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Forecasting Capacity of ARIMA Models; A Study on Croatian Industrial Production and its Sub-sectors

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  • Daniel Tomiæ Saša Stjepanoviæ

    (Juraj Dobrila University of Pula, Faculty of Economics and Tourism «Dr.Mijo Mirkoviæ», Pula, Croatia Juraj Dobrila University of Pula, Faculty of Economics and Tourism «Dr.Mijo Mirkoviæ», Pula, Croatia)

Abstract

As one of the most important indicator for monitoring the production in industry as well as for directing investment decisions, industrial production plays important role within growth perspectives. Not only does the composition and/or fluctuation of the goods produced indicate the course of economic activity but it also reflects the changes in cyclical development of the economy thereby providing opportunity to macro-manage with early signs of (short-term) turning-points and (long-term) trend variations. In this paper, we compare univariate autoregressive integrated moving average (ARIMA) models of the Croatian industrial production and its subsectors in order to evaluate their forecasting features within short and long-term data evolution. The aim of this study is not to forecast industrial production but to analyze the out-of-sample predictive performance of ARIMA models on aggregated and disaggregated level inside different forecasting horizons. Our results suggest that ARIMA models do perform very well over the whole rage of the prediction horizons. It is mainly because univariate models often improve the predictive ability of their single component over the short horizons. In that manner ARIMA modelling could be used at least as a benchmark for more complex forecasting methods in predicting the movements of industrial production in Croatia. JEL Classification: C22; E23; E61

Suggested Citation

  • Daniel Tomiæ Saša Stjepanoviæ, 2017. "Forecasting Capacity of ARIMA Models; A Study on Croatian Industrial Production and its Sub-sectors," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 20(1), pages 81-99, May.
    • Handle: RePEc:zag:zirebs:v:20:y:2017:i:1:p:81-99
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    References listed on IDEAS

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    1. Katarina Bacic & Maruska Vizek, 2006. "A Brand New CROLEI: Do We Need a New Forecasting Index?," Financial Theory and Practice, Institute of Public Finance, vol. 30(4), pages 311-346.
    2. Ivo Krznar, 2011. "Identifying Recession and Expansion Periods in Croatia," Working Papers 29, The Croatian National Bank, Croatia.
    3. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    4. Giancarlo Bruno & Claudio Lupi, 2004. "Forecasting industrial production and the early detection of turning points," Empirical Economics, Springer, vol. 29(3), pages 647-671, September.
    5. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination

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