A new semi-parametric estimator for LARCH processes

10/25/2021
by   Jean-Marc Bardet, et al.
0

This paper aims at providing a new semi-parametric estimator for LARCH(∞) processes, and therefore also for LARCH(p) or GLARCH(p, q) processes. This estimator is obtained from the minimization of a contrast leading to a least squares estimator of the absolute values of the process. The strong consistency and the asymptotic normality are showed, and the convergence happens with rate √($) n as well in cases of short or long memory. Numerical experiments confirm the theoretical results, and show that this new estimator clearly outperforms the smoothed quasi-maximum likelihood estimators or the weighted least square estimators often used for such processes.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/03/2023

Asymptotic properties of maximum likelihood estimators for determinantal point processes

We obtain the almost sure consistency and the Berry-Esseen type bound of...
research
01/08/2018

Semi-parametric detection of multiple changes in long-range dependent processes

This paper is devoted to the offline multiple changes detection for long...
research
05/15/2020

Contrast estimation of general locally stationary processes using coupling

This paper aims at providing statistical guarantees for a kernel based e...
research
03/05/2019

Tutorial: Deriving The Efficient Influence Curve for Large Models

This paper aims to provide a tutorial for upper level undergraduate and ...
research
11/06/2018

Consistency of quasi-maximum likelihood for processes with asymMetric laplacian innovation

Strong consistency of the quasi-maximum likelihood estimator is given fo...
research
04/13/2021

Bahadur efficiency of the maximum likelihood estimator and one-step estimator for quasi-arithmetic means of the Cauchy distribution

Some quasi-arithmetic means of random variables easily give unbiased str...
research
10/02/2019

Combining multiple imputation with raking of weights in the setting of nearly-true models

Raking of weights is one approach to using data from the full cohort in ...

Please sign up or login with your details

Forgot password? Click here to reset