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Joint inference based on Stein-type averaging estimators in the linear regression model

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  • Boot, Tom

Abstract

While averaging unrestricted with restricted estimators is known to reduce estimation risk, it is an open question whether this reduction in turn can improve inference. To analyze this question, we construct joint confidence regions centered at James–Stein averaging estimators in both homoskedastic and heteroskedastic linear regression models. These regions are asymptotically valid when the number of restrictions increases possibly proportionally with the sample size. When used for hypothesis testing, we show that suitable restrictions enhance power over the standard F-test. We study the practical implementation through simulations and an application to consumption-based asset pricing.

Suggested Citation

  • Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.
    • Handle: RePEc:eee:econom:v:235:y:2023:i:2:p:1542-1563
    DOI: 10.1016/j.jeconom.2023.01.006
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    More about this item

    Keywords

    Model averaging; James–Stein; Confidence regions;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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