| Abstract: |
The paper compares different estimation strategies of ordered response models
in the presence of non-random unobserved heterogeneity. By running Monte Carlo
simulations with a range of randomly generated panel data of differing
cross¬sectional and longitudinal dimension sizes we assess the consistency and
efficiency of standard models such as linear fixed effects, ordered and
conditional logit and several different binary recoding procedures. Among the
analyzed binary recoding procedures is the conditional ordered logit estimator
proposed by Ferrer-i-Carbonell and Frijters (2004) that recently has gained
some popularity in the analysis of individual well-being. The
Ferrer-i-Carbonell and Frijters estimator (FCF) performs best if the number of
observations is large and the number of categories on the ordered scale is
small. However, a much simpler individual mean based binary recoding scheme
performs similarly well and even outperforms the FCF estimator if the number
of categories on the ordered scale becomes large. If the researcher is,
however, only interested in the relative size of coefficients with respect to a
baseline the easy to compute linear fixed effect model essentially delivers the
same results as the more elaborate binary recoding schemes. |