This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Forecasting economic variables with nonlinear models

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Terasvirta, Timo

Additional information is available for the following registered author(s):

Abstract

The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are introduced and their properties discussed. These include the smooth transition regression model, the switching regression model whose univariate counterpart is called threshold autoregressive model, the Markov-switching or hidden Markov regression model, the artificial neural network model, and a couple of other models. Many of these nonlinear models nest a linear model. For this reason, it is advisable to test linearity before estimating the nonlinear model one thinks will fit the data. A number of linearity tests are discussed. These form a part of model specification: the remaining steps of nonlinear model building are parameter estimation and evaluation that are also briefly considered. There are two possibilities of generating forecasts from nonlinear models. Sometimes it is possible to use analytical formulas as in linear models. In many other cases, however, forecasts more than one periods ahead have to be generated numerically. Methods for doing that are presented and compared. The accuracy of point forecasts can be compared using various criteria and statistical tests. Some of these tests have the property that they are not applicable when one of the two models under comparison nests the other one. Tests that have been developed in order to work in this situation are described. The chapter also contains a simulation study showing how, in some situations, forecasts from a correctly specified nonlinear model may be inferior to ones from a certain linear model. There exist relatively large studies in which the forecasting performance of nonlinear models is compared with that of linear models using actual macroeconomic series. Main features of some such studies are briefly presented and lessons from them described. In general, no dominant nonlinear (or linear) model has emerged.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/B7P5J-4JSMTWJ-C/2/2e2dfab663a32700e2c0d694c5c5e905
File Format:
File Function:
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Publisher Info
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
This chapter was published in: G. Elliott & C. Granger & A. Timmermann (ed.) , Elsevier, chapter 08, pages 413-457, 2006.

This item is provided by Elsevier in its series Handbook of Economic Forecasting with number 1-08.

Handle: RePEc:eee:ecofch:1-08

Contact details of provider:
Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description

For technical questions regarding this item, or to correct its listing, contact: (Heidi Boesdal).

Related research
This chapter was published in the following book, which is listed on IDEAS:
G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1, September. [Downloadable!] (restricted)
Keywords:

Other versions of this item:

Find related papers by JEL classification:
B0 - Schools of Economic Thought and Methodology - - General

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. Henry, Olan T & Olekalns, Nilss & Summers, Peter M, 2001. "Exchange Rate Instability: A Threshold Autoregressive Approach," The Economic Record, The Economic Society of Australia, vol. 77(237), pages 160-66, June. [Downloadable!] (restricted)
  2. Granger, Clive W.J. & Machina, Mark J., 2006. "Forecasting and Decision Theory," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  3. Corradi, V. & Swanson, N.R., 2000. "A Consistent Test for Nonlinear Out of Sample Predictive Accuracy," Discussion Papers 00/12, University of Exeter, School of Business and Economics.
    Other versions:
  4. repec:att:wimass:1919997 is not listed on IDEAS
  5. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May. [Downloadable!] (restricted)
    Other versions:
  6. B. Siliverstovs & D.J. Van Dijk, 2003. "Forecasting industrial production with linear, nonlinear and structural change models," Econometric Institute Report 321, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
    Other versions:
  7. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183. [Downloadable!] (restricted)
    Other versions:
  8. Boero, Gianna & Marrocu, Emanuela, 2002. "The Performance of Non-linear Exchange Rate Models: A Forecasting Comparison," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 513-42, November.
  9. James Ramsey, 1996. "If Nonlinear Models Cannot Forecast, What Use Are They?," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 1(2), pages 65-86. [Downloadable!] (restricted)
  10. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253. [Downloadable!] (restricted)
  11. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November. [Downloadable!] (restricted)
    Other versions:
  12. Venetis, Ioannis A. & Paya, Ivan & Peel, David A., 2003. "Re-examination of the predictability of economic activity using the yield spread: a nonlinear approach," International Review of Economics & Finance, Elsevier, vol. 12(2), pages 187-206. [Downloadable!] (restricted)
  13. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November. [Downloadable!] (restricted)
    Other versions:
  14. Brown, Bryan W & Mariano, Roberto S, 1984. "Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System," Econometrica, Econometric Society, vol. 52(2), pages 321-43, March. [Downloadable!] (restricted)
  15. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2002. "Estimation and model selection based inference in single and multiple threshold models," Journal of Econometrics, Elsevier, vol. 110(2), pages 319-352, October. [Downloadable!] (restricted)
  16. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March. [Downloadable!] (restricted)
    Other versions:
  17. Dick van Dijk & Timo Teräsvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models - A Survey Of Recent Developments," Econometric Reviews, Taylor and Francis Journals, vol. 21(1), pages 1-47. [Downloadable!] (restricted)
    Other versions:
  18. Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  19. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
  20. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA. [Downloadable!]
    Other versions:
  21. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April. [Downloadable!] (restricted)
  22. Garcia, Rene, 1998. "Asymptotic Null Distribution of the Likelihood Ratio Test in Markov Switching Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 763-88, August.
  23. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  24. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June. [Downloadable!] (restricted)
  25. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September. [Downloadable!] (restricted)
    Other versions:
  26. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March. [Downloadable!] (restricted)
  27. Norman R. Swanson & Halbert White, 1992. "A Model Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," University of California at San Diego, Economics Working Paper Series 92-39, Department of Economics, UC San Diego.
    Other versions:
  28. Bierens, Herman J, 1990. "A Consistent Conditional Moment Test of Functional Form," Econometrica, Econometric Society, vol. 58(6), pages 1443-58, November. [Downloadable!] (restricted)
    Other versions:
  29. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania. [Downloadable!]
    Other versions:
  30. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June. [Downloadable!] (restricted)
  31. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99. [Downloadable!] (restricted)
  32. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De. [Downloadable!] (restricted)
  33. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1999. "Testing parameter constancy in linear models against stochastic stationary parameters," Journal of Econometrics, Elsevier, vol. 90(2), pages 193-213, June. [Downloadable!] (restricted)
    Other versions:
  34. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199. [Downloadable!] (restricted)
    Other versions:
  35. Ser-Huang Poon & Clive W. J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  36. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics. [Downloadable!]
  37. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January. [Downloadable!] (restricted)
    Other versions:
  38. 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. [Downloadable!]
  39. 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. [Downloadable!] (restricted)
  40. De Gooijer, Jan G. & De Bruin, Paul T., 1998. "On forecasting SETAR processes," Statistics & Probability Letters, Elsevier, vol. 37(1), pages 7-14, January. [Downloadable!] (restricted)
  41. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  42. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive Density Evaluation," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
    Other versions:
  43. Inoue, Atsushi & Kilian, Lutz, 2002. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," CEPR Discussion Papers 3671, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  44. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138. [Downloadable!]
  45. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  46. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34. [Downloadable!] (restricted)
  47. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society. [Downloadable!]
  48. Robert Breunig & Serinah Najarian & Adrian Pagan, 2003. "Specification Testing of Markov Switching Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 703-725, December. [Downloadable!] (restricted)
  49. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22. [Downloadable!]
    Other versions:
  50. White, Halbert, 2006. "Approximate Nonlinear Forecasting Methods," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  51. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372. [Downloadable!] (restricted)
    Other versions:
  52. Massimiliano Marcellino, . "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
    Other versions:
  53. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    Other versions:
  54. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  55. Hansen, Bruce E, 1999. " Testing for Linearity," Journal of Economic Surveys, Blackwell Publishing, vol. 13(5), pages 551-76, December. [Downloadable!] (restricted)
  56. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December. [Downloadable!] (restricted)
  57. Pfann, Gerard A. & Schotman, Peter C. & Tschernig, Rolf, 1996. "Nonlinear interest rate dynamics and implications for the term structure," Journal of Econometrics, Elsevier, vol. 74(1), pages 149-176, September. [Downloadable!] (restricted)
    Other versions:
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Andrés González & Kirstin Hubrich & Timo Teräsvirta, 2009. "Forecasting inflation with gradual regime shifts and exogenous information," CREATES Research Papers 2009-03, School of Economics and Management, University of Aarhus. [Downloadable!]
  2. Giancarlo Bruno, 2008. "Forecasting Using Functional Coefficients Autoregressive Models," ISAE Working Papers 98, ISAE - Institute for Studies and Economic Analyses - (Rome, ITALY). [Downloadable!]
  3. David Peel & Ivan Paya & E Pavlidis, 2009. "Forecasting the Real Exchange Rate using a Long Span of Data. A Rematch: Linear vs Nonlinear," Working Papers 006075, Lancaster University Management School, Economics Department. [Downloadable!]
  4. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008. [Downloadable!]
Statistics
Access and download statistics

Did you know? You can import bibliographic info in various formats into you bibliographic tool, or just into your word processor. See under "publisher info" on each abstract page.

This page was last updated on 2009-11-6.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.