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A simple improvement of the IV estimator for the classical errors-in-variables problem

Author

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  • Andersson, Jonas

    () (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration)

  • Møen, Jarle

    () (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration)

Abstract

Two measures of an error-ridden explanatory variable make it possible to solve the classical errors-in-variable problem by using one measure as an instrument for the other. It is well known that a second IV estimate can be obtained by reversing the roles of the two measures. We explore a simple estimator that is the linear combination of these two estimates, that minimizes the asymptotic mean squared error. In a Monte Carlo study we show that the gain in precision is significant compared to using only one of the original IV estimates. The proposed estimator also compares well with full information maximum likelihood under normality.

Suggested Citation

  • Andersson, Jonas & Møen, Jarle, 2009. "A simple improvement of the IV estimator for the classical errors-in-variables problem," Discussion Papers 2009/10, Norwegian School of Economics, Department of Business and Management Science.
  • Handle: RePEc:hhs:nhhfms:2009_010
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    File URL: http://hdl.handle.net/11250/163982
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    10. Jonas Andersson & Jarle Møen, 2016. "A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 113-125, February.
    11. Goldberger, Arthur S, 1972. "Maximum-Likelihood Estimation of Regressions Containing Unobservable Independent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(1), pages 1-15, February.
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    Cited by:

    1. Jonas Andersson & Jarle Møen, 2016. "A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 113-125, February.
    2. Federico Crudu, 2017. "Errors-in-Variables Models with Many Proxies," Department of Economics University of Siena 774, Department of Economics, University of Siena.

    More about this item

    Keywords

    Measurement errors; Classical Errors-in-Variables; multiple indicator method; Instrumental variable techniques;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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