Linear models with error components are widely used to analyse panel d
ata. Some applications of these models require knowledge of the probab
ility densities of the error components. Existing methods handle this
requirement by assuming that the densities belong to known parametric
families of distributions (typically the normal distribution). This pa
per shows how to carry out nonparametric estimation of the densities o
f the error components, thereby avoiding the assumption that the densi
ties belong to known parametric families. The nonparametric estimators
are applied to an earnings model using data from the Current Populati
on Survey. The model's transitory error component is not normally dist
ributed. Use of the nonparametric density estimators yields estimates
of the probability that individuals with row earnings will become high
earners in the future that are much lower than the estimates obtained
under the assumption of normally distributed error components.