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Change point estimation for threshold autoregressive (TAR) model.

時間序列之變點鬥檻模型是一種非線性的模型。此論文探討有關該模型之參數估計,同時對其參數估計作出統計分析。我們運用了遺傳式計算機運算來估計這些參數及對其作出研究。我們利用了MDL來對比不同的變點門檻模型,同時我們也利用了MDL來選取對應的變點門檻模型。 / This article considers the problem of modeling non-linear time series by using piece-wise TAR model. The numbers of change points, the numbers of thresholds and the corresponding order of AR in each piecewise TAR segments are assumed unknown. The goal is to nd out the “best“ combination of the number of change points, the value of threshold in each time segment, and the underlying AR order for each threshold regime. A genetic algorithm is implemented to solve this optimization problem and the minimum description length principle is applied to compare various segmented TAR. We also show the consistency of the minimal MDL model selection procedure under general regularity conditions on the likelihood function. / Detailed summary in vernacular field only. / Tang, Chong Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 45-47). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 2 --- Minimum Description Length for Pure TAR --- p.4 / Chapter 2.1 --- Model selection using Minimum Description Length for Pure TAR --- p.4 / Chapter 2.1.1 --- Derivation of Minimum Description Length for Pure TAR --- p.5 / Chapter 2.2 --- Optimization Using Genetic Algorithms (GA) --- p.7 / Chapter 2.2.1 --- General Description --- p.7 / Chapter 2.2.2 --- Implementation Details --- p.9 / Chapter 3 --- Minimum Description Length for TAR models with structural change --- p.13 / Chapter 3.1 --- Model selection using Minimum Description Length for TAR models with structural change --- p.13 / Chapter 3.1.1 --- Derivation of Minimum Description Length for TAR models with structural change --- p.14 / Chapter 3.2 --- Optimization Using Genetic Algorithms --- p.17 / Chapter 4 --- Main Result --- p.20 / Chapter 4.1 --- Main results --- p.20 / Chapter 4.1.1 --- Model Selection using minimum description length --- p.21 / Chapter 5 --- Simulation Result --- p.24 / Chapter 5.1 --- Simulation results --- p.24 / Chapter 5.1.1 --- Example of TAR Model Without Structural Break --- p.24 / Chapter 5.1.2 --- Example of TAR Model With Structural Break I --- p.26 / Chapter 5.1.3 --- Example of TAR Model With Structural Break II --- p.29 / Chapter 6 --- An empirical example --- p.33 / Chapter 6.1 --- An empirical example --- p.33 / Chapter 7 --- Consistency of the CLSE --- p.36 / Chapter 7.1 --- Consistency of the TAR parameters --- p.36 / Chapter 7.1.1 --- Consistency of the estimation of number of threshold --- p.36 / Chapter 7.1.2 --- Consistency of the change point parameters --- p.43 / Bibliography --- p.45

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328552
Date January 2012
ContributorsTang, Chong Man., Chinese University of Hong Kong Graduate School. Division of Risk Management Science.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatelectronic resource, electronic resource, remote, 1 online resource (v, 47 leaves) : ill. (some col.)
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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