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On the least-squares model averaging interval estimator

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  • Sebastian Ankargren
  • Shaobo Jin

Abstract

In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion.

Suggested Citation

  • Sebastian Ankargren & Shaobo Jin, 2018. "On the least-squares model averaging interval estimator," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(1), pages 118-132, January.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:1:p:118-132
    DOI: 10.1080/03610926.2017.1300272
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    Cited by:

    1. Giuseppe Luca & Jan R. Magnus & Franco Peracchi, 2023. "Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1637-1664, April.
    2. Shaobo Jin, 2022. "Frequentist Model Averaging in Structure Equation Model With Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1130-1145, September.

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