Advertisement

Measurement Error in the Linear Dynamic Panel Data Model

  • Erik Meijer
  • Laura Spierdijk
  • Tom WansbeekEmail author
Conference paper
  • 1k Downloads
Part of the Lecture Notes in Statistics book series (LNS, volume 211)

Abstract

We study measurement error in the simplest dynamic panel data model without covariates. We start by investigating the first-order effects, on the most commonly used estimator, of the presence of measurement error. As was to be expected, measurement error renders this estimator inconsistent. However, with a slight adaptation, the estimator can be made consistent. This approach to consistent estimation is ad hoc and we next develop a systematic approach to consistent estimation. We show how to obtain the most efficient estimator from this class of consistent estimators. We illustrate our findings through an empirical example.

Keywords

Arellano–Bond estimator Anderson–Hsiao estimator Attenuation Dynamic model Measurement error Panel data Systems GMM Wansbeek–Bekker estimator 

Notes

Acknowledgments

We would like to thank the symposium organizer, Brajendra Sutradhar, and an anonymous reviewer of an earlier version of this paper for their useful comments and suggestions.

References

  1. Ahn, S.C., Schmidt, P.: Efficient estimation of models for dynamic panel data. J. Econom. 68, 5–27 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  2. Aigner, D.J., Hsiao, C., Kapteyn, A., Wansbeek, T.: Latent variable models in econometrics. In: Griliches, Z., Intriligator, M.D. (eds.) Handbook of Econometrics, vol. 2, pp. 1321–1393. North-Holland, Amsterdam (1984)Google Scholar
  3. Altonji, J.G., Siow, A.: Testing the response of consumption to income changes with (noisy) panel data. Q. J. Econ. 101, 293–328 (1987)CrossRefGoogle Scholar
  4. Anderson, T.W., Hsiao, C.: Estimation of dynamic models with error components. J. Am. Stat. Assoc. 77, 598–606 (1981)MathSciNetCrossRefGoogle Scholar
  5. Anderson, T.W., Hsiao, C.: Formulation and estimation of dynamic models using panel data. J. Econom. 18, 47–82 (1982)MathSciNetzbMATHCrossRefGoogle Scholar
  6. Antman, F., McKenzie, D.: Poverty traps and nonlinear income dynamics with measurement error and individual heterogeneity. J. Dev. Stud. 43, 1057–1083 (2007)CrossRefGoogle Scholar
  7. Arellano, M., Bond, S.: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297 (1991)zbMATHCrossRefGoogle Scholar
  8. Arellano, M., Bover, O.: Another look at the instrumental variable estimation of error-components models. J. Econom. 68, 29–51 (1995)zbMATHCrossRefGoogle Scholar
  9. Biørn, E.: The measurement error problem in dynamic panel data analysis: modeling and GMM estimation, Working paper, University of Oslo, 2012Google Scholar
  10. Blundell, R., Bond, S.: Initial conditions and moment restrictions in dynamic panel data models. J. Econom. 87, 115–143 (1998)zbMATHCrossRefGoogle Scholar
  11. Bond, S., Hoeffler, A., Temple, J.: GMM estimation of empirical growth models, CEPR Working Paper 3048, 2001Google Scholar
  12. Buonaccorsi, J.: Measurement Error, Models, Methods, and Applications. Chapman & Hall, Boca Raton (2010)zbMATHCrossRefGoogle Scholar
  13. Chen, J., Ni, S., Podgursky, M.: Estimating dynamic panel data models with measurement errors with an application to school evaluation based on student test scores. In: Proceedings of the 2008 Meeting of the American Statistical Association, pp. 951–957 (2008)Google Scholar
  14. Fan, Z., Sutradhar, B.C., Rao, R.P.: Bias corrected generalized method of moments and generalized quasi-likelihood inferences in linear models for panel data with measurement error. Sankhyā B. 74, 126–148 (2012)zbMATHCrossRefGoogle Scholar
  15. Griliches, Z., Hausman, J.A.: Errors in variables in panel data. J. Econom. 31, 93–118 (1986)MathSciNetCrossRefGoogle Scholar
  16. Harris, M.N., Mátyás, L.: Performance of the operational Wansbeek-Bekker estimator for dynamic panel data models. Appl. Econ. Lett. 7, 149–153 (2000)CrossRefGoogle Scholar
  17. Juster, F.T., Suzman, R.: An overview of the Health and Retirement Study. J. Hum. Resour. 30, S7–S56 (1995)CrossRefGoogle Scholar
  18. Komunjer, I., Ng, S.: Measurement errors in dynamic models, Working paper, 2011Google Scholar
  19. Magnus, J.R., Neudecker, H.: Symmetry, 0–1 matrices and Jacobians: A review. Econom. Theory 2, 157–190 (1986)CrossRefGoogle Scholar
  20. Meijer, E., Wansbeek, T.J.: Measurement error in a single regressor. Econ. Lett. 69, 277–284 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  21. Meijer, E., Spierdijk, L., Wansbeek, T.J.: Point and set identification in linear panel data models with measurement error. Working Paper WR-941, RAND Corporation, Santa Monica, 2012Google Scholar
  22. St. Clair, P., et al.: RAND HRS Data Documentation, Version L. RAND Corporation, Santa Monica (2011)Google Scholar
  23. Wansbeek, T.J., Bekker, P.A.: On IV, GMM and ML in a dynamic panel data model. Econ. Lett. 51, 145–152 (1996)zbMATHCrossRefGoogle Scholar
  24. Wansbeek, T.J., Kapteyn, A.: Simple estimators for dynamic panel data models with errors in the variables. In: Bewley, R., Van Hoa, T. (eds.) Contributions to Consumer Demand and Econometrics. McMillan, London (1992)Google Scholar
  25. Wansbeek, T.J., Meijer, E.: Measurement Error and Latent Variables in Econometrics. North-Holland, Amsterdam (2000)zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.RAND CorporationSanta MonicaUSA
  2. 2.University of GroningenGroningenThe Netherlands

Personalised recommendations