Heteroscedasticity-robust Cp model averaging

Summary This paper proposes a new model‐averaging method, called the hetero‐scedasticity–robust Cp (HRCp) method, for linear regression models with heteroscedastic errors. We provide a feasible form of the Mallows’ Cp‐like criterion for choosing the weight vector for averaging. Under some regularity...

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Bibliographic Details
Published in:The econometrics journal 2013-10, Vol.16 (3), p.463-472
Main Authors: Liu, Qingfeng, Okui, Ryo
Format: Article
Language:English
Subjects:
Asymptotic methods
Asymptotic optimality
Average
Coefficients
Econometrics
Economic models
Economic statistics
Economic theory
Heteroscedastic errors
Linear equations
Linear models
Mallows' Cp
Model averaging
Model selection
Monte Carlo simulation
Regression analysis
Simulation
Theorems
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Summary:Summary This paper proposes a new model‐averaging method, called the hetero‐scedasticity–robust Cp (HRCp) method, for linear regression models with heteroscedastic errors. We provide a feasible form of the Mallows’ Cp‐like criterion for choosing the weight vector for averaging. Under some regularity conditions, we show that the HRCp method has asymptotic optimality. The simulation results show that our method works well and performs better than alternative methods in finite samples when the number of candidate models is large and/or the population coefficient of determination is not small.
ISSN:1368-4221
1368-423X
DOI:10.1111/ectj.12009