Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semiparametric Time Series Models with Log-concave Innovations: Maximum
  Likelihood Estimation and its Consistency   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.

24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom