## Advanced EconometricsAdvanced Econometrics is both a comprehensive text for graduate students and a reference work for econometricians. It will also be valuable to those doing statistical analysis in the other social sciences. Its main features are a thorough treatment of cross-section models, including qualitative response models, censored and truncated regression models, and Markov and duration models, as well as a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models. Although the treatment is mathematically rigorous, the author has employed the theorem-proof method with simple, intuitively accessible assumptions. This enables readers to understand the basic structure of each theorem and to generalize it for themselves depending on their needs and abilities. Many simple applications of theorems are given either in the form of examples in the text or as exercises at the end of each chapter in order to demonstrate their essential points. |

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### Contents

Classical Least Squares Theory | 1 |

Recent Developments in Regression Analysis | 45 |

Large Sample Theory | 81 |

Asymptotic Properties of Extremum Estimators | 105 |

Time Series Analysis | 159 |

Generalized Least Squares Theory | 181 |

Linear Simultaneous Equations Models | 228 |

Nonlinear Simultaneous Equations Models | 245 |

Tobit Models | 360 |

Markov Chain and Duration Models | 412 |

Useful Theorems in Matrix Analysis | 459 |

Notes | 465 |

475 | |

505 | |

511 | |

Qualitative Response Models | 267 |

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### Common terms and phrases

2SLS alternative Amemiya analysis applied approximation assume assumption asymptotic distribution asymptotic normality called Chapter characteristic choosing consider consistent constant continuous converges defined definite denoted density dependent derived determined diagonal discussed distribution Econometrics efficient elements equal equations equivalent error evaluated example exists given Heckman hypothesis implies independent variables individual iteration known least squares estimator likelihood function limit linear logit maximizing maximum likelihood estimator mean method minimizes nonlinear Note observations obtain parameters plim positive present probability probit problem proof properties proposed prove random variables regression model respect right-hand side roots sample satisfied Section sequence shown space specify Statistical Step subsection Suppose term Theorem tion Tobit model true Type unknown variance vector write yields