Bayesian Econometric MethodsThis volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises. |
Other editions - View all
Bayesian Econometric Methods Joshua Chan,Gary Koop,Dale J. Poirier,Justin L. Tobias Limited preview - 2019 |
Bayesian Econometric Methods Joshua Chan,Gary Koop,Dale J. Poirier,Justin L. Tobias No preview available - 2019 |
Common terms and phrases
beta distribution binomial distribution Cauchy conditional distribution continuous k-dimensional random continuous random variable covariance degrees of freedom degrees-of-freedom parameter denoted Y density Dirichlet distribution discrete random variable distribution A continuous distribution A discrete distribution Let distribution with parameter exist experiment fixed nonrandom following results hold function is given gamma distribution inverted gamma inverted gamma distribution k-dimensional random vector Kotz Linear combinations marginal distribution Marginals and conditionals matric-variate normal distribution Mean and variance multinomial multivariate normal distribution multivariate t-distribution notation Note obtained by reversing otherwise p.d.f. is denoted p.d.f. is given parameterize partitioned Poirier Poisson distribution positive definite matrix positive integer previous formulas probability function properties q matrix referred reversing subscripts scalar scale matrix standard Statistics Student success Theorem uniform distribution Var(Y variable Y Wishart distribution