from Business Survey Data: Evidence from Switzerland, 1990-2003," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(IV), pages 507-533, December.
    3. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    4. Nicholson, R J & Tebbutt, S G, 1979. "Modelling of New Orders for Private Industrial Building," Journal of Industrial Economics, Wiley Blackwell, vol. 28(2), pages 147-160, December.
    5. Helmut Herwartz, 2010. "A note on model selection in (time series) regression models - general-to-specific or specific-to-general?," Applied Economics Letters, Taylor & Francis Journals, vol. 17(12), pages 1157-1160.
    6. S. S. Alexander & H. O. Stekler, 1959. "Forecasting Industrial Production--Leading Series versus Autoregression," Journal of Political Economy, University of Chicago Press, vol. 67(4), pages 402-402.
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    Cited by:

    1. Knut Lehre Seip & Yunus Yilmaz & Michael Schröder, 2019. "Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?," Economies, MDPI, vol. 7(4), pages 1-18, October.
    2. Ting Fung Ma & Chun Yip Yau, 2016. "A pairwise likelihood-based approach for changepoint detection in multivariate time series models," Biometrika, Biometrika Trust, vol. 103(2), pages 409-421.

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    More about this item

    Keywords

    euro area; industrial new orders; Leading indicators; real-time analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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