A Conditional Least Squares approach to bilinear time series estimation
نویسنده
چکیده
In this paper we develop a Conditional Least Squares (CLS) procedure for estimating bilinear time series models. We apply this method to two general types of bilinear models. A model of type I is a special superdiagonal bilinear model which includes the linear ARMA model as a submodel. A model of type 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 we show that the limiting distribution of the resulting CLS estimates is Gaussian and the law of the iterated logarithm holds.
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