Transcription of Lecture 2: ARMA(p,q) models (part 3)
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Lecture 2: ARMA(p,q) models (part 3)Florian PelgrinUniversity of Lausanne, Ecole des HECD epartment of mathematics (IMEA-Nice)Sept. 2011 - Jan. 2012 Florian Pelgrin (HEC)Univariate time seriesSept. 2011 - Jan. 20121 / 32 IntroductionMotivationCharacterize the main properties of ARMA(p,q) of ARMA(p,q) modelsFlorian Pelgrin (HEC)Univariate time seriesSept. 2011 - Jan. 20122 / 32 IntroductionRoad map1 ARMA(1,1) modelDefinition and conditionsMomentsEstimation2 ARMA(p,q) modelDefinition and conditionsMomentsEstimation3 Application4 AppendixFlorian Pelgrin (HEC)Univariate time seriesSept.
ARMA(1,1) model De nition and conditions 1. ARMA(1,1) 1.1. De nition and conditions De nition A stochastic process (X t) t2Z is said to be a mixture autoregressive moving average model of order 1, ARMA(1,1), if it satis es the following equation : X t = + ˚X t 1 + t + t 1 8t ( L)X t = + ( L) t where 6= 0, 6= 0, is a constant term, ( t) t2Z is ...
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