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Approximate Nonlinear Forecasting Methods

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  • White, Halbert

Abstract

We review key aspects of forecasting using nonlinear models. Because economic models are typically misspecified, the resulting forecasts provide only an approximation to the best possible forecast. Although it is in principle possible to obtain superior approximations to the optimal forecast using nonlinear methods, there are some potentially serious practical challenges. Primary among these are computational difficulties, the dangers of overfit, and potential difficulties of interpretation. In this chapter we discuss these issues in detail. Then we propose and illustrate the use of a new family of methods (QuickNet) that achieves the benefits of using a forecasting model that is nonlinear in the predictors while avoiding or mitigating the other challenges to the use of nonlinear forecasting methods.

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This chapter was published in:

  • G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1, January.
    This item is provided by Elsevier in its series Handbook of Economic Forecasting with number 1-09.

    Handle: RePEc:eee:ecofch:1-09

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    Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description

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    Cited by:
    1. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(1), pages 29-56, Winter.
    2. Davide Pettenuzzo & Halbert White, 2010. "Granger Causality, Exogeneity, Cointegration, and Economic Policy Analysis," Working Papers 36, Brandeis University, Department of Economics and International Businesss School.
    3. Lu, Xun & White, Halbert, 2014. "Robustness checks and robustness tests in applied economics," Journal of Econometrics, Elsevier, vol. 178(P1), pages 194-206.
    4. González, Andrés & Teräsvirta, Timo, 2006. "Modelling autoregressive processes with a shifting mean," Working Paper Series in Economics and Finance 637, Stockholm School of Economics, revised 22 May 2007.
    5. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    6. Gradojevic, Nikola, 2007. "The microstructure of the Canada/U.S. dollar exchange rate: A robustness test," Economics Letters, Elsevier, vol. 94(3), pages 426-432, March.
    7. Enders, Walter & Holt, Matthew T., 2011. "Breaks, bubbles, booms, and busts: the evolution of primary commodity price fundamentals," MPRA Paper 31461, University Library of Munich, Germany.
    8. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378.
    9. González, Andrés & Hubrich, Kirstin & Teräsvirta, Timo, 2011. "Forecasting inflation with gradual regime shifts and exogenous information," Working Paper Series 1363, European Central Bank.
    10. Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies," CEIS Research Paper 255, Tor Vergata University, CEIS, revised 08 Nov 2012.
    11. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, School of Economics and Management, University of Aarhus.

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