## The Econometric Analysis of Time SeriesThis new edition of A.C. Harvey's clearly written, upper-level text has been revised and several sections have been completely rewritten. There is new material on a number of topics, including unit roots, ARCH, and cointegration. The Econometric Analysis of Time Series focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs. It explores the way in which recent advances in time series analysis have affected the development of a theory of dynamic econometrics, sets out an integrated approach to the problems of estimation and testing based on the method of maximum likelihood, and presents a coherent strategy for model selection.A.C. Harvey is Professor of Econometrics at the London School of Economics. |

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最后两章动态回归，一章是偏重transfer model方法

一章是偏重Johansen的向量方法，需要细读

### Contents

Introduction | 1 |

Regression | 37 |

The Method of Maximum Likelihood | 84 |

Numerical Optimisation | 122 |

Test Procedures and Model Selection | 146 |

Regression Models with Serially Correlated Disturbances | 191 |

Dynamic Models I | 225 |

Stochastic Difference Equations | 264 |

Simultaneous Equation Models | 313 |

Appendix on Matrix Algebra | 359 |

Answers to Selected Exercises | 369 |

380 | |

386 | |

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### Common terms and phrases

algorithm alternative approach ARMA process assumption asymptotic distribution asymptotically efficient autocorrelation autoregressive Cochrane-Orcutt computed condition Consider consistent estimator constructed covariance matrix defined denotes dependent variable derivatives diagonal difference equation distributed lag disturbance term dynamic model econometric endogenous evaluated example exogenous variables explanatory variables expression F-distribution Gauss-Newton generalisation given heteroscedasticity identifiability information matrix instrumental variables iterative lag coefficients lag operator Lagrange multiplier large samples least squares likelihood function linear regression LM test maximising mean minimising ML estimator multiplier multivariate normal distribution non-linear normally distributed null hypothesis observations obtained OLS residuals optimisation parameters plim polynomial prediction errors problem properties recursive residuals reduced form regressors restrictions result Section serial correlation series model single equation small samples specification stochastic difference equation sum of squares test procedure test statistic transfer function transformation two-step estimator uncorrelated unrestricted model variance vector Wald Wald test white noise yields