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Making Good Inferences from Bad Data

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  • John G. Cragg

Abstract

Errors in variables can seriously distort inference when they are not taken into account explicitly. Coefficient values, their significance, and whether some explanatory variables should instead be used as instruments are largely a matter of interpretation unless further information is available. Higher moments of the observable variables impose restrictions that allow testing for identification and specification and estimating the parameters of the standard errors-in-variables model. The argument is developed partly through examples illustrating the points. Errors in variables can seriously distort inference when they are not taken into account explicitly. Coefficient values, their significance, and whether some explanatory variables should instead be used as instruments are largely a matter of interpretation unless further information is available. Higher moments of the observable variables impose restrictions that allow testing for identification and specification and estimating the parameters of the standard errors-in-variables model. The argument is developed partly through examples illustrating the points.

Suggested Citation

  • John G. Cragg, 1994. "Making Good Inferences from Bad Data," Canadian Journal of Economics, Canadian Economics Association, vol. 27(4), pages 776-800, November.
    • Handle: RePEc:cje:issued:v:27:y:1994:i:4:p:776-800
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    Cited by:

    1. Craig Wesley Carpenter & Anders Van Sandt & Scott Loveridge, 2022. "Measurement error in US regional economic data," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 57-80, January.
    2. Sadefo Kamdem, J. & Mbairadjim Moussa, A. & Terraza, M., 2012. "Fuzzy risk adjusted performance measures: Application to hedge funds," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 702-712.
    3. Alfred Mbairadjim Moussa & Jules Sadefo Kamdem, 2022. "A fuzzy multifactor asset pricing model," Annals of Operations Research, Springer, vol. 313(2), pages 1221-1241, June.
    4. Mbairadjim Moussa, A. & Sadefo Kamdem, J. & Shapiro, A.F. & Terraza, M., 2014. "CAPM with fuzzy returns and hypothesis testing," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 40-57.
    5. Reese, Simon & Li, Yushu, 2013. "Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements," Working Papers 2013:36, Lund University, Department of Economics.
    6. Alfred Mbairadjim Moussa & Jules Sadefo Kamdem & Arnold F. Shapiro & Michel Terraza, 2012. "Capital asset pricing model with fuzzy returns and hypothesis testing," Working Papers 12-33, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    7. Kyle L Marquardt, 2020. "How and how much does expert error matter? Implications for quantitative peace research," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 692-700, November.
    8. Rachida Hennani & Michel Terraza, 2012. "Value-at-Risk stressée chaotique d’un portefeuille bancaire," Working Papers 12-23, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    9. Carmichael, Benoît & Coën, Alain, 2008. "Asset pricing models with errors-in-variables," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 778-788, September.
    10. Van Klaveren, Chris, 2011. "Lecturing style teaching and student performance," Economics of Education Review, Elsevier, vol. 30(4), pages 729-739, August.
    11. Ramazan Gencay & Nikola Gradojevic, 2009. "Errors-in-Variables Estimation with No Instruments," Working Paper series 30_09, Rimini Centre for Economic Analysis.
    12. Coën, Alain & Hübner, Georges, 2009. "Risk and performance estimation in hedge funds revisited: Evidence from errors in variables," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 112-125, January.
    13. Coen, Alain & Racicot, Francois-Eric, 2007. "Capital asset pricing models revisited: Evidence from errors in variables," Economics Letters, Elsevier, vol. 95(3), pages 443-450, June.

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