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Bayesian econometrics / edited by Siddhartha Chib ... [et al.] Bingley : Emerald Group Publishing Limited, 2009.672 p. : ill. Highlights various aspects of inference and computations. This book presents suggestions for improving Markov chain Monte Carlo computational strategies. It includes such topics as potential problem of improper posterior densities in a variety of dynamic models, and selection and averaging for forecasting with vector auto regressions. "Bayesian Econometrics" illustrates the scope and diversity of modern applications, reviews some recent advances, and highlights many desirable aspects of inference and computations. It begins with an historical overview by Arnold Zellner who describes key contributions to development and makes predictions for future directions. In the second paper, Giordani and Kohn makes suggestions for improving Markov chain Monte Carlo computational strategies. The remainder of the book is categorized according to micro econometric and time-series modeling. Models considered include an endogenous selection ordered probit model, a censored treatment-response model, equilibrium job search models and various other types. These are used to study a variety of applications for example dental insurance and care, educational attainment, voter opinions and the marketing share of various brands and an aggregate cross-section production function. Models and topics considered include the potential problem of improper posterior densities in a variety of dynamic models, selection and averaging for forecasting with vector auto regressions, a consumption capital-asset pricing model and various others. Applications involve U.S. macroeconomic variables, exchange rates, an investigation of purchasing power parity, data from London Metals Exchange, international automobile production data, and data from the Asian stock market.
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