Abstract
Measurement error is important in econometric analysis. Its presence causes inconsistent parameter estimates. Under the classical measurement error assumption, instrumental variable methods can be used to eliminate the bias caused by measurement errors using a second measurement. This technique can be extended to polynomial regression models. For nonlinear models, deconvolution methods have been developed to cope with classical measurement errors. When the classical measurement error assumption is violated, auxiliary data-sets are usually needed to provide additional source of identification. When the true variable takes only discrete values, the mismeasurement problem takes the form of misclassification and requires special techniques.
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Bibliography
Abrevaya, J., J. Hausman, and F. Scott-Morton. 1998. Identification and estimation of polynomial errors-in-variables models. Journal of Econometrics 87: 239–269.
Bollinger, C. 1998. Measurement error in the current population survey: A nonparametric look. Journal of Labor Economics 16: 576–594.
Bound, J., C. Brown, G. Duncan, and W. Rodgers. 1994. Evidence on the validity of cross-sectional and longitudinal labor market data. Journal of Labor Economics 12: 345–368.
Bound, J., and A. Krueger. 1991. The extent of measurement error in longitudinal earnings data: Do two wrongs make a right. Journal of Labor Economics 12: 1–24.
Carroll, R., and M. Wand. 1991. Semiparametric estimation in logistic measurement error models. Journal of the Royal Statistical Society 53: 573–585.
Chen, X., H. Hong, and E. Tamer. 2005. Measurement error models with auxiliary data. Review of Economic Studies 72: 343–366.
Chen, X., Hong, H. and Tarozzi, A. 2004. Semiparametric efficiency in GMM models nonclassical measurement errors. Working paper, Duke University and New York University.
Devereux, P. and Tripathi, G. 2005. Combining datasets to overcome selection caused by censoring and truncation in moment bases models. Working paper, University of Connecticut and UCLA.
Friedman, M. 1957. A theory of the consumption function. Princeton: Princeton University Press.
Frish, R. 1934. Statistical confluence study. Oslo: University Institute of Economics.
Hahn, J. 1998. On the role of propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66: 315–332.
Hausman, J., H. Ichimura, W. Newey, and J. Powell. 1991. Measurement errors in polynomial regression models. Journal of Econometrics 50: 271–295.
Hirano, K., G. Imbens, and G. Ridder. 2003. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71: 1161–1189.
Hong, H., and E. Tamer. 2003. A simple estimator for nonlinear error in variable models. Journal of Econometrics 117: 1–19.
Imbens, G., Newey, W. and Ridder, G. 2005. Mean-squared-error calculations for average treatment effects. Working paper, Harvard University, MIT and USC.
Lee, L., and J. Sepanski. 1995. Estimation of linear and nonlinear errors-in-variables models using validation data. Journal of the American Statistical Association 90(429): 130–140.
Li, T. 2002. Robust and consistent estimation of nonlinear errors-in-variables models. Journal of Econometrics 110: 1–26.
Mahajan, A. 2006. Identification and estimation of regression models with misclassification. Econometrica 74: 631–665.
Manski, C., and J. Horowitz. 1995. Identification and robustness with contaminated and corrupted data. Econometrica 63: 281–302.
Molinari, F. 2005. Partial identification of probability distributions with misclassified data. Working paper, Cornell University.
Parker, J., and B. Preston. 2005. Precautionary savings and consumption fluctuations. American Economic Review 95: 1119–1144.
Robins, J., S. Mark, and W. Newey. 1992. Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics 48: 479–495.
Schennach, S. 2004. Estimation of nonlinear models with measurement error. Econometrica 72: 33–75.
Sepanski, J., and R. Carroll. 1993. Semiparametric quasi-likelihood and variance estimation in measurement error models. Journal of Econometrics 58: 223–256.
Taupin, M. 2001. Semiparametric estimation in the nonlinear structural errors-in-variables model. Annals of Statistics 29: 66–93.
Acknowledgments
The author acknowledges generous research support from the NSF (SES-0452143) and the Sloan Foundation.
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Hong, H. (2018). Measurement Error Models. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2619
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2619
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