Greetings, Fellow R-ians: I'm working with a few different quasi-likelihood formulations for some data I'm analyzing. I'd like to implement the Information Matrix Test (see, e.g., White, 1982, or Lancaster, 1984) for each of them to determine which of the models is more likely. Since the null distribution of the test statistic is chi-squared, I envision calculating the ordinate of the sample value to give P(data|model) and using those values (and a discrete uniform prior on the model-space) to calculate Bayes Factors.
Is there any pre-existing package which performs this test in R? Halbert White, "Maximum Likelihood Estimation of Misspecified Models," Econometrica Vol. 50, No. 1, (Jan., 1982) pp.1-26 Tony Lancaster, "The Covariance Matrix of the Information Matrix Test," Econometrica, Vol 52, No.4, (July, 1984), pp.1051-1054 I would be grateful for any suggestions. Many Thanks. Charles Annis, P.E. [EMAIL PROTECTED] phone: 561-352-9699 eFAX: 503-217-5849 http://www.StatisticalEngineering.com ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
