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Non-linear relation between industrial production and business surveys data

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  • Bruno, Giancarlo

Abstract

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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Bibliographic Info

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
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Handle: RePEc:pra:mprapa:42337

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Keywords: Forecasting; Business Surveys; Non-linear time-series models; Non-parametric models;

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  1. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Ioannis A. Venetis & David A. Peel & Ivan Paya, 2004. "Asymmetry in the link between the yield spread and industrial production: threshold effects and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 23(5), pages 373-384.
  3. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 16(2), pages 254-59, April.
  4. Tommaso Proietti & Cecilia Frale, 2011. "New proposals for the quantification of qualitative survey data," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 30(4), pages 393-408, July.
  5. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0169, National Bureau of Economic Research, Inc.
  6. Bruno Giancarlo & Lupi Claudio, 2001. "Forecasting Industrial Production and the Early Detection of Turning POints," ISAE Working Papers, ISTAT - Italian National Institute of Statistics - (Rome, ITALY) 20, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
  7. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, Elsevier, vol. 20(2), pages 321-342.
  8. 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, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute EI 2001-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  9. Carlos Robalo Marques, 2005. "Inflation persistence: facts or artefacts?," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department, Banco de Portugal, Economics and Research Department.
  10. 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, Elsevier, vol. 16(2), pages 207-227.
  11. Jagric Timotej, 2003. "A Nonlinear Approach to Forecasting with Leading Economic Indicators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, De Gruyter, vol. 7(2), pages 1-20, July.
  12. Simpson, Paul W & Osborn, Denise R & Sensier, Marianne, 2001. "Forecasting UK Industrial Production over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 20(6), pages 405-24, September.
  13. Öcal Nadir, 2000. "Nonlinear Models for U.K. Macroeconomic Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, De Gruyter, vol. 4(3), pages 1-15, October.
  14. Harvill, Jane L. & Ray, Bonnie K., 2005. "A note on multi-step forecasting with functional coefficient autoregressive models," International Journal of Forecasting, Elsevier, Elsevier, vol. 21(4), pages 717-727.
  15. 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, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731.
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