Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers
This book presents some of Arnold Zellner's outstanding contributions to the philosophy, theory and application of Bayesian analysis, particularly as it relates to statistics, econometrics and economics. The volume contains both previously published and new material which cite and discuss the work of Bayesians who have made a contribution by helping researchers and analysts in many professions to become more effective in learning from data and making decisions. This volume will be essential reading for academics and students interested in quantitative methods as well as industrial analysts and government officials.
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Professor Arnold Zellner interviewed by Peter
18911989 Institute of Mathematical
Bayesian Econometrics Econometrica 53 2 March 1985 25369
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2SLS American Statistical Association Amsterdam analysis in econometrics applied approximate AR(3)LI model ARNOLD ZELLNER assumed assumptions asymptotic Bayes Bayes's theorem Bayesian analysis Bayesian approach Bayesian Econometrics Bayesian estimation Bayesian Inference Bayesian methods computed conjugate prior const covariance matrix criterion functional data density denoted derived econometric models Econometrics and Statistics Economic employed error terms evaluated example finite sample forecasts given growth rates Harold Jeffreys hypotheses integration Jaynes Jeffreys Jeffreys's Journal of Econometrics least squares likelihood function linear loss function maxent MDIP means and regression MELO estimate minimal minimum expected loss ML estimator moments multivariate North-Holland obtain optimal parameters pdf's point estimates posterior density posterior distributions posterior expectation posterior mean posterior odds ratios posterior pdf posterior probabilities prior density prior distributions prior information probability problems procedures regression coefficients regression model relative risk functions RMSEs sampling theory side conditions solution structural coefficients Table testing University of Chicago variables variance vector