Semiparametric Time Series Models with Log-concave Innovations

Details Semiparametric Time Series Models with Log-concave Innovations Semiparametric Time Series Models with Log-concave Innovations

2011-11-27

arxiv.org/abs/1111.6291v2

Statistics

Methodology

43 pages, 4 figures

Scientific paper

We study semiparametric time series models with innovations following a log-concave distribution. We propose a general maximum likelihood framework which allows us to estimate simultaneously the parameters of the model and the density of the innovations. This framework can be easily adapted to many well-known models, including ARMA, GARCH and ARMA-GARCH. Furthermore, we show that the estimator under our new framework is consistent in both ARMA and ARMA-GARCH settings. We demonstrate its finite sample performance via a thorough simulation study and apply it to two real data sets concerning the streamflow of the Hirnant river and the FTSE daily return.

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