Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency
Methodology
2018-01-30 v4
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.
Cite
@article{arxiv.1111.6291,
title = {Semiparametric Time Series Models with Log-concave Innovations: Maximum Likelihood Estimation and its Consistency},
author = {Yining Chen},
journal= {arXiv preprint arXiv:1111.6291},
year = {2018}
}
Comments
38 pages, 4 figures