  Fixed effects models are very flexible because they do not make assumptions
on the distribution of effects and can also be used if the heterogeneity
component is correlated with explanatory variables. A disadvantage is the large
number of effects that have to be estimated. A recursive partitioning (or tree
based) method is proposed that identifies clusters of units that share the same
effect. The approach reduces the number of parameters to be estimated and is
useful in particular if one is interested in identifying clusters with the same
effect on a response variable. It is shown that the method performs well and
outperforms competitors like the finite mixture model in particular if the
heterogeneity component is correlated with explanatory variables. In two
applications the usefulness of the approach to identify clusters that share the
same effect is illustrated.
