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The Bias of the 2SLS Variance Estimator

Author

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  • Kiviet, J.F.
  • Phillips, G.D.A.

Abstract

In simultaneous equation models the two stage least squares (2SLS) estimator of the coefficients, though consistent, is biased in general and the nature of this bias has given rise to a good deal of research. However, little if any attention has been given to the bias that arises when an estimate of the asymptotic variance is used to approximate the small sample variance. In this paper we use asymptotic expansions to show that, in general, the asymptotic variance estimator has an upwards bias.

Suggested Citation

  • Kiviet, J.F. & Phillips, G.D.A., 1999. "The Bias of the 2SLS Variance Estimator," Discussion Papers 9904, Exeter University, Department of Economics.
  • Handle: RePEc:exe:wpaper:9904
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    Cited by:

    1. Kiviet, Jan F. & Phillips, Garry D.A., 2014. "Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 424-448.
    2. Phillips, Garry D. A., 2000. "An alternative approach to obtaining Nagar-type moment approximations in simultaneous equation models," Journal of Econometrics, Elsevier, vol. 97(2), pages 345-364, August.

    More about this item

    Keywords

    ESTIMATOR ; REGRESSION ANALYSIS ; ECONOMETRICS;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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