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Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques

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  • Elena Olmedo

    ()

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    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

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    File URL: http://hdl.handle.net/10.1007/s10614-013-9371-1
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    Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 43 (2014)
    Issue (Month): 2 (February)
    Pages: 183-197

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    Handle: RePEc:kap:compec:v:43:y:2014:i:2:p:183-197
    DOI: 10.1007/s10614-013-9371-1
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