## Econometric Modeling: A Likelihood Approach
David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. |

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### Contents

Inference in the Bernoulli model | 14 |

A first regression model | 28 |

The logit model | 47 |

The twovariable regression model | 66 |

The matrix algebra of twovariable regression | 88 |

The multiple regression model | 98 |

The matrix algebra of multiple regression | 121 |

Misspecification analysis in cross sections | 127 |

Misspecification analysis in time series | 190 |

The vector autoregressive model | 203 |

Identification of structural models | 217 |

Nonstationary time series | 240 |

Cointegration | 254 |

Monte Carlo simulation experiments | 270 |

Automatic model selection | 286 |

Structural breaks | 302 |

Strong exogeneity | 140 |

Empirical models and modeling | 154 |

Autoregressions and stationarity | 175 |

Forecasting | 323 |