Zellner A 1971 An Introduction To Bayesian Inference In Econometrics
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An Introduction to Bayesian Inference in Econometrics
Author: Arnold Zellner
language: en
Publisher: New York : J. Wiley
Release Date: 1971-11-26
Remarks on inference in economics; Principles of bayesian analysis with selected applications; The univariate normal linear regression model; Special problems in regression analysis; On error in the variables; Analysis of single equation nonlinear models; Time series models: some selected examples; Multivariate regression models; Simultaneous equation econometric models; On comparing and testing hypotheses; Analysis of some control problems.
Bayesian Econometrics
Author: Siddhartha Chib
language: en
Publisher: Emerald Group Publishing
Release Date: 2008-12-18
Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
Introduction to Bayesian Econometrics
Author: Edward Greenberg
language: en
Publisher: Cambridge University Press
Release Date: 2012-11-12
This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.