Bayesian EconometricsBayesian 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 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. 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. |
Contents
The Normal Linear Regression Model with Natural Conjugate | 15 |
4 | 59 |
1 | 89 |
Copyright | |
15 other sections not shown
Common terms and phrases
assumption Bayes factor Bayesian analysis Bayesian inference Bayesian methods Bayesian model averaging calculate carry out Bayesian CRUZ The University denoted dependent variable discussed empirical Bayes empirical Bayes methods empirical illustration equation estimate explanatory variables frequentist Gamma Gibbs sampler Hence heteroskedasticity hierarchical prior HPDI hyperparameters implies importance sampling independent Normal-Gamma prior individual effects model informative prior intercept involves level model likelihood function linear regression model M₁ marginal likelihood Metropolis-Hastings algorithm model comparison Monte Carlo integration multinomial probit natural conjugate prior noninformative prior nonlinear nonlinear regression nonparametric regression Normal distribution Normal linear regression notation parameters Poirier posterior inference posterior mean posterior odds ratio posterior results posterior simulator previous chapters prior information probit model random variables regression coefficients replications researcher Savage-Dickey density ratio Section space models ẞ and h standard deviations Statistical stochastic frontier model Student-t errors Theorem values variance vector y₁