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Nonparametric identification of nonlinear time series: Selecting significant lags. (English) Zbl 0813.62037
Summary: We suggest a nonparametric procedure for selecting significant lags in the model description of a general nonlinear stationary time series. The procedure can be applied to both the conditional mean and the conditional variance and is valid for heteroscedastic series. The procedure is illustrated by simulations and sunspot data, lynx data, and blowfly data are analyzed. It is indicated that projectors can be used in conjunction with the procedure for selecting significant lags to check the adequacy of an adaptive time series model.

MSC:
62G07 Density estimation
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62G99 Nonparametric inference
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