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Master's Dissertation
DOI
10.11606/D.45.2004.tde-22082007-160332
Document
Author
Full name
Joao Ricardo Sato
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2004
Supervisor
Committee
Morettin, Pedro Alberto (President)
Alves, Denisard Cneio de Oliveira
Chiann, Chang
Title in Portuguese
Processos com memória longa compartilhada
Keywords in Portuguese
ARIMA
estacionariedade
integração
memória longa
séries temporais
Abstract in Portuguese
Este trabalho tem como objetivo a avaliação de três estimadores do parâmetro de integração fracionária d e de um teste para memória longa compartilhada. Os estimadores a serem avaliados são: o estimador de Geweke e Porter-Hudak, o estimador usando o periodograma suavizado e o estimador semiparamétrico truncado de Whittle. A avaliação dos estimadores será no contexto de processos ARFIMA+ARMA, e em relação a variações nos termos autoregressivos e de médias móveis, tanto do termo de memória curta quanto do termo de memória longa. Além disso, serão introduzidos o conceito de modelos com memória longa compartilhada e um método de identificação através da análise de correlação canônica para séries temporais multivariadas proposto, por Ray e Tsay (1997). Por fim, serão apresentadas três aplicações sobre dados reais dos tópicos estudados: uma para a velocidade do vento em São Paulo e Piracicaba e outras duas para séries das bolsas de valores de Hong Kong, Nova Zelândia, Singapura, Brasil e Reino Unido
Title in English
Processes with common long memory
Keywords in English
ARIMA
integration
long memory
stationarity
time series
Abstract in English
The goal of this project is the evaluation of three long memory parameter estimators and a common long range dependence test. The estimators evaluated are: the Geweke and Porter-Hudak, the smoothed periodogram and the semiparametric truncated Whittle estimators. The evaluation is in the context of processes ARFIMA+ARMA, and related to variations in the autoregressive and moving average coefficients, both in the short and long memory terms. Furthermore, we describe common long range dependence processes and an identification approach (Ray and Tsay, 1997) for them, using the canonical correlation analysis. Finally, three applications to real data are presented: the first one to the wind's speed in the Brazilian cities of São Paulo and Piracicaba, and the other ones to financial time series of the stock markets of Hong Kong, New Zealand, Singapore, Brazil and the United Kingdom.
 
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dissertacao.pdf (43.22 Mbytes)
Publishing Date
2013-04-15
 
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