The elephant in the corner: A cautionary tale about measurement error in treatment effects models

Advances in Econometrics

Volumn 27 A, Issue , 2011, Pages 1-39

  Millimet, Daniel L ^a  

^a NONE

Author keywords

Measurement error; Propensity score; Selection on observables; Treatment effects; Unconfoundedness

Indexed keywords

EID: 84884600740     PISSN: 07319053     EISSN: None     Source Type: Book Series    
DOI: 10.1108/S0731-9053(2011)000027A004     Document Type: Article

References (71)

1. Abrevaya, J., & Hausman, J. A. (2004). Response error in a transformation model with an application to earnings-equation estimation. Econometrics Journal, 7, 366-388.
2. Aigner, D. J. (1973). Regression with a binary independent variable subject to errors of observation. Journal of Econometrics, 1, 49-60.
3. Almeida, H., Campello, M., & Galvao Jr., A. F. (2010). Measurement errors in investment equations. NBER Working Paper no. 15951.
4. Ashenfelter, O., & Krueger, A. (1994). Estimates of the economic return to schooling from a new sample of twins. American Economic Review, 84, 1157-1173.
5. Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61, 962-972.
6. Barron, J. M., Berger, M. C., & Black, D. A. (1997). How well do we measure training? Journal of Labor Economics, 15, 507-528.
7. Basu, A., Polsky, D., & Manning, W.G. (2008). Use of propensity scores in non-linear response models: The case of health care expenditures. NBER Working Paper no. 14086.
8. Battistin, E., & Sianesi, B. (2011). Misclassified treatment status and treatment effects: An application to returns to education in the UK. Review of Economics and Statistics, 93, 495-509.
9. Battistin, E., & Chesher, A. (2009). Treatment effect estimation with covariate measurement error. CEMMAP Working Paper no. 25/09.
10. Baum-Snow, N., & Neal, D. (2009). Mismeasurement of usual hours worked in the census and ACS. Economics Letters, 102, 39-41.
11. Black, D., Sanders, S., & Taylor, L. (2003). Measurement of higher education in the census and current population survey. Journal of the American Statistical Association, 98, 545-554.
12. Black, D. A., Berger, M. C., & Scott, F. A. (2000). Bounding parameter estimates with nonclassical measurement error. Journal of the American Statistical Association, 95, 739-748.
13. Black, D. A., & Smith, J. A. (2004). How robust is the evidence on the effects of college quality? Evidence from matching. Journal of Econometrics, 121, 99-124.
14. Bollinger, C. R. (1996). Bounding mean regressions when a binary regressor is mismeasured. Journal of Econometrics, 73, 387-399.
15. Bound, J., Brown, C., & Mathiowetz, N. A. (2001). Measurement error in survey data. In J. J. Heckman & E.E. Leamer (Eds.), Handbook of Econometrics (Vol. 5, pp. 3705-3843).
16. Brener, N. D., McManus, T., Galuska, D. A., Lowry, R., & Wechsler, H. (2003). Reliability and validity of self-reported height and weight among high school students. Journal of Adolescent Health, 32, 281-287.
17. Card, D. (1996). The effect of unions on the structure of wages: A longitudinal analysis. Econometrica, 64, 957-979.
18. Chua, T. C., & Fuller, W. A. (1987). A model for multinomial response error applied to labor flows. Journal of the American Statistical Association, 82, 46-51.
19. D'Agostino, R. B. Jr. (1998). Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265-2281.
20. de Marchi, S., & Hamilton, J. T. (2006). Assessing the accuracy of self-reported data: An evaluation of the toxics release inventory. Journal of Risk and Uncertainty, 32, 57-76.
21. Dehejia, R. H., & Wahba, S. (1999). Casual effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053-1062.
22. Fisher, R. A. (1935). The design of experiments. Edinburgh: Oliver & Boyd.
23. Frazis, H., & Loewenstein, M. A. (2003). Estimating linear regressions with mismeasured, possibly endogenous, binary explanatory variables. Journal of Econometrics, 117, 151-178.
24. Freeman, R. B. (1984). Longitudinal analyses of the effects of trade unions. Journal of Labor Economics, 2, 1-26.
25. Frisch, R. (1934). Statistical confluence analysis by means of complete regression systems. Oslo: University Institute for Economics.
26. Gibson, J., & Kim, B. (2010). Non-classical measurement error in long-term retrospective recall surveys. Oxford Bulletin of Economics and Statistics, 72, 687-695.
27. Gini, C. (1921). Sull'interpolazione di una retta quando i valori della variabile indipendente sono affetti da errori accidntali. Metron, 1, 63-82.
28. Griliches, Z. (1985). Data and econometricians: The uneasy alliance. American Economic Review, 75, 196-200.
29. Griliches, Z., & Ringstad, V. (1970). Error in variables bias in nonlinear contexts. Econometrica, 42, 971-998.
30. Hausman, J. A. (2001). Mismeasured variables in econometric analysis: Problems from the right and problems from the left. Journal of Economic Perspectives, 15, 57-67.
31. Hausman, J. A., Abrevaya, J., & Scott-Morton, F. M. (1998). Misclassification of the dependent variable in a discrete-response setting. Journal of Econometrics, 87, 239-269.
32. Hausman, J. A., Newey, W. K., Ichimura, H., & Powell, J. L. (1991). Identification and estimation of polynomial errors-in-variables models. Journal of Econometrics, 50, 273-295.
33. Heckman, J., & Navarro-Lozano, S. (2004). Using matching, instrumental variables, and control functions to estimate economic choice models. Review of Economics and Statistics, 86, 30-57.
34. Heckman, J. J., & Robb, R. (1985). Alternative methods for evaluating the impact of interventions. In: J. J. Heckman & B. Singer (Eds.), Longitudinal analysis of labor market data. Cambridge, UK: Cambridge University Press.
35. Hirano, K., & Imbens, G. W. (2001). Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes Research Methodology, 2, 259-278.
36. Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663-685.
37. Hu, Y. (2006). Bounding parameters in a linear regression model with a mismeasured regressor using additional information. Journal of Econometrics, 133, 51-70.
38. Hyslop, R., & Imbens, G. W. (2001). Bias from classical and other forms of measurement error. Journal of Business & Economic Statistics, 19, 475-481.
39. Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86, 4-29.
40. Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5-86.
41. Jakubowski, M. (2010). Latent variables and propensity score matching. Working Paper no. 6/2010. University of Warsaw, Warszawa, Poland.
42. Keane, M. P., & Sauer, R. M. (2009). Classification error in dynamic discrete choice models: Implications for female labor supply behavior. Econometrica, 77, 975-991.
43. Klepper, S., & Leamer, E. E. (1984). Consistent sets of estimates for regressions with errors in all variables. Econometrica, 52, 163-184.
44. Klein, R., & Vella, F. (2009). A semiparametric model for binary response and continuous outcomes under index heteroskedasticity. Journal of Applied Econometrics, 24, 735-762.
45. Koopmans, T. (1937). Linear regression analysis of economic time series, Amsterdam, Netherlands Econometric Institute, Harrlem, de Erwen F Bohn N.V.
46. Kreider, B. (2010). Regression coefficient identification decay in the presence of infrequent classification errors. Review of Economics and Statistics, 92, 1017-1023.
47. Kreider, B., & Pepper, J. V. (2007). Disability and employment: Reevaluating the evidence in light of reporting errors. Journal of the American Statistical Association, 102, 432-441.
48. Lewbel, A. (1997). Constructing instruments for regressions with measurement error when no additional data are available, with an application to patents and r&d. Econometrica, 65, 1201-1213.
49. Lewbel, A. (2007). Estimation of average treatment effects with misclassification. Econometrica, 75, 537-551.
50. Li, T., Trivedi, P. K., & Guo, J. (2003). Modeling response bias in count: A structural approach with an application to the national crime victimization survey data. Sociological Methods & Research, 31, 514-544.
51. Mellow, W., & Sider, H. (1983). Accuracy of response to labor market surveys: Evidence and implications. Journal of Labor Economics, 1, 331-344.
52. Millimet, D. L., & Tchernis, R. (2009). On the specification of propensity scores: With applications to the analysis of trade policies. Journal of Business & Economic Statistics, 27, 397-415.
53. Millimet, D. L., & Tchernis, R. (2010). Estimating treatment effects without an exclusion restriction: with an application to the school breakfast program, NBER Working Paper no. 15539.
54. Mitchell, C. (2010). Are divorce studies trustworthy? The effects of survey nonresponse and response errors. Journal of Marriage and Family, 72, 893-905.
55. Mroz, T. (1999). Discrete factor approximations for use in simultaneous equation models: Estimating the impact of a dummy endogenous variable on a continuous outcome. Journal of Econometrics, 92, 233-274.
56. Navarro, S. (2008). Control function. In: S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave dictionary of economics (2nd ed.). London: Palgrave Macmillan Press.
57. Neyman, J. (1923). On the application of probability theory to agricultural experiments: Essay on principles. Section 9, translated in Statistical Science (with discussion), 5, 465-480, (1990).
58. Peracchi, F. (2006). Educational wage premia and the distribution of earnings. In: E. Hanushek& F. Welch (Eds.), Handbook of the economics of education (Vol. 1). Elsevier B.V.
59. Poterba, J. M., & Summers, L. H. (1986). Reporting errors and labor market dynamics. Econometrica, 54, 1319-1338.
60. Reiersøl, O. (1950). Identifiability of a linear relation between variables which are subject to error. Econometrica, 18, 375-389.
61. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
62. Roy, A. D. (1951). Some thoughts on the distribution of income. Oxford Economic Papers, 3, 135-146.
63. Rubin, D. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688-701.
64. Rubin, D. (1986). Statistics and causal inference: Which ifs have causal answers. Journal of the American Statistical Association, 81, 961-962.
65. Scharfstein, D. O., Rotnitzky, A., & Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models. Journal of the American Statistical Association, 94, 1096-1120.
66. Schennach, S. M. (2004). Estimation of nonlinear models with measurement error. Econometrica, 72, 33-75.
67. Smith, J. A., & Todd, P. E. (2005). Does matching overcome lalonde's critique? Journal of Econometrics, 125, 305-353.
68. Strauss, J., & Thomas, D. (1995). Human resources: Empirical models of household decisions. In: J. R. Behrman & T. N. Srinivasan (Eds.), Handbook of development economics (Vol. 3A, pp. 1885-2023). North Holland.
69. Villanueva, E. V. (2001). The validity of self-reported weight in us adults: A population based cross-sectional study. BMC Public Health, 1(11).
70. Whittemore, A. S., & Gong, G. (1991). Poisson regression with misclassified counts: Application to cervical cancer mortality rates. Journal of the Royal Statistical Society. Series C (Applied Statistics), 40, 81-93.
71. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.