Skip to main content

Errors in Variables in Econometrics

  • Chapter
Econometrics in Theory and Practice
  • 308 Accesses

Summary

This article discusses the use of instrumental variables and grouping methods in the linear errors-in-variables or measurement error model. Comparisons are made between these methods, standard measurement error model methods with side conditions, least squares methods, and replicated models. It is demonstrated that there are close relationships between these apparently diverse estimation techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, T. W. (1951). Estimating linear restrictions on regression coefficients for multivariate normal distributions. Ann. Math. Statist. 22, 327–351.

    Article  Google Scholar 

  2. Barlett, M.S. (1949). Fitting a Straight line when both variables are subject to error. Biometrics 5, 207–212.

    Article  Google Scholar 

  3. Bowden, R. J. and Turkington, D. A. (1984). Instrumental Variables. Cambridge: Cambridge University Press.

    Google Scholar 

  4. Carroll, R. J., Ruppert, D. & Stefanski, L. A. (1995). Measurement Error in Nonlinear Model London: Chapman & Hall.

    Google Scholar 

  5. Chang, Y. P. and Huang, W. T. (1997). Inferences for the linear errors-in-variables with changepoint models. J. Am. Statist. Assoc. 56, 171–178.

    Article  Google Scholar 

  6. Cheng, C-L. and Schneeweiß, H. (1997). Polynomial regression with errors in the variables. Journal of the Royal Statistical Society Ser. B. To appear.

    Google Scholar 

  7. Cheng, C-L. and Van Ness, J. W. (1998). Statistical Regression with Measure-ment Error. London: Edward Arnold. To appear.

    Google Scholar 

  8. Cox, N. R. (1976). The linear structural relation for several groups of data. Biometrika 63, 231–237.

    Article  Google Scholar 

  9. Dorff, M. and Gurland, J. (1961). Estimation of the parameters of a linear functional relation. J. R. Statist. Soc. Ser. B23, 160–170.

    Google Scholar 

  10. Fuller, W. A. (1987) Measurement Error Models. New York: Wiley.

    Book  Google Scholar 

  11. Goldberger, A. S. (1972). Structural equation methods in the social sciences. Econometrica 40, 979–1001.

    Article  Google Scholar 

  12. Johnston, J. (1972) Econometric Methods, 2nd ed., New York: McGraw-Hill.

    Google Scholar 

  13. Kendall, M. G. and Stuart, A. (1979) The Advanced Theory of Statistics. Vol. 2, 4th ed., London: Griffin.

    Google Scholar 

  14. Learner, E. E. (1978). Least square versus instrumental variables estimation in a simple errors in variables model. Econometrica 46, 961–968.

    Article  Google Scholar 

  15. Nair, K. R. and Banerjee, K. S. (1942). A note on fitting of straight line if both variables are subject to error. Sankhya 6, 331.

    Google Scholar 

  16. Neyman, J. and Scott, E. L. (1951). On certain methods of estimating the linear structural relation. Ann. Math. Statist. 22, 352–361.

    Article  Google Scholar 

  17. Pakes, A. (1982). On the asymptotic bias of the Wald-type estimators of a straight line when both variables are subject to error. Inst. Econ. Rev. 23, 491–497.

    Article  Google Scholar 

  18. Reiersol, O. (1941). Confluence analysis by means of lag moments and other methods of confluence analysis. Econometrica 9, 1–24.

    Article  Google Scholar 

  19. Reiersol, O. (1950). Identifiability of a linear relation between variables which are subject to error. Econometrica 18, 375–389.

    Article  Google Scholar 

  20. Richardson, D. H. and Wu, D. M. (1970). Least squares and grouping method estimators in the errors-in-variables model. J. Am. Statist. Assoc. 65, 724–748.

    Article  Google Scholar 

  21. Schneeweiß, H. (1985). Estimating linear relations with errors in the variables; the merging of two approaches. In Contributions to Econometrics and Statistics Today. H. Schneeweiß and H. Strecker Eds., Berlin: Springer-Verlag.

    Chapter  Google Scholar 

  22. Schneeweiß, H. and Mittag, H. J. (1986). Lineare Modelle mit Fehlerbehafteten Daten. Heidelberg: Physica-Verlag.

    Book  Google Scholar 

  23. Wald, A. (1940). Fitting of straight lines if both variables are subject to error. Ann. Math. Statist. 11, 284–300.

    Article  Google Scholar 

  24. Wald, A. (1949). Note on the consistency of the maximum likelihood estimate. Ann. Math. Statist. 20, 595.

    Article  Google Scholar 

  25. Ware, J. H. (1972). Fitting straight lines when both variables are subject to error and the ranks of the means are known. J. Am. Statist. Assoc. 67, 891–897.

    Article  Google Scholar 

  26. White, H. (1984). Asymptotic Theory for Econometricians. New York: Academic Press.

    Google Scholar 

  27. Zellner, A. (1970). Estimating of regression relationships containing unobservable variables. Int. Econ. Rev. 11, 441–454.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Physica-Verlag Heidelberg

About this chapter

Cite this chapter

Cheng, CL., Van Ness, J.W. (1998). Errors in Variables in Econometrics. In: Galata, R., Küchenhoff, H. (eds) Econometrics in Theory and Practice. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-47027-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-47027-1_1

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-642-47029-5

  • Online ISBN: 978-3-642-47027-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics