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Measure Of Location-Based Estimators In Simple Linear Regression

Author

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  • DANIEL PREVE

    (City University of Hong Kong)

  • Shu-Ping XIJIA LIU

    (Uppsala University)

Abstract

In this note we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The medianunbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two di erent cases are considered as we investigate the statistical properties of the MLBEs. In the rst case, the regressor and error is assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact nitesample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroskedastic and multiple regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study

Suggested Citation

  • DANIEL PREVE & Shu-Ping XIJIA LIU, 2013. "Measure Of Location-Based Estimators In Simple Linear Regression," Working Papers CoFie-02-2013, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    • Handle: RePEc:skb:wpaper:cofie-02-2013
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