Nonparametric ARCH with additive mean and multiplicative volatility: a new estimation procedure
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Motivated by the misspecification problem in time series analysis, the nonparametric approach has
quickly developed in the latest years. First models in literature were focused on the estimation of
the conditional mean. It is well known that alongside the conditional mean it is important to study
the series volatility (conditional variance). The following paper deals with nonparametric autoregression
with multiplicative volatility and additive mean as studied by Yang et al. (1999). A new estimation
procedure is here provided. The procedure uses the residual-based estimator, backfitting
algorithm and the local polynomial estimation. Some applications with simulated and real data will
be presented.
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