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Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques

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  • Elena Olmedo

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Abstract

In this paper, alternative non-parametric forecasting techniques are analysed, with emphasis placed on the difference between the reconstruction and learning approaches. The former is based on Takens’ Theorem, which recovers unknown dynamic properties of a system; it is appropriate in deterministic systems. The latter is a powerful instrument in noisy systems. Both techniques are applied to the forecasting of Spanish unemployment, first one step -forecasting and second using a longer time horizon of prediction. To assess the robustness and generality of the methods we have employed unemployment time series of different European countries. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Elena Olmedo, 2014. "Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 183-197, February.
    • Handle: RePEc:kap:compec:v:43:y:2014:i:2:p:183-197
    DOI: 10.1007/s10614-013-9371-1
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    2. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    3. repec:spr:qualqt:v:51:y:2017:i:5:d:10.1007_s11135-016-0375-5 is not listed on IDEAS
    4. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Georgios Sermpinis & Charalampos Stasinakis & Konstantinos Theofilatos & Andreas Karathanasopoul, 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 471-487, September.

    More about this item

    Keywords

    Forecasting; Neural networks; Unemployment; Nonlinearity; B41; C14; C32; C45; C51; C53;

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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