Titlebook: Nonlinear Time Series; Nonparametric and Pa Jianqing Fan,Qiwei Yao Book 2003 Springer-Verlag New York 2003 Time series.econometrics.linear

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Book 2003ventions have led to the explosion in data c- lection (e.g., daily grocery sales, stock market trading, microarray data). The Internet makes big data warehouses readily accessible. Although cl- sic parametric models, which postulate global structures for underlying systems, are still very useful, la

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of . will on occasion be denoted {..}., and we will say that it is indexed by .. At times . will be linearly or totally ordered, i.e. it will have a partial ordering ≥ such that for any . and . in ., either .≥ . or .> ..

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Introduction,hastic manner, on the observations available at present. Such dependence makes it worthwhile to predict the future from its past. Indeed, we will depict the underlying dynamics from which the observed data are generated and will therefore forecast and possibly control future events. This chapter int

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Parametric Nonlinear Time Series Models,w of the fact that any statistical model is an approximation to the real world, a linear model is merely a first step in representing an unknown dynamic relationship in terms of a mathematical formula. The truth is that the world is nonlinear! Therefore, it is not surprising that there exists an abu