Maximum likelihood estimation in the random coefficient regression model via the EM algorithm
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Maximum likelihood estimates in a random coefficient regression (RCR) model from cross-sectional and time series sample data are presented, within the framework of expectation-maximization (EM) algorithm Unlike the model considered by Swamy (1970), the full rank assumption of the design matrix is not assumed in this research. A simulation study is performed to compare computational feasibility, in terms of CPU time, of the EM algorithm versus PROC MIXED in SAS/STAT®. Efficiencies of estimators using homoscedastic and heteroscedastic models are also compared. The RCR model is applied to the Ernst & Young/University of Michigan Individual Taxpayer Panel data to obtain the maximum likelihood estimates, and the results are compared to previously existing works. Some advantages and limitations of the EM algorithm are also discussed.