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Nonlinear Forecasting of Euro Area Industrial Production Using Evolutionary Approaches

Author

Listed:
  • Christos Avdoulas

    () (Athens University of Economics and Business (AUEB))

  • Stelios Bekiros

    () (European University Institute (EUI))

Abstract

Abstract Stock Watson (in: Mills T, Patterson K (eds) Palgrave handbook of econometrics, Palgrave MacMillan, Basingstoke, 2003) argue that robust forecastability is dependent upon the optimality of the estimated parameters. Whilst recent studies in macroeconomic forecasting report the superiority of nonlinear models, yet they still suffer from precise parameter estimation. Our approach introduces evolutionary programming to optimize the parameters of various Threshold Autoregressive models. We generate forecasts for industrial production and compare our results versus linear benchmarks and quasi-maximum likelihood estimates for three Euro area countries. Based on our robust method, central banks and policy-makers could dynamically adjust their monetary and fiscal policy predictions.

Suggested Citation

  • Christos Avdoulas & Stelios Bekiros, 2018. "Nonlinear Forecasting of Euro Area Industrial Production Using Evolutionary Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 521-530, August.
  • Handle: RePEc:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9695-3
    DOI: 10.1007/s10614-017-9695-3
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    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Timmermann, Allan, 2005. "Small sample properties of forecasts from autoregressive models under structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 183-217.
    2. Albonico, Alice & Paccagnini, Alessia & Tirelli, Patrizio, 2016. "In search of the Euro area fiscal stance," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 254-264.
    3. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    4. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    5. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    6. Bekiros, Stelios D., 2009. "A robust algorithm for parameter estimation in smooth transition autoregressive models," Economics Letters, Elsevier, vol. 103(1), pages 36-38, April.
    7. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    8. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
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    More about this item

    Keywords

    Growth forecasting; Nonlinear models; Evolutionary methods;

    JEL classification:

    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment

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