Bayesian Econometrics (Advances in Econometrics, 23). This item is unavailable.Bayesian Econometrics (Advances in Econometrics, 23)

"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 microeconometric 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 autoregressions, 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.

Bayesian Econometrics introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self–contained and does not require previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. It includes numerous numerical examples and topics covered in the book include:

• the regression model (and variants applicable for use with panel data
• time series models
• models for qualitative or censored data
• nonparametric methods and Bayesian model averaging.

A website containing computer programs and data sets to help the student develop the computational skills of modern Bayesian econometrics can be found at: www.wiley.co.uk/koopbayesian