Abstract
In this chapter we present nonparametric methods and available quantlets for nonlinear modelling of univariate time series. A general nonlinear time series model for an univariate stochastic process Y t T t=1 is given by the heteroskedastic nonlinear autoregressive (NAR) process
where ξ t denotes an i.i.d. noise with zero mean and unit variance and ƒ(·) and σ(·) denote the conditional mean function and conditional standard deviation with lags i 1,... ,i m , respectively. In practice, the conditional functions ƒ(·) and σ(·) as well as the number of lags m and the lags itself i 1,... ,i m are unknown and have to be estimated.
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Härdle, W., Tschernig, R. (2000). Flexible Time Series Analysis. In: XploRe® — Application Guide. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57292-0_16
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DOI: https://doi.org/10.1007/978-3-642-57292-0_16
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