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A Conditional Least Squares Approach to Bilinear Time Series Estimation

Grahn, Thorsten

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Abstract

In this paper we develop a Conditional Least Squares (CLS) procedurefor estimating bilinear time series models. We apply this method to twogeneral types of bilinear models. A model of type I is a special superdiagonalbilinear model which includes the linear ARMA model as a submodel. A model oftype II is a standardized version of the popular bilinear BL(p,0,p,1) model(see e.g. Liu and Chen (1990), Sesay and Subba Rao (1991)). For both models weshow that the limiting distribution of the resulting CLS estimates is Gaussianand the law of the iterated logarithm holds.

Item Type: Working paper
Place of Publication: Heidelberg
Date Deposited: 20 Jun 2016 09:10
Date: April 1993
Number of Pages: 47
Faculties / Institutes: The Faculty of Mathematics and Computer Science > Department of Applied Mathematics
Subjects: 510 Mathematics
Uncontrolled Keywords: Estimation; bilinear time series; central limit theorem; law of the iterated logarithm; conditional moments
Schriftenreihe ID: Beiträge zur Statistik > Beiträge
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