## Introductory EconometricsThis book has taken form over several years as a result of a number of courses taught at the University of Pennsylvania and at Columbia University and a series of lectures I have given at the International Monetary Fund. Indeed, I began writing down my notes systematically during the academic year 1972-1973 while at the University of California, Los Angeles. The diverse character of the audience, as well as my own conception of what an introductory and often terminal acquaintance with formal econometrics ought to encompass, have determined the style and content of this volume. The selection of topics and the level of discourse give sufficient variety so that the book can serve as the basis for several types of courses. As an example, a relatively elementary one-semester course can be based on Chapters one through five, omitting the appendices to these chapters and a few sections in some of the chapters so indicated. This would acquaint the student with the basic theory of the general linear model, some of the prob lems often encountered in empirical research, and some proposed solutions. For such a course, I should also recommend a brief excursion into Chapter seven (logit and pro bit analysis) in view of the increasing availability of data sets for which this type of analysis is more suitable than that based on the general linear model. |

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### Contents

Chapter | 1 |

Chapter | 12 |

Chapter 4 | 20 |

Questions and Problems | 24 |

Chapter 2 | 34 |

Questions and Problems | 67 |

The General Linear Model III | 98 |

Questions and Problems | 144 |

Chapter 5 | 217 |

Questions and Problems | 266 |

Chapter 7 | 319 |

Appendix | 340 |

Statistical and Probabilistic Background | 353 |

Questions and Problems | 397 |

416 | |

The General Linear Model IV | 185 |

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### Common terms and phrases

2SLS 2SLS estimator 3SLS Aitken estimator alternative asymptotic distribution autoregression Bayesian chapter characteristic roots characteristic vectors chi-square chi-square distribution coefficient of determination collinearity column compute conclude condition Consequently consistent estimator constant term context correlation corresponding covariance matrix data matrix defined degrees of freedom density function dependent variable diagonal Econometrics elements ellipsoid error process error term estimator of ft explanatory variables F-test follows given GLSEM Hence hypothesis H0 i.i.d. random variables ith structural equation joint density likelihood function linear Mathematics for Econometrics multicollinearity mutually independent nonsingular matrix nonstochastic normal null hypothesis obeys observations obtain OLS estimator orthogonal parameter vector partition plim positive definite matrix positive semidefinite principal components problem procedure PROOF Proposition quadratic forms random variables rank reduced form regression respect restrictions sample means scalar squared residuals sum of squared suppose test statistic Theorem transformation unbiased estimator unknown parameters variance