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Erschienen in:
How Accurate Can Instrumental Variable Models Become? Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

12. Automatic Selection for Non-linear Models

verfasst von : Jennifer L. Castle, David F. Hendry

Erschienen in: System Identification, Environmental Modelling, and Control System Design

Verlag: Springer London

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Abstract

Our strategy for automatic selection in potentially non-linear processes is: test for non-linearity in the unrestricted linear formulation; if that test rejects, specify a general model using polynomials, to be simplified to a minimal congruent representation; finally select by encompassing tests of specific non-linear forms against the selected model. Non-linearity poses many problems: extreme observations leading to non-normal (fat-tailed) distributions; collinearity between non-linear functions; usually more variables than observations when approximating the non-linearity; and excess retention of irrelevant variables; but solutions are proposed. A returns-to-education empirical application demonstrates the feasibility of the non-linear automatic model selection algorithm Autometrics.

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Vorheriges Kapitel On Application of State Dependent Parameter Models in Electrical Demand Forecast
Nächstes Kapitel Construction of Radial Basis Function Networks with Diversified Topologies
Fußnoten
1
p-values shown in brackets.
 
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Metadaten
Titel
Automatic Selection for Non-linear Models
verfasst von
Jennifer L. Castle
David F. Hendry
Copyright-Jahr
2012
Verlag
Springer London
Buch
System Identification, Environmental Modelling, and Control System Design

Print ISBN: 978-0-85729-973-4

Electronic ISBN: 978-0-85729-974-1

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-0-85729-974-1_12

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