The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Partial Identification in Econometrics

  • Charles F. Manski
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2407

Abstract

Econometricians long thought of identification as a binary event: a parameter is either identified or not. Empirical researchers combined available data with assumptions that yield point identification, and reported point estimates of parameters. Yet there is enormous scope for fruitful inference using weaker and more credible assumptions that partially identify parameters. Until recently, study of partial identification was rare and fragmented. However, a coherent body of research took shape in the 1990s and has grown rapidly. This research has yielded new approaches to inference with missing outcome data, analysis of treatment response, and other important problems of empirical research.

Keywords

Counterfactuals Discrete response analysis Errors in variables Gini coefficient Identification region Law of Total Probability Nonparametric methods Parametric prediction Partial identification in econometrics Reverse regression Sampling Statistical inference Treatment response 
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Bibliography

  1. Balke, A., and J. Pearl. 1997. Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association 92: 1171–1177.CrossRefGoogle Scholar
  2. Blundell, R., A. Gosling, H. Ichimura, and C. Meghir. 2004. Changes in the distribution of male and female wages accounting for employment composition using bounds. In Working Paper W04/25. London: Institute of Fiscal Studies.Google Scholar
  3. Bollinger, C. 1996. Bounding mean regressions when a binary regressor is mismeasured. Journal of Econometrics 73: 387–399.CrossRefGoogle Scholar
  4. Brock, W. 2005. Profiling problems with partially identified structure. Mimeo. Madison: WI: Department of Economics, University of Wisconsin-Madison.Google Scholar
  5. Chernozhukov, V., H. Hong, and E. Tamer. 2004. Parameter set inference in a class of econometric models. Mimeo. Evanston: IL: Department of Economics, Northwestern University.Google Scholar
  6. Ciliberto, F., and E. Tamer. 2004. Evanston, IL: Market structure and multiple equilibria in airline markets. Mimeo. Evanston: IL: Department of Economics, Northwestern University.Google Scholar
  7. Cochran, W. 1977. Sampling techniques. 3rd ed. New York: Wiley.Google Scholar
  8. Cochran, W., F. Mosteller, and J. Tukey. 1954. Statistical problems of the kinsey report on sexual behavior in the human male. Washington, DC: American Statistical Association.Google Scholar
  9. Cross, P., and C. Manski. 2002. Regressions, short and long. Econometrica 70: 357–368.CrossRefGoogle Scholar
  10. Dominitz, J., and R. Sherman. 2004. Sharp bounds under contaminated or corrupted sampling with verification, with an application to environmental pollutant data. Journal of Agricultural, Biological, and Environmental Statistics 9: 319–338.CrossRefGoogle Scholar
  11. Duncan, O., and B. Davis. 1953. An alternative to ecological correlation. American Sociological Review 18: 665–666.CrossRefGoogle Scholar
  12. Frisch, R. 1934. Statistical confluence analysis by means of complete regression systems. Oslo: University Institute for Economics.Google Scholar
  13. Ginther, D. 2002. Alternative estimates of the effect of schooling on earnings. The Review of Economics and Statistics 82: 103–116.CrossRefGoogle Scholar
  14. Haile, P., and E. Tamer. 2003. Inference with an incomplete model of English auctions. Journal of Political Economy 111: 1–51.CrossRefGoogle Scholar
  15. Heckman, J., J. Smith, and N. Clements. 1997. Making the most out of programme evaluations and social experiments: accounting for heterogeneity in programme impacts. Review of Economic Studies 64: 487–535.CrossRefGoogle Scholar
  16. Heckman, J., and E. Vytlacil. 2001. Localinstrumental variables. In Nonlinear statistical inference: essays in honor of takeshi amemiya, ed. C. Hsiao, K. Morimune, and J. Powell. Cambridge, MA: Cambridge University Press.Google Scholar
  17. Horowitz, J., and C. Manski. 1995. Identification and robustness with contaminated and corrupted data. Econometrica 63: 281–302.CrossRefGoogle Scholar
  18. Horowitz, J., and C. Manski. 1998. Censoring of outcomes and regressors due to survey nonresponse: identification and estimation using weights and imputations. Journal of Econometrics 84: 37–58.CrossRefGoogle Scholar
  19. Horowitz, J., and C. Manski. 2000. Nonparametric analysis of randomized experiments with missing covariate and outcome data. Journal of the American Statistical Association 95: 77–84.CrossRefGoogle Scholar
  20. Horowitz, J., and C. Manski. 2006. Identification and estimation of statistical functionals using incomplete data. Journal of Econometrics 132(2): 445–459.CrossRefGoogle Scholar
  21. Horowitz, J., C. Manski, M. Ponomareva, and J. Stoye. 2003. Computation of bounds on population parameters when the data are incomplete. Reliable Computing 9: 419–440.CrossRefGoogle Scholar
  22. Hotz, J., C. Mullin, and S. Sanders. 1997. Bounding causal effects using data from a contaminated natural experiment: analyzing the effects of teenage childbearing. Review of Economic Studies 64: 575–603.CrossRefGoogle Scholar
  23. Imbens, G., and C. Manski. 2004. Confidence intervals for partially identified parameters. Econometrica 72: 1845–1857.CrossRefGoogle Scholar
  24. King, G., and L. Zeng. 2002. Estimating risk and rate levels, ratios and differences in case-control studies. Statistics in Medicine 21: 1409–1427.CrossRefGoogle Scholar
  25. Klepper, S., and E. Leamer. 1984. Consistent sets of estimates for regressions with errors in all variables. Econometrica 52: 163–183.CrossRefGoogle Scholar
  26. Koopmans, T. 1949. Identification problems in economic model construction. Econometrica 17: 125–144.CrossRefGoogle Scholar
  27. Kreider, B., and J. Pepper. 2004. Disability and employment: reevaluating the evidence in light of reporting errors. Mimeo. Ames: IA: Department of Economics, Iowa State University.Google Scholar
  28. Manski, C. 1989. Anatomy of the selection problem. Journal of Human Resources 24: 343–360.CrossRefGoogle Scholar
  29. Manski, C. 1990. Nonparametric bounds on treatment effects. American Economic Review: Papers and Proceedings 80: 319–323.Google Scholar
  30. Manski, C. 1994. The selection problem. In Advances in Econometrics, Sixth World Congress, ed. C. Sims. Cambridge: Cambridge University Press.Google Scholar
  31. Manski, C. 1995. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.Google Scholar
  32. Manski, C. 1997a. Monotone treatment response. Econometrica 65: 1311–1334.CrossRefGoogle Scholar
  33. Manski, C. 1997b. The mixing problem in programme evaluation. Review of Economic Studies 64: 537–553.CrossRefGoogle Scholar
  34. Manski, C. 2000. Identification problems and decisions under ambiguity: empirical analysis of treatment response and normative analysis of treatment choice. Journal of Econometrics 95: 415–442.CrossRefGoogle Scholar
  35. Manski, C. 2001. Nonparametric identification under response-based sampling. In Nonlinear statistical inference: Essays in honor of Takeshi Amemiya, ed. C. Hsiao, K. Morimune, and J. Powell. New York: Cambridge University Press.Google Scholar
  36. Manski, C. 2002. Treatment choice under ambiguity induced by inferential problems. Journal of Statistical Planning and Inference 105: 67–82.CrossRefGoogle Scholar
  37. Manski, C. 2003. Partial identification of probability distributions. New York: Springer-Verlag.Google Scholar
  38. Manski, C. 2005a. Social choice with partial knowledge of treatment response. Princeton: Princeton University Press.Google Scholar
  39. Manski, C. 2005b. Search profiling with partial knowledge of treatment response. Mimeo. Evanston: IL: Department of Economics, Northwestern University.CrossRefGoogle Scholar
  40. Manski, C. 2006. Minimax-regret treatment choice with missing outcome data. Journal of Econometrics f139(1): 105–115.Google Scholar
  41. Manski, C., and D. Nagin. 1998. Bounding disagreements about treatment effects: a case study of sentencing and recidivism. Sociological Methodology 28: 99–137.CrossRefGoogle Scholar
  42. Manski, C., and J. Pepper. 2000. Monotone instrumental variables: with an application to the returns to schooling. Econometrica 68: 997–1010.CrossRefGoogle Scholar
  43. Manski, C., G. Sandefur, S. McLanahan, and D. Powers. 1992. Alternative estimates of the effect of family structure during adolescence on high school graduation. Journal of the American Statistical Association 87: 25–37.CrossRefGoogle Scholar
  44. Manski, C., and E. Tamer. 2002. Inference on regressions with interval data on a regressor or outcome. Econometrica 70: 519–546.CrossRefGoogle Scholar
  45. Moinari, F. 2002. Missing treatments. Mimeo. Ithaca: NY: Department of Economics, Cornell University.Google Scholar
  46. Molinari, F. 2004. Partial identification of probability distributions with misclassified data. Mimeo. Ithaca: NY: Department of Economics, Cornell University.Google Scholar
  47. Pepper, J. 2000. The intergenerational transmission of welfare receipt: a nonparametric bounds analysis. The Review of Economics and Statistics 82: 472–488.CrossRefGoogle Scholar
  48. Pepper, J. 2003. Using experiments to evaluate performance standards: what do welfare-to-work demonstrations reveal to welfare reformers? Journal of Human Resources 38: 860–880.CrossRefGoogle Scholar
  49. Peterson, A. 1976. Bounds for a joint distribution function with fixed subdistribution functions: application to competing risks. Proceedings of the National Academy of Sciences of the United States of America 73: 11–13.CrossRefGoogle Scholar
  50. Reiersol, O. 1941. Confluence analysis by means of lag moments and other methods of confluence analysis. Econometrica 9: 1–24.CrossRefGoogle Scholar
  51. Robins, J. 1989. The analysis of randomized and non-randomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: A Focus on AIDS, ed. L. Sechrest, H. Freeman, and A. Mulley. Washington, DC: NCHSR, US Public Health Service.Google Scholar
  52. Scharfstein, J., C. Manski, and J. Anthony. 2004. On the construction of bounds in prospective studies with missing ordinal outcomes: application to the good behavior game trial. Biometrics 60: 154–164.CrossRefGoogle Scholar
  53. Stoye, J. 2005. Partial identification of spread parameters when some data are missing. Mimeo. Evanston: IL: Department of Economics, Northwestern University.Google Scholar
  54. Zaffalon, M. 2002. Exact credal treatment of missing data. Journal of Statistical Planning and Inference 105: 105–122.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Charles F. Manski
    • 1
  1. 1.