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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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    1. Dufourt, Frédéric & Lloyd-Braga, Teresa & Modesto, Leonor, 2008. "Indeterminacy, Bifurcations, And Unemployment Fluctuations," Macroeconomic Dynamics, Cambridge University Press, vol. 12(S1), pages 75-89, April.
    2. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 298-312, July.
    3. Geraint Johnes, 1999. "Forecasting unemployment," Applied Economics Letters, Taylor & Francis Journals, vol. 6(9), pages 605-607.
    4. Bask, Mikael, 2000. "A Positive Lyapunov Exponent in Swedish Exchange Rates?," Umeå Economic Studies 528, Umeå University, Department of Economics.
    5. Diamond, Peter A, 1982. "Aggregate Demand Management in Search Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 881-894, October.
    6. Theodore Panagiotidis, 2002. "Testing the assumption of Linearity," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-9.
    7. Filippo Altissimo & Giovanni L. Violante, 2001. "The non-linear dynamics of output and unemployment in the U.S," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(4), pages 461-486.
    8. Neugart, Michael, 2004. "Complicated dynamics in a flow model of the labor market," Journal of Economic Behavior & Organization, Elsevier, vol. 53(2), pages 193-213, February.
    9. Brock, William A. & Sayers, Chera L., 1988. "Is the business cycle characterized by deterministic chaos?," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 71-90, July.
    10. Catherine Kyrtsou & Michel Terraza, 2010. "Seasonal Mackey–Glass–GARCH process and short-term dynamics," Empirical Economics, Springer, vol. 38(2), pages 325-345, April.
    11. Annalisa Fabretti & Marcel Ausloos, 2005. "Recurrence analysis of the NASDAQ crash of April 2000," Papers physics/0505170, arXiv.org.
    12. Ossama Mikhail & Curtis Eberwein & Jagdish Handa, 2005. "On the evidence of non-linear structure in Canadian unemployment," Applied Economics Letters, Taylor & Francis Journals, vol. 12(2), pages 101-104.
    13. Hallegatte, Stéphane & Ghil, Michael & Dumas, Patrice & Hourcade, Jean-Charles, 2008. "Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 57-77, July.
    14. Mehmet Caner & Bruce E. Hansen, 2001. "Threshold Autoregression with a Unit Root," Econometrica, Econometric Society, vol. 69(6), pages 1555-1596, November.
    15. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    16. Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 224-236, April.
    17. Skalin, Joakim & Ter svirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(02), pages 202-241, April.
    18. Franses, Philip Hans & Paap, Richard & Vroomen, Bjorn, 2004. "Forecasting unemployment using an autoregression with censored latent effects parameters," International Journal of Forecasting, Elsevier, vol. 20(2), pages 255-271.
    19. Mortensen, Dale T, 1999. "Equilibrium Unemployment Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(4), pages 889-914, November.
    20. Jorge Belaire-Franch, & Dulce Contreras & Lorena Tordera-Lledo, 2002. "Assessing Non-Linear Structures in Real Exchange Rates Using Recurrence Plot Strategies," Computing in Economics and Finance 2002 239, Society for Computational Economics.
    21. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    22. repec:gue:guelph:1991-4 is not listed on IDEAS
    23. Claudio Bonilla & Rafael Romero-Meza & Melvin Hinich, 2006. "Episodic nonlinearity in Latin American stock market indices," Applied Economics Letters, Taylor & Francis Journals, vol. 13(3), pages 195-199.
    24. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
    25. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    26. Fanti, Luciano & Manfredi, Piero, 2007. "Neoclassical labour market dynamics, chaos and the real wage Phillips curve," Journal of Economic Behavior & Organization, Elsevier, vol. 62(3), pages 470-483, March.
    27. Laura Brown & Saeed Moshiri, 2004. "Unemployment variation over the business cycles: a comparison of forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 497-511.
    28. repec:rim:rimwps:06-07 is not listed on IDEAS
    29. Panagiotidis, Theodore & Pelloni, Gianluigi, 2007. "Nonlinearity In The Canadian And U.S. Labor Markets: Univariate And Multivariate Evidence From A Battery Of Tests," Macroeconomic Dynamics, Cambridge University Press, vol. 11(05), pages 613-637, November.
    30. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
    31. Tramontana, F. & Gardini, L. & Ferri, P., 2010. "The dynamics of the NAIRU model with two switching regimes," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 681-695, April.
    32. D. A. Peel & A. E. H. Speight, 2000. "Threshold nonlinearities in unemployment rates: further evidence for the UK and G3 economies," Applied Economics, Taylor & Francis Journals, vol. 32(6), pages 705-715.
    33. Soliman, A. S., 1996. "Transitions from stable equilibrium points to periodic cycles to chaos in a phillips curve system," Journal of Macroeconomics, Elsevier, vol. 18(1), pages 139-153.
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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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