Economics - Research Publications

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    A Gibbs' Sampler for the Parameters of a Truncated Multivariate Normal Distribution
    GRIFFITHS, WILLIAM ( 2002-09)
    The inverse distribution function method for drawing randomly from normal andtruncated normal distributions is used to set up a Gibbs' sampler for the posterior densityfunction of the parameters of a truncated multivariate normal distribution. The sampler isapplied to shire level rainfall for five shires in Western Australia.
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    Including Prior Information in Probit Model Estimation
    Griffiths, William E. ; Hill, R. Carter ; O'Donnell, Christopher J. ( 2001-10)
    The effects of including different kinds of prior information in estimation of the probit model is examined within the framework of Bayesian inference. Of interest is the effect on posterior information for coefficients, probabilities and elasticities. In a model designed to explain choice between fixed and variable interest-rate mortgages, we show that using Bayesian inference to include inequality information on the signs of coefficients yields inferences about probabilities and elasticities that are substantially different from those obtained using maximum likelihood estimation. In a second model, concerned with state voting behavior, we find that putting prior information on probabilities, rather than coefficients, has a dramatic effect on the posterior density functions for the model coefficients, probabilities and elasticities.
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    Bayesian inference in the seemingly unrelated regressions model
    Griffiths, William E. ( 2001-04)
    Zellner's idea of combining several equations into one model to improve estimation efficiency (Zellner 1962) ranks as one of the most successful and lasting innovations in the history of econometrics. The resulting seemingly unrelated regressions (SUR)model has generated a wealth of both theoretical and empirical contributions. With the recent explosion of literature on MCMC techniques, Bayesian inference in the SUR model has become a practical reality. However, it is the author's view that, prior to the writing of this chapter, the relevant results have not been collected and summarised in a form convenient for applied researchers to implement. It is my hope this chapter will facilitate and motivate many more applications of Bayesian inference in the SUR m