Computational Economics

, Volume 43, Issue 2, pp 183–197 | Cite as

Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques

  • Elena Olmedo


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.


Forecasting Neural networks Unemployment Nonlinearity 

JEL Classification

B41 C14 C32 C45 C51 C53 


  1. Abhyankar, A., Copeland, L. S., & Wong, W. (1997). Uncovering nonlinear structure in real-time stock-market indexes: the S &P 500, the DAX, the Nikkei 225 and the FTSE-100. Journal of Business and Economic Statistics, 15(1), 1–14.Google Scholar
  2. Alogoskoufis, G. S., & Stengos, T. (1991). Testing for nonlinear dynamics in historical unemployment series. European University Institute Working Paper ECO 91/38, Badia Fiesolana, Italy.Google Scholar
  3. Altissimo, F., & Violante, G. L. (2001). The non-linear dynamics of output and unemployment in the U.S. Journal of Applied Econometrics, 16, 461–486.CrossRefGoogle Scholar
  4. Bask, M. (2002). A positive Lyapunov exponent in Swedish exchange rates? Chaos, Solitons & Fractals, 14, 1295–1304.CrossRefGoogle Scholar
  5. Barkoulas, J. T. (2008). Testing for deterministic monetary chaos: metric and topological diagnostics. Chaos, Solitons & Fractals, 38, 1013–1024.CrossRefGoogle Scholar
  6. Belaire-Franch, J., Contreras, D., & Tordera-Lledó, L. (2002). Assessing nonlinear structures in real exchange rates using recurrence plot strategies. Physica D, 171, 249–264.CrossRefGoogle Scholar
  7. Bigdeli, N., & Afshar, K. (2009). Characterization of Iran electricity market indices with pay-as-bid payment mechanism. Physica A-Statistical Mechanics and its Applications, 388(8), 1577–1592.CrossRefGoogle Scholar
  8. Bonilla, C. A., Romero-Meza, R., & Hinich, M. J. (2006). Episodic nonlinearity in Latin American stock market indices. Applied Economic Letters, 13, 195–199.CrossRefGoogle Scholar
  9. Brock, W. A., & Sayers, C. L. (1988). Is the business cycle characterized by deterministic chaos? Journal of Monetary Economics, 22, 71–90.CrossRefGoogle Scholar
  10. Caner, M., & Hansen, B. E. (2001). Threshold autoregression with a unit root. Econometrica, 69, 1555–1596.CrossRefGoogle Scholar
  11. Casdagli, M. (1989). Nonlinear prediction of chaotic time series. Physica D, 35, 35–56.CrossRefGoogle Scholar
  12. Casdagli, M., & Weigend, A. S. (1994). Exploring the continuum between deterministic and stochastic modelling. In A. S. Weigend & N. A. Gernshenfeld (Eds.), Time series prediction: forecasting the future and understanding the past (pp. 347–366). New York: Addison-Wesley.Google Scholar
  13. De Lima, P. J. F. (1998). Nonlinearities and nonstationarities in stock returns. Journal of Business and Economic Studies, 16(2), 227–236.Google Scholar
  14. DeLong, B. J., & Summers, L. (1986). Are business cycle symmetrical? In R. Gordon The (Ed.), American business cycle. Continuity change. Chicago: NBER University of Chicago Press.Google Scholar
  15. Diamond, P. (1982). Aggregate demand management in search equilibrium. Journal of Political Economy, 70, 881–894.CrossRefGoogle Scholar
  16. Dufourt, F., Lloyd-Braga, T., & Modesto, L. (2008). Indeterminacy, bifurcations and unemployment fluctuations. Macroeconomic Dynamics, 12(S1), 7589.CrossRefGoogle Scholar
  17. Fanti, L., & Manfredi, P. (2007). Neoclassical labour market dynamics, chaos and the real wage Phillips curve. Journal of Economic Behavior and Organization, 62(3), 470–483.CrossRefGoogle Scholar
  18. Fabretti, A., & Ausloos, M. (2005). Recurrence analysis of the NASDAQ crash of April 2000. International Journal of Moderns Physics C, 16(5), 671–706.CrossRefGoogle Scholar
  19. Farmer, J. D., & Sidorowich, J. J. (1987). Predicting chaotic time series. Physical Review Letters, 59, 845–848.CrossRefGoogle Scholar
  20. Franses, P. H., Paap, R., & Vroomen, B. (2004). Forecasting unemployment using an autoregression with censored latent effects parameters. International Journal of Forecasting, 20, 255–271.CrossRefGoogle Scholar
  21. Gencay, R., & Dechert, W. D. (1992). An algorithm for the n Lyapunov exponents of an n-dimensional unknown dynamical system. Physica D, 59, 142–157.CrossRefGoogle Scholar
  22. Golan, A., & Perloff, J. M. (2004). Superior forecasts of the U.S. unemployment rate using a nonparametric method. The Review of Economic and Statistics, 86(1), 433–438.CrossRefGoogle Scholar
  23. Granger, C. W. J., & Teräsvirta, T. (1993). Modelling Noninear Economic Relationships. Oxford: Oxford University Press.Google Scholar
  24. Guhathakurta, K., Bhattacharya, B., & Chowdhury, A. R. (2010). Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes. Physica A-Statistical Mechanics and its Applications, 389(9), 1874–1882.CrossRefGoogle Scholar
  25. Hallegatte, S., Ghil, M., & Dumas, P. (2008). Business cycles, bifurcations and chaos in a neo-classical model with investment dynamics. Journal of Economic Behavior and Organization, 67(1), 57–77.CrossRefGoogle Scholar
  26. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series subject to changes in regime. Econometrica, 57, 357–384.CrossRefGoogle Scholar
  27. Hansen, B. E. (1997). Inference in TAR models. Studies in Nonlinear Dynamics and Econometrics, 2, 1–14.CrossRefGoogle Scholar
  28. Johnes, G. (1999). Forecasting unemployment. Applied Economics Letters, 6(9), 605–607.CrossRefGoogle Scholar
  29. Jungeilges, J. A. (1996). Operational characteristics of White’s test. In W. A. Barnett, A. P. Kirman, & M. Salmon Nonlinear (Eds.), dynamics and economics. Cambridge: Cambridge University Press : Cambridge.Google Scholar
  30. Kantz, H., & Schreiber, T. (1994). Nonlinear time series analysis. Cambridge: Cambridge University Press.Google Scholar
  31. Kaplan, D. T. (1994). Exceptional events as evidence for determinism. Physica D, 73, 38–48.CrossRefGoogle Scholar
  32. Kaplan, D. T. (1995). Nonlinearity and nonstationarity: The use of surrogate data in interpreting fluctuations. Center for Nonlinear Dynamics and Dept. of Physiology, Montreal, Québec, Canada: McGill University.Google Scholar
  33. Kodera, J., & Tran, V. Q. (2009). Visual Recurrence Analysis and its application on the Czech Stock Market. Politicka Ekonomie, 57(3), 305–322.Google Scholar
  34. Koller, W., & Fischer, M. M. (2002). Testing for non-linear dependence in univariate time series. An empirical investigation of the Austrian unemployment rate. Networks and Spatial Economics, 2, 191–209.Google Scholar
  35. Koop, G., & Potter, S. M. (1999). Dynamic asymmetries in U.S. unemployment. Journal of Business and Economic Statistic, 17(3), 298–312.Google Scholar
  36. Kyrtsou, C., Malliaris, A. G., & Serletis, A. (2009). Energy sector pricing: On the role of neglected nonlinearity. Energy Economics, 31(3), 492–502.CrossRefGoogle Scholar
  37. Kyrtsou, C., & Terraza, M. (2010). Seasonal Mackey-Glass-GARCH process and short-term dynamics. Empirical Economics, 38, 325–345.CrossRefGoogle Scholar
  38. Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20(2), 130–141.CrossRefGoogle Scholar
  39. McQueen, G., & Thorley, D. (1993). Asymmetric business cycle turning points. Journal of Monetary Economics, 31, 341–363.CrossRefGoogle Scholar
  40. Mikhail, O., Eberwein, C. J., & Handa, J. (2005). On the evidence of non-linear structure in Canadian unemployment. Applied Economic Letters, 12(2), 101–104.CrossRefGoogle Scholar
  41. Milas, C., & Rothman, P. (2008). Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts. International Journal of Forecasting, 24, 101–121.CrossRefGoogle Scholar
  42. Montgomery, A. L., Zarnowithz, V., Tsay, R. S., & Tiao, G. C. (1998). Forecasting the US unemployment rate. Journal of American Statistical Association, 93, 478–493.CrossRefGoogle Scholar
  43. Mortensen, D. (1999). Equilibrium unemployment dynamics. International Economic Review, 40, 889–914.CrossRefGoogle Scholar
  44. Mortensen, D., & Pissarides, C. (1994). Job creation and job destruction in the theory of unemployment rate. Review of Economic Studies, 61, 397–415.CrossRefGoogle Scholar
  45. Moshiri, S., & Brown, L. (2004). Unemployment variation over the business cycles: A comparison of forecasting models. Journal of Forecasting, 23, 497–511.CrossRefGoogle Scholar
  46. Neftçi, S. N. (1984). Are economic time series asymmetric over the business cycle? Journal of Political Economy, 92, 307–328.CrossRefGoogle Scholar
  47. Neugart, M. (2004). Complicated dynamics in a flow model of the labour market. Journal of Economic Behavior and Organization, 53, 193–213.CrossRefGoogle Scholar
  48. Nychka, D., Ellner, S., Gallant, A. R., & McCaffrey, D. (1992). Finding chaos in noisy systems. Journal of the Royal Statistical Society B, 54(2), 399–426.Google Scholar
  49. Olmedo, E. (2011). Is there chaos in the Spanish labour market. Chaos, Solitons and Fractals, 44, 1045–1053.Google Scholar
  50. Panagiotidis, T. (2002). Testing the assumption of linearity. Economics Bulletin, 3(29), 1–9.Google Scholar
  51. Panagiotidis, T., & Pelloni, G. (2007). Nonlinearity in the Canadian and US labor markets: Univariate and multivariate evidence from a battery of tests. Macroeconomic Dynamics, 11(5), 613–667.CrossRefGoogle Scholar
  52. Peel, D. A., & Speight, A. E. H. (2000). Threshold nonlinearities in unemployment rates: Further evidence for the UK and G3 economies. Applied Economics, 32(6), 705–715.CrossRefGoogle Scholar
  53. Rothman, P. (1991). Forecasting asymmetric unemployment rates. Review of Economic Statistics, 80, 164–168.Google Scholar
  54. Rothman, P. (1998). Further evidence on the asymmetric behaviour of unemployment rates over the bysiness cycle. Journal of Macroeconomics, 13, 291–298.CrossRefGoogle Scholar
  55. Sichel, D. E. (1993). Business cycle asymmetry: A deeper look. Economic Inquiry, 31, 224–236.CrossRefGoogle Scholar
  56. Skalin, J., & Teräsvirta, T. (2002). Modelling asymmetries moving equilibria in unemployment rates. Macroeconomic Dynamics, 6(2), 202–241.CrossRefGoogle Scholar
  57. Soliman, A. S. (1996). Transitions from stable equilibrium points to periodic cycles to chaos in a Phillips Curve System. Journal of Macroeconomics, 18(1), 139–153.CrossRefGoogle Scholar
  58. Sugihara, G., & May, R. M. (1990). Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344, 734–741.CrossRefGoogle Scholar
  59. Takens, F. (1981). Detecting strange attractors in turbulence. In D. A. Rand & L.-S. Young (Eds.), Dynamical systems and turbulence. Lecture Notes in nathematics (Vol. 898, pp. 366–381). New York: Springer.Google Scholar
  60. Tramontana, F., Gardini, L., & Ferri, P. (2010). The dynamics of the NAIRU model with two switching regimes. Journal of Economic Dynamics and Control, 34(4), 681–695.CrossRefGoogle Scholar
  61. Tong, H. (1990). Non-linear time series: A dynamical systems approach. Oxford: Oxford University Press.Google Scholar
  62. Van Dijk, D., Teräsvirta, T., & Franses, P. H. (2000). Smooth transition autoregressive models—a survey of recent developments. Working Paper Series in Economics and Finance 380, Stockholm School of Economics.Google Scholar
  63. Van Dijk, D., Franses, P. H., & Paap, R. (2002). A nonlinear long memory model, with an application to US unemployment. Journal of Econometrics, 110, 135–165.CrossRefGoogle Scholar
  64. Wang, H., Chen, G., & Lü, J. (2004). Complex dynamical behaviors of daily data series in stock exchange. Physics Letters A, 333, 246–255.CrossRefGoogle Scholar
  65. White, A. (1989). Some asymptotic results for learning in single hidden-layer feedforward network models. Journal of the American Statistical Association, 84(408), 1003–1013.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Departamento de Economía Aplicada IUniversidad de SevillaSevillaSpain

Personalised recommendations