## Bayesian EconometricsResearchers 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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### Contents

The Normal Linear Regression Model with Other Priors | 59 |

NormalGamma Prior | 73 |

5 | 86 |

Copyright | |

17 other sections not shown

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algorithm allows alternatives applications approach approximation assume assumption averaging Bayesian inference calculate carry choice choose coefficients common comparing conditional consider contains data set defined Definition denoted density dependent dependent variable derive described developed discussed distribution draws econometrics effects elements empirical equal equations error estimate example explanatory variables extensions fact function Gibbs sampler given Hence illustration implies important independent indicate individual instance intercept interested interpreted introduced involves issues likelihood likelihood function linear regression model literature marginal likelihood matrix mean methods mixture natural conjugate noninformative prior nonparametric Normal linear regression notation Note observations obtain ordered parameters particular posterior simulator precise predictive present prior probability probit model problem properties random variables ratio reader referred relating researcher restrictions sampling similar space model standard Table Theorem values variance vector write written