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Nonlinear optimisation approach to proposing novel Croatian Industrial Confidence Indicator

Author

Listed:
  • Čižmešija Mirjana

    (University of Zagreb, Faculty of Economics & Business, Croatia)

  • Lukač Zrinka

    (University of Zagreb, Faculty of Economics & Business, Croatia)

  • Novoselec Tomislav

    (Microline d.o.o., Croatia)

Abstract

Croatian Industrial Confidence Indicator (ICI) is one of the measures of mangers’ sentiment about the economic situation in the Croatian manufacturing industry. Since 2005, the ICI has been calculated in accordance with the harmonised European Commission methodology as a simple average of three variables: order books, stocks of finished products and production expectation. It was empirically confirmed that the ICI could predict the direction of change in industrial production more than one month ahead. With the aim of raising the ICI forecasting power, this paper proposes a novel ICI with a different weighting scheme. The empirical analysis is based on monthly data for three standard ICI subcomponents and industrial production expressed as year-on-year growth rates. The data set covers the period from May 2008 to February 2019. Data sources were the European Commission and Eurostat. The newly defined ICI was constructed by using the nonlinear optimisation approach. The weights were determined by minimizing the root mean square error (RMSE) in a simple regression model and by maximizing the correlation coefficient between the ICI and industrial production for various time lags. The results reveal that the newly defined ICI performs better in adapting and following the industrial production growth rate as well as that the dominant component in the ICI is the production expectation.

Suggested Citation

  • Čižmešija Mirjana & Lukač Zrinka & Novoselec Tomislav, 2019. "Nonlinear optimisation approach to proposing novel Croatian Industrial Confidence Indicator," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 5(2), pages 17-26, December.
    • Handle: RePEc:vrs:crebss:v:5:y:2019:i:2:p:17-26:n:2
    DOI: 10.2478/crebss-2019-0008
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    More about this item

    Keywords

    business and consumer survey; industrial confidence indicator; nonlinear optimisation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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