Siddhartha Chib, William Griffiths
Emerald Group Publishing, Dec 18, 2008 - Business & Economics - 672 pages
"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.
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algorithm American Statistical Association application autoregressive Bayes factors Bayesian analysis Bayesian econometrics Bayesian inference Bayesian model Chen Chib coefficients cointegration cointegration space components compute conditional density conditional posterior correlation covariance cutpoints dataset dental insurance Dijk Dirichlet process discussed draws dynamic economic effects empirical equation equilibrium search model estimation GARCH Geweke Gibbs sampler given growth heteroscedasticity income individuals input iterates joint posterior Journal of Econometrics Koop likelihood function linear marginal likelihood Markov chain matrix MCMC measurement error model averaging model selection Monte Carlo multivariate noninformative nonlinear nonparametric normal observations obtain outcomes output panel data paper parameters posterior density posterior distribution posterior means posterior model probabilities posterior probability predictive likelihood prior distribution problem proposal density regression model restrictions sampling Section simulation specification standard deviation Student-t subsidies SV model Table values variables variance vector Zellner