Wild bootstrap in RCA(1) model. (English) Zbl 1248.62155

Prášková, Zuzana

Kybernetika 39, No. 1, 1-12 (2003).

Summary: A heteroskedastic autoregressive process of first order is considered where the autoregressive parameter is random and errors are allowed to be non-identically distributed. A wild bootstrap procedure to approximate the distribution of the least-squares estimator of the mean of the random parameter is proposed as an alternative to the approximation based on asymptotic normality; and the consistency of this procedure is established.

MSC:

62M10	Time series, auto-correlation, regression, etc. in statistics (GARCH)
62G09	Nonparametric statistical resampling methods
62G20	Asymptotic properties of nonparametric inference

Keywords:

random coefficient autoregression; heteroskedasticity

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