Generalized Heteroskedasticity ACF for Moving Average Models in Explicit Forms

Samir Khaled Safi

Abstract


The autocorrelation function (ACF) measures the correlation between observations at different   distances apart. We derive explicit equations for generalized heteroskedasticity ACF for moving average of order q, MA(q). We consider two cases: Firstly: when the disturbance term follow the general covariance matrix structure Cov(wi, wj)=S with si,j¹ 0 " i¹j . Secondly: when the diagonal elements of S are not all identical but sij = 0 " i¹j, i.e. S=diag(s11, s22,…,stt). The forms of the explicit equations depend essentially on the moving average coefficients and covariance structure of the disturbance terms.

 


Keywords


Heteroscedasticity, Homoscedasticity, Autocorrelation, Moving Average, Covariance.

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DOI: http://dx.doi.org/10.18187/pjsor.v9i4.644

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Title

Generalized Heteroskedasticity ACF for Moving Average Models in Explicit Forms

Keywords

Heteroscedasticity, Homoscedasticity, Autocorrelation, Moving Average, Covariance.

Description

The autocorrelation function (ACF) measures the correlation between observations at different   distances apart. We derive explicit equations for generalized heteroskedasticity ACF for moving average of order q, MA(q). We consider two cases: Firstly: when the disturbance term follow the general covariance matrix structure Cov(wi, wj)=S with si,j ¹ 0 " i¹j . Secondly: when the diagonal elements of S are not all identical but sij = 0 " i¹j, i.e. S=diag(s11, s22,…,stt). The forms of the explicit equations depend essentially on the moving average coefficients and covariance structure of the disturbance terms.

 


Date

2014-02-06

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 9 No. 4, 2013



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810