Showing 91 - 100 of 600
Persistent link: https://www.econbiz.de/10005166618
Zellner (1975), Chaloner and Brant (1988), and Chaloner (1991) used the posterior distributions of the realized errors to define outliers in a linear model. The same concept is used here to define outliers in a state-space model. An effective approach to compute the posterior probabilities of...
Persistent link: https://www.econbiz.de/10005319288
In this paper we present a widely applicable definition of the predictive likelihood based on estimators that are either sufficient or approximately sufficient. Under regularity conditions, this predictive likelihood is shown to equal the Bayes prediction density up to terms of order O p(n-1)....
Persistent link: https://www.econbiz.de/10005319352
The method of Bayesian model selection for join point regression models is developed. Given a set of "K"&plus;1 join point models "M"<sub>0</sub>, "M"<sub>1</sub>, …, "M"<sub>" K"</sub> with 0, 1, …, "K" join points respec-tively, the posterior distributions of the parameters and competing models "M"<sub>"k"</sub> are computed...
Persistent link: https://www.econbiz.de/10005217072
Persistent link: https://www.econbiz.de/10005192397
Persistent link: https://www.econbiz.de/10005192662
This paper revisits the question of whether CEO compensation practices are in keeping with those justified by agency theory. We develop and analyze a new panel Tobit model, estimated by modern Bayesian methods, in which the heterogeneity of covariate effects across firms is modeled in a...
Persistent link: https://www.econbiz.de/10005194300
Persistent link: https://www.econbiz.de/10005052751
Persistent link: https://www.econbiz.de/10005115510
This paper is concerned with statistical inference in multinomial probit, multinomial-$t$ and multinomial logit models. New Markov chain Monte Carlo (MCMC) algorithms for fitting these models are introduced and compared with existing MCMC methods. The question of parameter identification in the...
Persistent link: https://www.econbiz.de/10005119186