## Econometric Modeling and InferencePresents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work. |

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### Contents

3 | |

Sequential Models and Asymptotics | 17 |

Estimation by Maximization and by the Method of Moments | 33 |

290 Sims C A 1980 Macroeconomics and reality Econometrica 48 148 | 48 |

Nonparametric Methods | 87 |

Simulation Methods | 103 |

of Estimators and Tests | 116 |

Conditional Expectation | 129 |

Stationary Dynamic Models | 261 |

292 Spanos A 1986 Statistical foundations of econometric modelling Cambridge | 292 |

Nonstationary Processes and Cointegration | 304 |

305 Wand M P Jones M C 1995 Kernel smoothing Chapman and Hall London | 305 |

Models for Conditional Variance | 341 |

Nonlinear Dynamic Models | 366 |

Identiﬁcation and Overidentiﬁcation | 395 |

Simultaneity | 421 |

Generalized Least Squares Method Heteroskedasticity | 179 |

Nonparametric Estimation of the Regression | 213 |

Discrete Variables and Partially Observed Models | 234 |

Models with Unobservable Variables | 446 |

493 | |

### Other editions - View all

Econometric Modeling and Inference Jean-Pierre Florens,Velayoudom Marimoutou,Anne Peguin-Feissolle No preview available - 2007 |

Econometric Modeling and Inference Jean-Pierre Florens,Velayoudom Marimoutou,Anne Peguin-Feissolle No preview available - 2007 |

### Common terms and phrases

ˆfn assume assumption asymptotic distribution asymptotic properties asymptotic variance asymptotically normal calculate central limit theorem Chapter cointegrating conditional distribution conditional expectation conditional model Consider consistent estimator converges covariance deﬁned deﬁnition denoted density depends derivatives dimension distribution function Econometrica econometrics equal equation equivalent exogenous explanatory variables ﬁnite ﬁrst Florens fmarg given hence heteroskedasticity homoskedasticity i.i.d. model identiﬁcation implies independent integrable invertible Jmarg kernel large numbers law of large least squares likelihood function linear regression matrix maximization maximum likelihood method of moments minimization Moreover multivariate nonlinear nonparametric normal distribution notation null hypothesis observations obtain OLS estimator optimal parameters probability distribution problem random variables random vector regression model replacing restrictions result satisﬁed satisfies scalar sequence simulation solution speciﬁc stationary process statistical model stochastic Suppose test statistic unobservable verify Wald test zero