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Modelling industrial new orders

Author

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  • de Bondt, Gabe J.
  • Dieden, Heinz C.
  • Muzikarova, Sona
  • Vincze, Istvan

Abstract

This article models industrial new orders across the European Union (EU) countries for various breakdowns. A common modelling framework exploits soft (business opinion surveys) as well as hard data (industrial turnover). The estimates show for about 200 cases that the model determinants significantly help in explaining new orders' monthly growth rates. 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 content of the ECB indicator on euro area new orders for industrial production.

Suggested Citation

  • de Bondt, Gabe J. & Dieden, Heinz C. & Muzikarova, Sona & Vincze, Istvan, 2014. "Modelling industrial new orders," Economic Modelling, Elsevier, vol. 41(C), pages 46-54.
    • Handle: RePEc:eee:ecmode:v:41:y:2014:i:c:p:46-54
    DOI: 10.1016/j.econmod.2014.04.004
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    References listed on IDEAS

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    1. 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.
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    5. 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.
    6. 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.
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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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