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

结构

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Model Validation,rm of the true underlying dynamics correctly. However, this is not always warranted and leads naturally to a nonparametric alternative hypothesis. It is clear that nonparametric models will reduce the danger of model misspecification.

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Characteristics of Time Series,erent levels to suit various practical needs. In this chapter, we introduce the most commonly used definitions for stationarity and dependence measures. We also make comments on when those definitions and measures are most relevant in practice.

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Spectral Density Estimation and Its Applications,n family of models, one wishes to verify if the family of models adequately fits a given time series by checking whether or not the residual series is a white noise process. The latter can be done by inspecting whether the estimated spectral density based on residuals is nearly a constant.

FEIGN

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Nonlinear Prediction,r prediction from linear prediction. The sensitivity to initial condition, a key concept in deterministic chaos, plays an important role in understanding nonlinearity. Three types of predictors-namely point predictors, predictive intervals, and predictive distributions-constructed based on local regression will be presented.

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