Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
A limited information estimator for the multivariate ordinal probit model is developed. The main advantage of the estimator is that even for high dimensional models, the estimation procedure requires the evaluation of bivariate normal integrals only. The proposed estimator also avoids the potential problem of encountering local maxima in the estimation process, which is looming using maximum likelihood. The performance of the limited information estimator is shown by Monte Carlo experiments to be excellent and it is comparable to that of the maximum likelihood estimator. Finally, an application of the limited information multivariate ordinal probit to model the consumption level of cigarette, alcohol and betel nut is presented.