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Resurrecting weighted least squares

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  • Joseph P. Romano
  • Michael Wolf

Abstract

This paper shows how asymptotically valid inference in regression models based on the weighted least squares (WLS) estimator can be obtained even when the model for reweighting the data is misspecified. Like the ordinary least squares estimator, the WLS estimator can be accompanied by heterokedasticty-consistent (HC) standard errors without knowledge of the functional form of conditional heteroskedasticity. First, we provide rigorous proofs under reasonable assumptions; second, we provide numerical support in favor of this approach. Indeed, a Monte Carly study demonstrates attractive finite-sample properties compared to the status quo, both in terms of estimation and making inference.

Suggested Citation

  • Joseph P. Romano & Michael Wolf, 2014. "Resurrecting weighted least squares," ECON - Working Papers 172, Department of Economics - University of Zurich, revised Oct 2016.
  • Handle: RePEc:zur:econwp:172
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    1. Here's Your Reading List!
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2014-12-02 00:12:00

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    More about this item

    Keywords

    Conditional heteroskedasticity; HC standard errors; weighted least squares;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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