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We argue for the adoption of a predictive approach to model specification. Specifically , we derive the difference between means and the ratio of determinants of covari-ance matrices when a subset of explanatory variables is included or excluded from a regression. For several special cases these measures are shown to be related to widely used tools for(More)
We analyze a semiparametric model for data that suffer from the problems of incidental truncation, where some of the data are observed for only part of the sample with a probability that depends on a selection equation, and of endogeneity, where a covariate is correlated with the disturbance term. The introduction of nonparametric functions in the model(More)
The purpose of this article is to report the results of a comparison between the educational climate of a baccalaureate nursing program and that of a Nursing Workforce Diversity Grant. Fifteen Hispanic/Latino and American Indian upper-division baccalaureate nursing students completed 37 questionnaires. A 76-item questionnaire was designed to measure the(More)
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the random effects and fixed effects is proposed and compared with a(More)
This paper considers a puzzle in growth theory from a Keynesian perspective. If neither wage and price adjustment nor monetary policy are effective at stimulating demand, no endogenous dynamic process exists to assure that demand grows fast enough to employ a growing labor force. Yet output grows persistently over long periods, occasionally reaching(More)
In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib(More)