## Intermediate Statistics and Econometrics: A Comparative ApproachThe standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses—giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics. Poirier's text provides a thoroughly modern, self-contained, comprehensive, and accessible treatment of the probability and statistical foundations of econometrics with special emphasis on the linear regression model. Written primarily for advanced undergraduate and graduate students who are pursuing research careers in economics, Intermediate Statistics and Econometrics offers a broad perspective, bringing together a great deal of diverse material. Its comparative approach, emphasis on regression and prediction, and numerous exercises and references provide a solid foundation for subsequent courses in econometrics and will prove a valuable resource to many nonspecialists who want to update their quantitative skills.The introduction closes with an example of a real-world data set—the Challenger space shuttle disaster—that motivates much of the text's theoretical discussion. The ten chapters that follow cover basic concepts, special distributions, distributions of functions of random variables, sampling theory, estimation, hypothesis testing, prediction, and the linear regression model. Appendixes contain a review of matrix algebra, computation, and statistical tables. |

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The book is intended for first year graduate students and focuses on Bayesian econometrics. Having learnt Bayesian econometrics from Dale himself, straight from this book, the book does make a lot of sense and brings out the problems with frequentist econometrics. Book has a lot of typos (blame the publisher), fortunately Dale has a list of all the typos, which one may get from his website.

### Contents

Special Distributions | 81 |

Distributions of Functions of Random Variables | 143 |

Sampling Theory | 165 |

Estimation | 168 |

Hypothesis Testing | 351 |

Prediction | 405 |

The Linear Regression Model | 445 |

Other Windows on the World | 585 |

Appendix A Matrix Algebra Review I | 619 |

Appendix B Matrix Algebra Review II | 645 |

Computation | 653 |

Statistical Tables | 661 |

667 | |

Author Index | 699 |

705 | |

### Common terms and phrases

analysis asymptotic Bayes Bayes factor Bayesian point estimate binomial classical coefficient confidence interval conjugate prior Consider Example Consider the standard constant continuous random variable convergence corresponding defined denoted depend discussion distribution function econometrics elements equations Exercise exists exponential Find finite fixed regressors following theorem forecast frequentist given hence hypothesis testing implies independent interpretation joint p.d.f. known likelihood function linear normal regression linear regression loss function minimal multicollinearity multivariate normal multivariate normal distribution noninformative normal distribution normal regression model Note null hypothesis observed OLS estimator pivotal quantity point estimate positive definite posterior density posterior distribution posterior mean predictive prior density prior distribution probability problem Proof quadratic random sample regressors researcher sample mean sample space sampling distribution scalar Section sequence Show standard multiple linear stochastic sufficient statistic Suppose symmetric tion unbiased estimator unknown parameters variance vector zero

### Popular passages

Page 695 - Varian, HR (1975). A Bayesian approach to real estate assessment. In Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage (SE Feinberg and A.