## Introduction to EconometricsIntroduction to Econometrics provides an introduction to econometrics using analytical and intuitive methods of the classical linear regression model. Mathematical notation is kept simple and step-by-step explanations of mathematical proofs are provided to facilitate learning. The text also provided to facilitate learning. The text also contains a large number of practical exercises, enabling students to practice what they have learned. This new edition has been substantially updated and revised with the inclusion of new material on specification tests, binary choice models, tobit analysis, sample selection bias, nonstationary time series, and unit root tests and basic cointegration. The new edition is also accompanied by a website with Powerpoint slideshows giving a parallel graphical treatment of topics treated in the book, cross-section and time series data sets, manuals for practical exercises, and lecture note extending the text. |

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### Contents

Random Variables Sampling and Estimation | 1 |

1 Simple Regression Analysis | 43 |

2 Properties of the Regression Coefficients and Hypothesis Testing | 67 |

3 Multiple Regression Analysis | 119 |

4 Transformations of Variables | 153 |

5 Dummy Variables | 173 |

A Preliminary Skirmish | 199 |

7 Heteroscedasticity | 224 |

11 Models Using Time Series Data | 323 |

12 Properties of Regression Models with Time Series Data | 350 |

13 Introduction to Nonstationary Time Series | 381 |

14 Introduction to Panel Data Models | 408 |

Statistical Tables | 424 |

Data Sets | 442 |

451 | |

455 | |

8 Stochastic Regressors and Measurement Errors | 241 |

9 Simultaneous Equations Estimation | 269 |

10 Binary Choice and Limited Dependent Variable Models and Maximum Likelihood Estimation | 291 |

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### Common terms and phrases

_cons assumption ASVABC autocorrelation bias calculate Coef component Conf constant correlation cost critical value defined degrees of freedom dependent variable df MS Number distributed independently disturbance term dummy variable EAEF data set earnings function equal ETHBLACK ETHHISP example Exercise expected value expenditure explanatory variables F statistic F test Figure fixed effects Hence heteroscedasticity homoscedasticity intercept lagged least squares LGEARN linear logarithm MALE measurement error multicollinearity normal distribution null hypothesis Number of obs obtain OLS estimator OLS regression parameters percent level plim population variance Prob>F probability density function R-squared random variable regression analysis regression coefficients regression model regression results regressors relationship residual sum Root MSE simple regression slope coefficient Source SS df specification standard deviation standard errors sum of squares Suppose Table true value Type I error zero