IDEAS home Printed from https://ideas.repec.org/a/bes/jnlbes/v19y2001i4p475-81.html
   My bibliography  Save this article

Bias from Classical and Other Forms of Measurement Error

Author

Listed:
  • Hyslop, Dean R
  • Imbens, Guido W

Abstract

We consider the implications of an alternative to the classical measurement-error model, in which the observed, mismeasured data are optimal predictions of the true values, given some information set. In this model, any measurement error is uncorrelated with the reported value and, by necessity, correlated with the true value of interest. In a regression model, such measurement error in the regressor does not lead to bias, whereas measurement error in the dependent variable leads to bias toward 0. In general, the measurement-error model, together with the information set, is critical for determining the bias in econometric estimates.

Suggested Citation

  • Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-481, October.
    • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:4:p:475-81
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. David Card & Dean Hyslop, 1997. "Does Inflation "Grease the Wheels of the Labor Market"?," NBER Chapters, in: Reducing Inflation: Motivation and Strategy, pages 71-122, National Bureau of Economic Research, Inc.
    2. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    3. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    4. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    5. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    6. Card, David, 1996. "The Effect of Unions on the Structure of Wages: A Longitudinal Analysis," Econometrica, Econometric Society, vol. 64(4), pages 957-979, July.
    7. Leamer, Edward E, 1987. "Errors in Variables in Linear Systems," Econometrica, Econometric Society, vol. 55(4), pages 893-909, July.
    8. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-183, January.
    9. Das, J.W.M. & Dominitz, J. & van Soest, A.H.O., 1997. "Comparing Predictions and Outcomes : Theory and Application to Income Changes," Other publications TiSEM 6eef11dd-0ae4-4673-b8c0-2, Tilburg University, School of Economics and Management.
    10. Ashenfelter, Orley & Krueger, Alan B, 1994. "Estimates of the Economic Returns to Schooling from a New Sample of Twins," American Economic Review, American Economic Association, vol. 84(5), pages 1157-1173, December.
    11. Manski, C.F., 1989. "The Use Of Intentions Data To Predict Behaviour : A Best- Case Analysis," Working papers 8905, Wisconsin Madison - Social Systems.
    12. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    13. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    2. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    3. Christopher R. Bollinger, 2001. "Response Error and the Union Wage Differential," Southern Economic Journal, John Wiley & Sons, vol. 68(1), pages 60-76, July.
    4. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
    5. Hu, Yingyao, 2006. "Bounding parameters in a linear regression model with a mismeasured regressor using additional information," Journal of Econometrics, Elsevier, vol. 133(1), pages 51-70, July.
    6. Magnac, Thierry, 2013. "Identification partielle : méthodes et conséquences pour les applications empiriques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 233-258, Décembre.
    7. Christopher R. Bollinger & Amitabh Chandra, 2005. "Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data," Journal of Labor Economics, University of Chicago Press, vol. 23(2), pages 235-258, April.
    8. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    9. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    10. Bastianin, Andrea & Castelnovo, Paolo & Florio, Massimo, 2018. "Evaluating regulatory reform of network industries: a survey of empirical models based on categorical proxies," Utilities Policy, Elsevier, vol. 55(C), pages 115-128.
    11. Verdugo, Gregory, 2016. "Real wage cyclicality in the Eurozone before and during the Great Recession: Evidence from micro data," European Economic Review, Elsevier, vol. 82(C), pages 46-69.
    12. O'Neill, Donal & Sweetman, Olive, 2012. "The Consequences of Measurement Error when Estimating the Impact of BMI on Labour Market Outcomes," IZA Discussion Papers 7008, Institute of Labor Economics (IZA).
    13. Thomas J. Kane & Cecilia E. Rouse & Douglas Staiger, 1999. "Estimating Returns to Schooling When Schooling is Misreported," Working Papers 798, Princeton University, Department of Economics, Industrial Relations Section..
    14. Bound, John & Solon, Gary, 1999. "Double trouble: on the value of twins-based estimation of the return to schooling," Economics of Education Review, Elsevier, vol. 18(2), pages 169-182, April.
    15. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," Papers 2011.07276, arXiv.org.
    16. Donal O'Neill, 2015. "Correcting for Self-Reporting Bias in BMI: A Multiple Imputation Approach," Economics Department Working Paper Series n263-15.pdf, Department of Economics, National University of Ireland - Maynooth.
    17. Prati, Alberto, 2017. "Hedonic recall bias. Why you should not ask people how much they earn," Journal of Economic Behavior & Organization, Elsevier, vol. 143(C), pages 78-97.
    18. Adele Bergin, 2013. "Job Changes and Wage Changes: Estimation with Measurement Error in a Binary Variable," Economics Department Working Paper Series n240-13.pdf, Department of Economics, National University of Ireland - Maynooth.
    19. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    20. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.

    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bes:jnlbes:v:19:y:2001:i:4:p:475-81. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.