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arXiv · 1111.6291

Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency

Yining Chen
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Abstract

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 model the daily log-return of FTSE 100 index and the rabbit population.

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Yining Chen. 2011-11-27. Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency. https://doi.org/10.1111/sjos.12092

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