Approximate Nonlinear Forecasting Methods

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Approximate Nonlinear Forecasting Methods

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White, Halbert

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Halbert White

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.) , Elsevier, chapter 09, pages 459-512, 2006.

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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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)
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B0 - Schools of Economic Thought and Methodology - - General

Cited by:
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Timo Terasvirta & Andrés González, 2006. "Modelling autoregressive processes with a shifting mean," BORRADORES DE ECONOMIA 003230, BANCO DE LA REPÚBLICA. [Downloadable!]
Other versions:
- 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.
- Timo Terasvirta & Andrés González, . "Modelling autoregressive processes with a shifting mean," Borradores de Economia 420, Banco de la Republica de Colombia. [Downloadable!]
- Andrés González & Timo Teräsvirta, 2008. "Modelling Autoregressive Processes with a Shifting Mean," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 12(1). [Downloadable!]
Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
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!]
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. [Downloadable!]
Other versions:
- Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
Jin Seo Cho & Meng Huang & Halbert White, 2009. "Testing for a Constant Mean Function using Functional Regression," Discussion Paper Series 0915, Institute of Economic Research, Korea University. [Downloadable!]

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