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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

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

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    Keywords

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    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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