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Maximum likelihood drift estimation for a threshold diffusion
Author(s) -
Lejay Antoine,
Pigato Paolo
Publication year - 2020
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12417
Subject(s) - mathematics , estimator , ergodic theory , stochastic differential equation , brownian motion , m estimator , weak convergence , diffusion , constant (computer programming) , diffusion process , statistics , statistical physics , mathematical analysis , physics , knowledge management , computer security , innovation diffusion , computer science , asset (computer security) , thermodynamics , programming language
We study the maximum likelihood estimator of the drift parameters of a stochastic differential equation, with both drift and diffusion coefficients constant on the positive and negative axis, yet discontinuous at zero. This threshold diffusion is called drifted oscillating Brownian motion. For this continuously observed diffusion, the maximum likelihood estimator coincides with a quasi‐likelihood estimator with constant diffusion term. We show that this estimator is the limit, as observations become dense in time, of the (quasi)‐maximum likelihood estimator based on discrete observations. In long time, the asymptotic behaviors of the positive and negative occupation times rule the ones of the estimators. Differently from most known results of the literature, we do not restrict ourselves to the ergodic framework: indeed, depending on the signs of the drift, the process may be ergodic, transient, or null recurrent. For each regime, we establish whether or not the estimators are consistent; if they are, we prove the convergence in long time of the properly rescaled difference of the estimators towards a normal or mixed normal distribution. These theoretical results are backed by numerical simulations.