Presentation 2011-03-11
Estimation of Structural Changes in Nonlinear Time Series Models by using Particle Filters and the Genetic Programming
Shozo TOKINAGA, Yoshikazu IKEDA,
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Abstract(in English) This report deals with the estimation of structural changes in nonlinear time series models by using Particle Filters (PFs) and the Genetic Programming (GP) and its applications. It is assumed that the state equation A after changes is modified from current functional form C. Then, the function A is estimated by using the crossover operations of the GP. As applications, we show the conditions for the estimation for artificially generated time series, and also discuss estimation of functional forms for real tome series before and after structural changes.
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Keyword(in English) Structural changes / Particle filters / Genetic Programming / State estimation / Nonlinear time series
Paper # NLP2010-183
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Committee NLP
Conference Date 2011/3/3(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Estimation of Structural Changes in Nonlinear Time Series Models by using Particle Filters and the Genetic Programming
Sub Title (in English)
Keyword(1) Structural changes
Keyword(2) Particle filters
Keyword(3) Genetic Programming
Keyword(4) State estimation
Keyword(5) Nonlinear time series
1st Author's Name Shozo TOKINAGA
1st Author's Affiliation Graduate School of Economics, Kyushu University()
2nd Author's Name Yoshikazu IKEDA
2nd Author's Affiliation Faculty of Economics and Business administration, The University of Kitakyushu
Date 2011-03-11
Paper # NLP2010-183
Volume (vol) vol.110
Number (no) 465
Page pp.pp.-
#Pages 6
Date of Issue