## Econometric AnalysisFor a one-year graduate course in Econometrics. This text has two objectives. The first is to introduce students to applied econometrics, including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate. The second is to present students with sufficient theoretical background that they will recognize new variants of the models learned about here as merely natural extensions that fit within a common body of principles. The Fifth Edition features a complete update of techniques and developments, a reorganization of material for improved presentation, and new material and applications. |

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### Contents

Introduction | 6 |

The Classical Multiple Linear Regression Model | 7 |

The Classical Multiple Linear Regression Model | 8 |

Copyright | |

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### Common terms and phrases

analysis application approach assumed assumption asymptotic covariance matrix asymptotically normally autocorrelation binary Chapter chi-squared chi-squared distribution choice coefficients cointegrating column computed consider consistent estimator constant term convergence correlation critical value data set degrees of freedom denote density dependent variable derivatives diagonal discussed disturbances dummy variable econometrics equal equation example FGLS finite GMM estimator heteroscedasticity homoscedasticity income independent instrumental variables inverse iteration Lagrange multiplier least squares estimator likelihood function likelihood ratio linear model linear regression linear regression model log-likelihood log-likelihood function logit model maximum likelihood estimator method nonlinear regression normal distribution null hypothesis observations obtain ordinary least squares panel data parameter vector plim Poisson probability probit model problem produces random effects random effects model random variable regressors restrictions Section solution specification standard errors standard normal suggested sum of squares Table test statistic Theorem transformation unrestricted variance Wald test zero