The Composite Marginal Likelihood (CML) Estimation of Panel Ordered-Response Models
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In the context of panel ordered-response structures, the current paper compares the performance of the maximum-simulated likelihood (MSL) inference approach and the composite marginal likelihood (CML) inference approach. The panel structures considered include the pure random coefficients (RC) model with no autoregressive error component, as well as the more general case of random coefficients combined with an autoregressive error component. The ability of the MSL and CML approaches to recover the true parameters is examined using simulated datasets. The results indicate that the performances of the MSL approach (with 150 scrambled and randomized Halton draws) and the simulation-free CML approach are of about the same order in all panel structures in terms of the absolute percentage bias (APB) of the parameters and econometric efficiency. However, the simulation-free CML approach exhibits no convergence problems of the type that affect the MSL approach. At the same time, the CML approach is about 5-12 times faster than the MSL approach for the simple random coefficients panel structure, and about 100 times faster than the MSL approach when an autoregressive error component is added. As the number of random coefficients increases, or if higher order autoregressive error structures are considered, one can expect even higher computational efficiency factors for the CML over the MSL approach. These results are promising for the use of the CML method for the quick, accurate, and practical estimation of panel ordered-response models with flexible and rich stochastic specifications.
At the time of publication Rajesh Paleti was at Parsons Brinckerhoff and Chandra R. Bhat was at the University of Texas at Austin.