  This paper is devoted to the parametric estimation of a shift together with
the nonparametric estimation of a regression function in a semiparametric
regression model. We implement a very efficient and easy to handle
Robbins-Monro procedure. On the one hand, we propose a stochastic algorithm
similar to that of Robbins-Monro in order to estimate the shift parameter. A
preliminary evaluation of the regression function is not necessary to estimate
the shift parameter. On the other hand, we make use of a recursive
Nadaraya-Watson estimator for the estimation of the regression function. This
kernel estimator takes into account the previous estimation of the shift
parameter. We establish the almost sure convergence for both Robbins-Monro and
Nadaraya--Watson estimators. The asymptotic normality of our estimates is also
provided. Finally, we illustrate our semiparametric estimation procedure on
simulated and real data.
