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Unifying Estimation and Inference for Linear Regression with Stationary and Integrated or Near-Integrated Variables

Author

Listed:
  • Shaoxin Hong
  • Daniel J Henderson
  • Jiancheng Jiang
  • XQingshan Ni

Abstract

There is a discrepancy in the limiting distributions of least-squares estimators for stationary and integrated variables. For statistical inference, it must be decided which distribution should be used in advance. This motivates us to develop a unifying inference procedure based on weighted estimation. The asymptotic distributions of the proposed estimators are developed and a random weighting bootstrap method is proposed for constructing confidence regions. The proposed method outperforms existing methods (with time constant or time-varying error variance) in simulations. We further study the predictability of asset returns in a setting where some of our state variables are endogenous.

Suggested Citation

  • Shaoxin Hong & Daniel J Henderson & Jiancheng Jiang & XQingshan Ni, 2024. "Unifying Estimation and Inference for Linear Regression with Stationary and Integrated or Near-Integrated Variables," Journal of Financial Econometrics, Oxford University Press, vol. 22(5), pages 1397-1420.
    • Handle: RePEc:oup:jfinec:v:22:y:2024:i:5:p:1397-1420.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbad030
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    More about this item

    Keywords

    integrated; nearly integrated; random weighting; unit roots; weighted estimation equation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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