Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book 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 that readers have 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. 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. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.
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The Normal Linear Regression Model with Natural Conjugate
The Normal Linear Regression Model with Other Priors
The Nonlinear Regression Model
9 other sections not shown
approximation assumption Bayes factor Bayesian analysis Bayesian econometrics Bayesian inference Bayesian methods Bayesian model averaging calculate carry out Bayesian choice convergence defined denoted dependent variable discussed econometrician empirical Bayes methods empirical illustration equations estimate explanatory variables frequentist Gamma Gibbs sampler Hence hierarchical prior HPDI hyperparameters implies importance sampling independent Normal-Gamma prior informative prior instance intercept involves latent data level model likelihood function linear regression model marginal likelihood Metropolis-Hastings algorithm mixtures of Normals model comparison Monte Carlo integration multinomial probit multinomial probit model natural conjugate prior noninformative prior nonlinear regression nonparametric regression Normal distribution Normal linear regression notation numerical standard error parameters partial linear model Poirier posterior inference posterior mean posterior odds ratio posterior results posterior simulation previous chapters prior information probit model random draws random variables reader regression coefficients replications researcher Savage-Dickey density ratio scalar Section space model standard deviations Theorem tobit values variance vector