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Forecasting Industrial Production and the Early Detection of Turning Points

Author

Listed:
  • Giancarlo Bruno

    (Institute for Studies and Economic Analyses)

  • Claudio Lupi

    (Institute for Studies and Economic Analyses & Libera Università Maria SS. Assunta)

Abstract

In this paper we propose a simple model to forecast industrial production in Italy. We show that the forecasts produced using the model outperform some popular forecasts as well as those stemming from a trading days- and outlier-robust ARIMA model used as a benchmark. We show that the use of appropriately selected leading variables allows to produce up to twelve-step ahead reliable forecasts. We show how and why the use of these forecasts can improve the estimate of a cyclical indicator and the early detection of turning points for the manufacturing sector. This is of paramount importance for short-term economic analysis.

Suggested Citation

  • Giancarlo Bruno & Claudio Lupi, 2001. "Forecasting Industrial Production and the Early Detection of Turning Points," Econometrics 0110004, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0110004
    Note: Type of Document - zipped PDF; prepared on IBM PC ; pages: 38; figures: included
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    References listed on IDEAS

    as
    1. Giancarlo Bruno, 2001. "Seasonal Adjustment of Italian Industrial Production Index using Tramo-Seats," ISAE Working Papers 18, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    2. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
    3. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecasting; Forecast Encompassing; VAR Models; Industrial Production; Cyclical Indicators;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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