1932

Abstract

For the past 10 years, the topic of set identification has been much studied in the econometric literature. Classical inference methods have been generalized to the case in which moment inequalities and equalities define a set instead of a point. We review several instances of partial identification by focusing on examples in which the underlying economic restrictions are expressed as linear moments. This setting illustrates the fact that convex analysis helps not only for characterizing the identified set but also for inference. From this perspective, we review inference methods using convex analysis or inversion of tests and detail how geometric characterizations can be useful.

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2017-08-02
2024-04-23
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Literature Cited

  1. Andrews DWK, Barwick PJ. 2012. Inference for parameters defined by moment inequalities: a recommended moment selection procedure. Econometrica 80:2805–26 [Google Scholar]
  2. Andrews DWK, Guggenberger P. 2009. Validity of subsampling and plug-in asymptotic inference for parameters defined by moment inequalities. Econom. Theory 25:669–709 [Google Scholar]
  3. Andrews DWK, Shi X. 2013. Inference based on conditional moment inequalities. Econometrica 81:609–66 [Google Scholar]
  4. Andrews DWK, Soares G. 2010. Inference for parameters defined by moment inequalities using generalized moment selection. Econometrica 78:119–57 [Google Scholar]
  5. Armstrong TB. 2013. Bounds in auctions with unobserved heterogeneity. Quant. Econ. 4:377–415 [Google Scholar]
  6. Armstrong TB. 2014. Weighted KS statistics for inference on conditional moment inequalities. J. Econom. 181:92–116 [Google Scholar]
  7. Armstrong TB. 2015. Asymptotically exact inference in conditional moment inequality models. J. Econom. 186:51–65 [Google Scholar]
  8. Beresteanu A, Molchanov I, Molinari F. 2011. Sharp identification regions in models with convex moment predictions. Econometrica 79:1785–821 [Google Scholar]
  9. Beresteanu A, Molchanov I, Molinari F. 2012. Partial identification using random set theory. J. Econom. 166:17–32 [Google Scholar]
  10. Beresteanu A, Molinari F. 2008. Asymptotic properties for a class of partially identified models. Econometrica 76:763–814 [Google Scholar]
  11. Berry S, Reiss P. 2007. Empirical models of entry and market structure. Handbook of Industrial Organization 3 M Armstrong, R Porter 1845–86 Amsterdam: Elsevier [Google Scholar]
  12. Blundell R, Browning M, Crawford I. 2008. Best nonparametric bounds on demand responses. Econometrica 76:1227–62 [Google Scholar]
  13. Blundell R, Gosling A, Ichimura H, Meghir C. 2007. Changes in the distribution of male and female wages accounting for employment composition using bounds. Econometrica 75:323–63 [Google Scholar]
  14. Blundell R, Kristensen D, Matzkin R. 2014. Bounding quantile demand functions using revealed preference inequalities. J. Econom. 117:112–27 [Google Scholar]
  15. Bontemps C, Magnac T, Maurin E. 2012. Set identified linear models. Econometrica 80:1129–55 [Google Scholar]
  16. Bugni F. 2010. Bootstrap inference in partially identified models defined by moment inequalities. Econometrica 78:735–54 [Google Scholar]
  17. Bugni FA, Canay IA, Shi X. 2017. Inference for subvectors and other functions of partially identified parameters in moment inequality models. Quant. Econ. 8:1–38 [Google Scholar]
  18. Canay I. 2010. EL inference for partially identified models: large deviations optimality and bootstrap validity. J. Econom. 156:408–25 [Google Scholar]
  19. Chandrasekhar A, Chernozhukov V, Molinari F, Schrimpf P. 2012. Inference for best linear approximations to set identified functions Work. Pap. 43/12, Cent. Microdata Methods Pract. London:
  20. Chernozhukov V, Chetverikov D, Kato K. 2016. Testing many moment inequalities Work. Pap. 42/16, Cent. Microdata Methods Pract. London:
  21. Chernozhukov V, Hong H, Tamer E. 2007. Inference on parameter sets in econometric models. Econometrica 75:1243–84 [Google Scholar]
  22. Chernozhukov V, Kocatulum E, Menzel K. 2015. Inferences on sets in finance. Quant. Econ. 6:309–58 [Google Scholar]
  23. Chernozhukov V, Lee S, Rosen AM. 2013. Intersection bounds: estimation and inference. Econometrica 81:667–737 [Google Scholar]
  24. Chesher A. 2005. Nonparametric identification under discrete variation. Econometrica 73:1525–50 [Google Scholar]
  25. Chesher A. 2010. Instrumental variable models for discrete outcomes. Econometrica 78:575–601 [Google Scholar]
  26. Chesher A, Rosen A. 2015a. Characterizations of identified sets delivered by structural econometric models Work. Pap. 63/15, Cent. Microdata Methods Pract. London:
  27. Chesher A, Rosen A. 2015b. Identification of the distribution of valuations in an incomplete model of English auctions Work. Pap. 30/15, Cent. Microdata Methods Pract. London:
  28. Chesher A, Smolinski K. 2012. IV models of ordered choice. J. Econom. 166:133–48 [Google Scholar]
  29. Ciliberto F, Tamer E. 2009. Market structure and multiple equilibria in airline markets. Econometrica 77:1791–828 [Google Scholar]
  30. Conley T, Hansen C, Rossi P. 2012. Plausibly exogenous. Rev. Econ. Stat. 94:260–72 [Google Scholar]
  31. Davezies L, d'Haultfoeuille X. 2012. Partial identification with missing outcomes Unpublished manuscript, Cent. Rech. Écon. Stat., Inst. Natl. Stat. Études Écon. Paris:
  32. de Paula A. 2016. Econometrics of network models Work. Pap. 06/16, Cent. Microdata Methods Pract. London:
  33. de Paula A, Richards-Shubik S, Tamer E. 2015. Identification of preferences in network formation games Work. Pap. 29/15, Cent. Microdata Methods Pract. London:
  34. Fan Y, Park S. 2010. Sharp bounds on the distribution of the treatment effects and their statistical inference. Econom. Theory 26:931–51 [Google Scholar]
  35. Fan Y, Wu J. 2010. Partial identification of the distribution of treatment effects in switching regimes models and its confidence sets. Rev. Econ. Stud. 77:1002–41 [Google Scholar]
  36. Fréchet M. 1951. Sur les tableaux de corrélation dont les marges sont données. Ann. Univ. Lyon III Sér. Sci. A 9:53–77 [Google Scholar]
  37. Frisch R. 1934. Statistical Confluence Analysis by Means of Complete Regression Systems Oslo, Nor.: Univ. Inst. Econ.
  38. Galichon A, Henry M. 2009. A test of non-identifying restrictions and confidence regions for partially identified parameters. J. Econom. 152:186–96 [Google Scholar]
  39. Galichon A, Henry M. 2011. Set identification in models with multiple equilibria. Rev. Econ. Stud. 78:1264–98 [Google Scholar]
  40. Gentry M, Li T. 2014. Identification in auctions with selective entry. Econometrica 82:315–44 [Google Scholar]
  41. Gini C. 1921. Sull'interpoliazone di una retta quando i valori della variabile indipendente sono affetti da errori accidentali. Metroeconomica 1:63–82 [Google Scholar]
  42. Gomez F, Pacini D. 2013. Measuring inequality from contaminated data Unpublished manuscript Univ. Bristol Bristol, UK:
  43. Grieco PLE. 2014. Discrete games with flexible information structures: an application to local grocery markets. RAND J. Econ. 45:303–40 [Google Scholar]
  44. Guggenberger P, Kleibergen F, Mavroeidis S, Chen L. 2012. On the asymptotic sizes of subset Anderson–Rubin and Lagrange multiplier tests in linear instrumental variables regression. Econometrica 80:2649–66 [Google Scholar]
  45. Haile P, Tamer E. 2003. Inference with an incomplete model of English auctions. J. Polit. Econ. 111:1–51 [Google Scholar]
  46. Heckman JJ. 1978. Dummy endogenous variables in a simultaneous equation system. Econometrica 46:931–60 [Google Scholar]
  47. Henry M, Meango R, Queyranne M. 2015. Combinatorial approach to inference in partially identified incomplete structural models. Quant. Econ. 6:499–529 [Google Scholar]
  48. Henry M, Mourifié I. 2013. Euclidean revealed preferences: testing the spatial voting model. J. Appl. Econom. 28:4650–66 [Google Scholar]
  49. Henry M, Onatski A. 2012. Set coverage and robust policy. Econ. Lett. 115:2256–57 [Google Scholar]
  50. Ho K, Rosen A. 2015. Partial identification in applied research: benefits and challenges Work. Pap. 64/15, Cent. Microdata Methods Pract. London:
  51. Hoeffding W. 1940. Masstabinvariante Korrelationstheorie. Schr. Math. Inst. Inst. Angew. Math. Univ. Berlin 5:3179–233 [Google Scholar]
  52. Honoré B, Lleras-Muney A. 2004. Bounds in competing risks models and the war on cancer. Econometrica 74:1675–98 [Google Scholar]
  53. Horowitz JL, Manski CF. 1995. Identification and robustness with contaminated and corrupted data. Econometrica 63:281–302 [Google Scholar]
  54. Horowitz JL, Manski CF. 2006. Identification and estimation of statistical functions using incomplete data. J. Econom. 132:445–59 [Google Scholar]
  55. Horowitz JL, Manski CF, Ponomareva M, Stoye J. 2003. Computation of bounds on population parameters when the data are incomplete. Reliab. Comput. 9:419–40 [Google Scholar]
  56. Hotz VJ, Mullin C, Sanders SG. 1997. Bounding causal effects using data from a contaminated natural experiment: analysing the effects of teenage childbearing. Rev. Econ. Stud. 64:4575–603 [Google Scholar]
  57. Imbens G, Manski CF. 2004. Confidence intervals for partially identified parameters. Econometrica 72:1845–59 [Google Scholar]
  58. Kaido H, Molinari F, Stoye J. 2016. Confidence intervals for projections of partially identified parameters. arXiv1601.0094 [math.ST]
  59. Kaido H, Santos A. 2014. Asymptotically efficient estimation of models defined by convex moment inequalities. Econometrica 82:1387–413 [Google Scholar]
  60. Kitagawa T. 2012. Estimation and inference for set-identified parameters using posterior lower probability Work. Pap., Cent. Microdata Methods Pract. London:
  61. Klepper S, Leamer EE. 1984. Consistent sets of estimates for regressions with errors in all variables. Econometrica 52:163–83 [Google Scholar]
  62. Kline B, Tamer E. 2016. Bayesian inference in a class of partially identified models. Quant. Econ. 7:329–66 [Google Scholar]
  63. Komarova T. 2013. Partial identification in asymmetric auctions in the absence of independence. Econom. J. 16:160–92 [Google Scholar]
  64. Leamer EE. 1987. Errors in variables in linear systems. Econometrica 55:4893–909 [Google Scholar]
  65. Lee DS. 2009. Training, wages, and sample selection: estimating sharp bounds on treatment effects. Rev. Econ. Stud. 76:31071–102 [Google Scholar]
  66. Lee S, Song K, Whang Y. 2014. Testing for a general class of functional inequalities Work. Pap. 09/14, Cent. Microdata Methods Pract. London:
  67. Liao Y, Jiang W. 2010. Bayesian analysis in moment inequality models. Ann. Stat. 38:1275–316 [Google Scholar]
  68. Liao Y, Simoni A. 2016. Bayesian inference for partially identified convex models: Is it valid for frequentist inference? Work. Pap 2016–07 Rutgers Univ. New Brunswick, NJ:
  69. Liu X, Shao Y. 2003. Asymptotics for likelihood ratio tests under loss of identifiability. Ann. Stat. 31:807–32 [Google Scholar]
  70. Magnac T, Maurin E. 2008. Partial identification in monotone binary models: discrete regressors and interval data. Rev. Econ. Stud. 75:835–64 [Google Scholar]
  71. Manski CF. 1989. Anatomy of the selection problem. J. Hum. Resour. 24:343–60 [Google Scholar]
  72. Manski CF. 2003. Partial Identification of Probability Distributions Berlin: Springer-Verlag
  73. Manski CF, Pepper JV. 2000. Monotone instrumental variables: with an application to the returns to schooling. Econometrica 68:4997–1010 [Google Scholar]
  74. Manski CF, Pepper JV. 2013. Deterrence and the death penalty: partial identification analysis using repeated cross sections. J. Quant. Criminol. 29:1123–41 [Google Scholar]
  75. Manski CF, Tamer E. 2002. Inference on regressions with interval data on a regressor or outcome. Econometrica 70:519–46 [Google Scholar]
  76. Marschak J, Andrews WH. 1944. Random simultaneous equations and the theory of production. Econometrica 12:143–205 [Google Scholar]
  77. Menzel K. 2014. Consistent estimation with many moment inequalities. J. Econom. 182:2329–50 [Google Scholar]
  78. Molchanov I. 2005. Theory of Random Sets Berlin: Springer-Verlag
  79. Molchanov I, Molinari F. 2015. Applications of random set theory in econometrics. Annu. Rev. Econ. 6:1229–51 [Google Scholar]
  80. Moon HR, Schorfheide F. 2012. Bayesian and frequentist inference in partially identified models. Econometrica 80:2755–82 [Google Scholar]
  81. Nevo A, Rosen A. 2012. Identification with imperfect instruments. Rev. Econ. Stat. 94:3659–71 [Google Scholar]
  82. Pacini D. 2017. Two-sample least squares projection. Econom. Rev. In press. https://doi.org/10.1080/07474938.2016.1222068 [Crossref]
  83. Pakes A. 2010. Alternative models for moment inequalities. Econometrica 78:1783–822 [Google Scholar]
  84. Pakes A, Porter J, Ho K, Ishii J. 2015. Moment inequalities and their applications. Econometrica 83:1315–34 [Google Scholar]
  85. Ponomareva M. 2010. Inference in models defined by conditional moment inequalities with continuous covariates Unpublished manuscript Dep. Econ., Univ. West. Ont. London, Can.:
  86. Ponomareva M, Tamer E. 2011. Misspecification in moment inequality models: back to moment equalities. Econom. J. 14:186–203 [Google Scholar]
  87. Popper K. 2005. The Logic of Scientific Discovery London/New York: Routledge/Taylor & Francis
  88. Redner RA. 1981. On the assumptions used to prove asymptotic normality of maximum likelihood estimates. Ann. Math. Stat. 9:225–28 [Google Scholar]
  89. Reiersol O. 1941. Confluence analysis by means of lag moments and other methods of confluence analysis. Econometrica 9:1–24 [Google Scholar]
  90. Ridder GE, Moffitt R. 2007. The econometrics of data combination. Handbook of Econometrics 6 JJ Heckman, EE Leamer 5469–547 Amsterdam: North-Holland [Google Scholar]
  91. Rockafellar RT. 1970. Convex Analysis Princeton, NJ: Princeton Univ. Press
  92. Romano JP, Shaikh AM. 2008. Inference for identifiable parameters in partially identified econometric models. J. Stat. Plan. Inference 138:2786–807 [Google Scholar]
  93. Romano JP, Shaikh AM. 2010. Inference for the identified set in partially identified econometric models. Econometrica 78:1169–211 [Google Scholar]
  94. Romano JP, Shaikh AM, Wolf M. 2014. A practical two-step method for testing moment inequalities. Econometrica 82:51979–2002 [Google Scholar]
  95. Rosen AM. 2008. Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities. J. Econom. 146:107–17 [Google Scholar]
  96. Rothenberg TJ. 1971. Identification in parametric models. Econometrica 39:577–91 [Google Scholar]
  97. Sheng S. 2014. A structural econometric analysis of network formation games Work. Pap. Dep. Econ., Univ. Calif. Los Angeles:
  98. Silvapulle M, Sen P. 2005. Constrained Statistical Inference London: Wiley
  99. Stoye J. 2007. Bounds on generalized linear predictors with incomplete outcome data. Reliab. Comput. 13:293–302 [Google Scholar]
  100. Stoye J. 2009. More on confidence intervals for partially identified parameters. Econometrica 77:1299–315 [Google Scholar]
  101. Tamer E. 2003. Incomplete simultaneous discrete response model with multiple equilibria. Rev. Econ. Stud. 70:1147–65 [Google Scholar]
  102. Tamer E. 2010. Partial identification in econometrics. Annu. Rev. Econ. 2:167–95 [Google Scholar]
  103. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data Cambridge, MA: MIT Press
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