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Non-linear relation between industrial production and business surveys data

  • Bruno, Giancarlo

n this paper I compare different models, a linear and a non-linear one, for forecasting industrial production by means of some related indicators. I claim that the difficulties associated with the correct identification of a non-linear model could be a possible cause of the often observed worse performance of non-linear models with respect to linear ones observed in the empirical literature. To cope with this issue I use a non-linear non-parametric model. The results are promising, as the forecasting performance shows a clear improvement over the linear parametric model.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 42337.

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Date of creation: Sep 2009
Date of revision:
Handle: RePEc:pra:mprapa:42337
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  1. Bruno, Giancarlo & Lupi, Claudio, 2003. "Forecasting Industrial Production and the Early Detection of Turning Points," Economics & Statistics Discussion Papers esdp03004, University of Molise, Dept. EGSeI.
  2. Öcal Nadir, 2000. "Nonlinear Models for U.K. Macroeconomic Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(3), pages 1-15, October.
  3. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  4. Tommaso Proietti & Cecilia Frale, 2011. "New proposals for the quantification of qualitative survey data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(4), pages 393-408, July.
  5. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
  6. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  7. Denise R. Osborn & Paul W. Simpson, 2000. "Forecasting UK Industrial Production Over the Business Cycle," Econometric Society World Congress 2000 Contributed Papers 1059, Econometric Society.
  8. D R Osborn & A Matas-Mir, 2003. "The Extent of Seasonal/Business Cycle Interactions in European Industrial Production," Centre for Growth and Business Cycle Research Discussion Paper Series 38, Economics, The Univeristy of Manchester.
  9. Harvill, Jane L. & Ray, Bonnie K., 2005. "A note on multi-step forecasting with functional coefficient autoregressive models," International Journal of Forecasting, Elsevier, vol. 21(4), pages 717-727.
  10. Ivan Paya & David A. Peel & Ioannis A. Venetis, 2004. "Asymmetry In The Link Between The Yield Spread And Industrial Production. Threshold Effects And Forecasting," Working Papers. Serie AD 2004-41, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  11. Carlos Robalo Marques, 2004. "Inflation Persistence: Facts or Artefacts?," Working Papers w200408, Banco de Portugal, Economics and Research Department.
  12. Jianqing Fan, 2000. "Simultaneous Confidence Bands and Hypothesis Testing in Varying-coefficient Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731.
  13. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2001. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," Econometric Institute Research Papers EI 2001-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  14. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
  15. Jagric Timotej, 2003. "A Nonlinear Approach to Forecasting with Leading Economic Indicators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-20, July.
  16. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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