  Consider the semiparametric transformation model
$\Lambda_{\theta_o}(Y)=m(X)+\epsilon$, where $\theta_o$ is an unknown finite
dimensional parameter, the functions $\Lambda_{\theta_o}$ and $m$ are smooth,
$\epsilon$ is independent of $X$, and $\esp(\epsilon)=0$. We propose a
kernel-type estimator of the density of the error $\epsilon$, and prove its
asymptotic normality. The estimated errors, which lie at the basis of this
estimator, are obtained from a profile likelihood estimator of $\theta_o$ and a
nonparametric kernel estimator of $m$. The practical performance of the
proposed density estimator is evaluated in a simulation study.
