## Introduction to EconometricsRetaining the student-friendly approach of previous editions, Introduction to Econometrics, Fourth Edition, uses clear and simple mathematics notation and step-by step explanations of mathematical proofs to help students thoroughly grasp the subject. Extensive practical exercises throughout--including fifty exercises on the same dataset--build students' confidence and provide them with hands-on practice in applying techniques. NEW TO THE FOURTH EDITION: * An expanded review section at the beginning of the book offers a more comprehensive guide to all of the statistical concepts needed to study econometrics * Additional exercises provide students with even more opportunities to put theory into practice * More Monte Carlo simulations help students use visualization to understand the math * New final sections at the end of each chapter contain summaries and non-technical introductions to more advanced topics An updated and expanded Companion Website contains resources for students and instructors: For students: * Data sets * Gretl, a free econometrics software application * PowerPoint-based slides with explanations * A study guide For instructors: * Instructor manuals for the text and data sets that detail the exercises and their solutions * PowerPoint-based slides * A "Contact the Author" link |

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### Contents

INTRODUCTION | 1 |

RANDOM VARIABLES SAMPLING AND ESTIMATION | 5 |

1 SIMPLE REGRESSION ANALYSIS | 83 |

2 PROPERTIES OF THE REGRESSION COEFFICIENTS AND HYPOTHESIS TESTING | 110 |

3 MULTIPLE REGRESSION ANALYSIS | 151 |

4 NONLINEAR MODELS AND TRANSFORMATIONS OF VARIABLES | 192 |

5 DUMMY VARIABLES | 224 |

6 SPECIFICATION OF REGRESSION VARIABLES | 250 |

10 BINARY CHOICE AND LIMITED DEPENDENT VARIABLE MODELS AND MAXIMUM LIKELIHOOD ESTIMATION | 354 |

11 MODELS USING TIME SERIES DATA | 391 |

12 AUTOCORRELATION | 429 |

13 INTRODUCTION TO NONSTATIONARY TIME SERIES | 463 |

14 INTRODUCTION TO PANEL DATA MODELS | 514 |

Statistical tables | 531 |

Data Sets | 548 |

559 | |

7 HETEROSCEDASTICITY | 280 |

8 STOCHASTIC REGRESSORS AND MEASUREMENT ERRORS | 300 |

9 SIMULTANEOUS EQUATIONS ESTIMATION | 331 |

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### Common terms and phrases

actual analysis assumed assumption ASVABC autocorrelation becomes bias calculate Chapter Coef coefficients component condition consistent constant correlation cost course data set defined degrees of freedom determined discussion distribution disturbance term dummy variable earnings effect equal equation error estimator example Exercise expected expenditure explanatory variables Figure function given gives Hence included income increase independently individual interpretation Interval lagged limit linear logarithm MALE mean measured normal distribution Note null hypothesis observations obtain ofthe output parameters percent perform period population positive possible Prob probability problem R-squared random random variable reason region regression model reject relationship residuals restriction Root sample sample mean schooling shown shows significance simple slope specification squares standard standard errors statistic Suppose Table tion true Type unit variance zero β β