Published online by Cambridge University Press: 04 September 2018
This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (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.
We thank three anonymous referees, the co-editor Liangjun Su, and the editor Peter C.B. Phillips for many constructive comments and suggestions. We also thank conference participants of SETA 2016, AMES 2016, and CFE 2017 for their discussions and suggestions. Xinyu Zhang gratefully acknowledges the research support from National Natural Science Foundation of China (Grant numbers 71522004, 11471324 and 71631008). Chu-An Liu gratefully acknowledges the research support from the Ministry of Science and Technology of Taiwan (MOST 104-2410-H-001-092-MY2). All errors remain the authors’.
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