Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques

Elena Olmedo ()

Computational Economics, 2014, vol. 43, issue 2, 183-197

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

Keywords: Forecasting; Neural networks; Unemployment; Nonlinearity; B41; C14; C32; C45; C51; C53 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:43:y:2014:i:2:p:183-197

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DOI: 10.1007/s10614-013-9371-1

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