Inference after Model Averaging in Linear Regression Models

Xinyu Zhang () and Chu-An Liu
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Xinyu Zhang: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, http://english.amss.cas.cn/

No 17-A005, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan

Abstract: This paper considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estima-tor (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distributions of MMA and JMA estimators and propose a simulation-based confidence interval for the least squares averaging estimator. Monte Carlo simulations show that the coverage probabilities of proposed confidence intervals achieve the nominal level. JEL Calssification: C51, C52

Keywords: Confidence intervals; Inference post-model-averaging; Jackknife model averaging; Mallows model averaging (search for similar items in EconPapers)
Pages: 31 pages
Date: 2017-04, Revised 2018-04
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2019)
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