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A Quasi Real‐Time Leading Indicator for the EU Industrial Production

Author

Listed:
  • Michael Donadelli
  • Antonio Paradiso
  • Max Riedel

Abstract

We build a quasi‐real‐time leading indicator (LI) for the EU industrial production (EU IP). Differently from previous studies, the technique developed in this paper gives rise to an ex‐ante LI that is immune to ex‐post revisions in constituent variables and, thus, does not suffer from overlapping information drawbacks. Moreover, the set of variables used to construct the LI relies on a two‐step dynamic and systematic statistical procedure. This approach ensures that the choice of the variables is not driven by subjective views. Our LI anticipates—on average—main recession periods in the EU industrial production by two to three months. If revised, its predictive power improves. Additional empirical analyses show that the proposed LI (i) forecasts the Great Recession period better than the ex‐post LIs proposed by the OECD and the Conference Board, and (ii) captures the interest rate policy pattern rather well.

Suggested Citation

  • Michael Donadelli & Antonio Paradiso & Max Riedel, 2019. "A Quasi Real‐Time Leading Indicator for the EU Industrial Production," Manchester School, University of Manchester, vol. 87(4), pages 510-542, July.
    • Handle: RePEc:bla:manchs:v:87:y:2019:i:4:p:510-542
    DOI: 10.1111/manc.12233
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    Cited by:

    1. Uluceviz, Erhan & Yilmaz, Kamil, 2021. "Measuring real–financial connectedness in the U.S. economy," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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

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