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Model Averaging by Jackknife Criterion in Models with Dependent Data
2012-08-27 | 编辑:

论文题目: Model Averaging by Jackknife Criterion in Models with Dependent Data

作者: Zhang X., Wan A.T.K. and Zou G.

论文摘要: The past decade witnessed a rapidly growing literature on model averaging by frequentist methods. For the most part, the asymptotic optimality of various existing frequentist model averaging estimators has been established under i.i.d. errors. Recently, Hansen and Racine (2012, Journal of Econometrics) developed a jackknife model averaging (JMA) estimator, which has an important advantage over its competitors in that it achieves the lowest possible asymptotic squared error under heteroscedastic errors. In this paper, we broaden Hansen and Racine’s scope of analysis to encompass models with i) a non-diagonal error covariance structure, and ii) lagged dependent variables, thus allowing for dependent data. We show that under these set-ups, the JMA estimator is asymptotically optimal by a criterion equivalent to that used by Hansen and Racine. A Monte Carlo study demonstrates the finite sample performance of the JMA estimator in a variety of model settings.

所属实验室或研究中心: 预测中心
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