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Cost Function Estimation with Proportional Errors in Variables

Author

Listed:
  • Richard E. Just

    (Department of Agricultural and Resource Economics, University of Maryland, College Park Maryland, USA.)

  • Rulon D. Pope

    (Department of Economics, Brigham Young University, Provo, Utah, USA.)

Abstract

A model with proportional errors in variables arising naturally in microeconomics is considered. Unlike the classical additive errors case, all OLS parameter estimates exhibit attenuation bias that does not depend on the limiting distribution of the data. The distribution of OLS estimators is developed. With no intercept, a simple correction of OLS based on mean predictions is identified that is consistent and asymptotically normal. With an intercept, a readily available additional moment based on sample data identifies the parameters. In neither case are additional restrictions or use of extra-sample data as instruments required as for common errors-in-variables methods.

Suggested Citation

  • Richard E. Just & Rulon D. Pope, 2012. "Cost Function Estimation with Proportional Errors in Variables," International Econometric Review (IER), Econometric Research Association, vol. 4(2), pages 59-81, September.
    • Handle: RePEc:erh:journl:v:4:y:2012:i:2:p:59-81
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    References listed on IDEAS

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    More about this item

    Keywords

    Errors in variables; Proportional errors; Estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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