Chang, Jinyuan and Chen, Song Xi and Chen, Xiaohong
(2014):
*High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data.*
Forthcoming in: Journal of Econometrics

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## Abstract

This paper considers the maximum generalized empirical likelihood (GEL) estimation and inference on parameters identified by high dimensional moment restrictions with weakly dependent data when the dimensions of the moment restrictions and the parameters diverge along with the sample size. The consistency with rates and the asymptotic normality of the GEL estimator are obtained by properly restricting the growth rates of the dimensions of the parameters and the moment restrictions, as well as the degree of data dependence. It is shown that even in the high dimensional time series setting, the GEL ratio can still behave like a chi-square random variable asymptotically. A consistent test for the over-identification is proposed. A penalized GEL method is also provided for estimation under sparsity setting.

Item Type: | MPRA Paper |
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Original Title: | High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data |

English Title: | High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data |

Language: | English |

Keywords: | Generalized empirical likelihood; High dimensionality; Penalized likelihood; Variable selection; Over-identification test; Weak dependence. |

Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General |

Item ID: | 59640 |

Depositing User: | Professor Song Xi Chen |

Date Deposited: | 04 Nov 2014 05:43 |

Last Modified: | 26 Sep 2019 08:47 |

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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/59640 |