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A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem

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  • Jonas Andersson
  • Jarle Møen

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.
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  • 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.
    • Handle: RePEc:bla:obuest:v:78:y:2016:i:1:p:113-125
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    File URL: http://hdl.handle.net/10.1111/obes.12103
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    11. 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.
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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. Erkin Diyarbakirlioglu & Marc Desban & Souad Lajili Jarjir, 2022. "Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms," Post-Print hal-03643083, HAL.
    3. Federico Crudu, 2017. "Errors-in-Variables Models with Many Proxies," Department of Economics University of Siena 774, Department of Economics, University of Siena.

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    More about this item

    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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