Model averaging by jackknife criterion in models with dependent data |
| |
Authors: | Xinyu Zhang Alan T.K. Wan Guohua Zou |
| |
Affiliation: | 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;2. Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong |
| |
Abstract: | The past decade witnessed a 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 [Hansen, B.E., Racine, J., 2012. Jackknife model averaging. Journal of Econometrics 167, 38–46] 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. |
| |
Keywords: | C51 C52 |
本文献已被 ScienceDirect 等数据库收录! |
|