Preprint #97-16
Random effect analysis for panel data is considered when some explanatory
variables are measured with error. In some applications (e.g., economic
analysis), the covariance between the random effect and the unobservable
true explanatory variables is to be estimated and contributes to the
difficulty of the problem. Identification of model parameters given the
first two moments of observed variables is examined, and relatively
unrestrictive sufficient conditions for identification are obtained.
Estimation based on maximum normal likelihood is proposed. This method
can be easily implemented using available computer packages that perform
moment structure analysis. Compared to the only existing procedure based
on instrumental variables, the new method is shown to be more efficient and
to have much wider applicability. Standard error estimates and goodness-of-fit
statistics obtained under the assumption of normally distributed observations
are shown to be asymptotically valid for a broad class of non-normal observations.
Simulation results demonstrating the efficiency and usefulness of the new
procedure are presented.
Copies of preprints are available from the author upon request. Use
the preprint number (located at the top of the page) and make the
request directly to the author, Iowa State University,
Department of Statistics, Snedecor Hall, Ames, IA 50011-1210.