  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 coincide 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 in 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.
