A cursory reading of recent textbooks on econometrics shows that historically the emphasis in our discipline has been placed on models that are without measurement error in the variables but instead have stochastic ‘shocks’ in the equations. To the extent that the topic of errors of measurement in variables (or latent variables) is treated, one will usually find that for a classical single-equation regression model, measurement error in the dependent variable, y, causes no particular problem because it can be subsumed within the equation’s disturbance term. But when it comes to the matter of measurement errors in the independent variables, the argument will usually be made that consistent parameter estimation is unobtainable unless repeated observations on y are available at each data point, or strong a priori information can be employed. The presentation usually ends there, leaving us with the impression that the errors-in-variables ‘problem’ is bad enough in the classical regression model and surely must be worse in more complicated models.
- Aigner, D.J., Hsiao, C., Kapteyn, A. and T. Wansbeek. 1984. Latent variable models in econometrics. Chapter 23 in Handbook of econometrics, ed. Z. Griliches., and M. Intriligator, vol. 2. Amsterdam: North-Holland.Google Scholar
- Jöreskog, K.G., and A.S. Goldberger. 1975. Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association 70: 631–639.Google Scholar
- Maravall, A. and D.J. Aigner. 1977. Identification of the dynamic shock-error model: The case of dynamic regression. Chapter 18 in Latent variables in socioeconomic models, ed. D.J. Aigner., and A.S. Goldberger. Amsterdam: North-Holland.Google Scholar
- Zellner, A. 1971. An introduction to Bayesian inference in econometrics. New York: Wiley.Google Scholar