Yasuo Amemiya Abstract Random effect analysis for panel data is considered when some explanatory variables are measured with error. 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. Simulation results demonstrating the efficiency and usefulness of the new procedure are presented. Copies of preprints are available from the author upon request.