1932

Abstract

Identification strategies concern what can be learned about the value of a parameter based on the data and the model assumptions. The literature on partial identification is motivated by the fact that it is not possible to learn the exact value of the parameter for many empirically relevant cases. A typical result in the literature on partial identification is a statement about characterizing the identified set, which summarizes what can be learned about the parameter of interest given the data and model assumptions. For instance, this may mean that the value of the parameter can be learned to be necessarily within some set of values. First, the review surveys the general frameworks that have been developed for conducting a partial identification analysis. Second, the review surveys some of the more recent results on partial identification.

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2023-09-13
2024-06-19
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Literature Cited

  1. Abrevaya J, Hausman JA, Khan S. 2010. Testing for causal effects in a generalized regression model with endogenous regressors. Econometrica 78:62043–61
    [Google Scholar]
  2. Aguiar VH, Kashaev N. 2021. Stochastic revealed preferences with measurement error. Rev. Econ. Stud. 88:42042–93
    [Google Scholar]
  3. Allen R, Rehbeck J. 2022. Latent complementarity in bundles models. J. Econometr. 228:2322–41
    [Google Scholar]
  4. Aradillas-López A. 2010. Semiparametric estimation of a simultaneous game with incomplete information. J. Econometr. 157:2409–31
    [Google Scholar]
  5. Aradillas-López A. 2011. Nonparametric probability bounds for Nash equilibrium actions in a simultaneous discrete game. Quant. Econ. 2:2135–71
    [Google Scholar]
  6. Aradillas-López A. 2012. Pairwise-difference estimation of incomplete information games. J. Econometr. 168:1120–40
    [Google Scholar]
  7. Aradillas-López A. 2020. The econometrics of static games. Annu. Rev. Econ. 12:135–65
    [Google Scholar]
  8. Aradillas-López A, Gandhi A 2016. Estimation of games with ordered actions: an application to chain-store entry. Quant. Econ. 7:3727–80
    [Google Scholar]
  9. Aradillas-López A, Rosen AM 2022. Inference in ordered response games with complete information. J. Econometr. 226:2451–76
    [Google Scholar]
  10. Aradillas-López A, Tamer E 2008. The identification power of equilibrium in simple games. J. Bus. Econ. Stat. 26:3261–83
    [Google Scholar]
  11. Arias JE, Rubio-Ramírez JF, Waggoner DF. 2018. Inference based on structural vector autoregressions identified with sign and zero restrictions: theory and applications. Econometrica 86:2685–720
    [Google Scholar]
  12. Artstein Z. 1983. Distributions of random sets and random selections. Isr. J. Math. 46:4313–24
    [Google Scholar]
  13. Bajari P, Hong H, Krainer J, Nekipelov D. 2010a. Estimating static models of strategic interactions. J. Bus. Econ. Stat. 28:4469–82
    [Google Scholar]
  14. Bajari P, Hong H, Ryan SP. 2010b. Identification and estimation of a discrete game of complete information. Econometrica 78:51529–68
    [Google Scholar]
  15. Barseghyan L, Coughlin M, Molinari F, Teitelbaum JC. 2021. Heterogeneous choice sets and preferences. Econometrica 89:52015–48
    [Google Scholar]
  16. Barseghyan L, Molinari F, Teitelbaum JC. 2016. Inference under stability of risk preferences. Quant. Econ. 7:2367–409
    [Google Scholar]
  17. Baumeister C, Hamilton JD. 2015. Sign restrictions, structural vector autoregressions, and useful prior information. Econometrica 83:51963–99
    [Google Scholar]
  18. Beresteanu A, Molchanov I, Molinari F. 2011. Sharp identification regions in models with convex moment predictions. Econometrica 79:61785–821
    [Google Scholar]
  19. Beresteanu A, Molchanov I, Molinari F. 2012. Partial identification using random set theory. J. Econometr. 166:117–32
    [Google Scholar]
  20. Beresteanu A, Molinari F. 2008. Asymptotic properties for a class of partially identified models. Econometrica 76:4763–814
    [Google Scholar]
  21. Bergemann D, Morris S 2013. Robust predictions in games with incomplete information. Econometrica 81:41251–308
    [Google Scholar]
  22. Blundell R, Browning M, Crawford I 2008. Best nonparametric bounds on demand responses. Econometrica 76:61227–62
    [Google Scholar]
  23. Blundell R, Kristensen D, Matzkin R. 2014. Bounding quantile demand functions using revealed preference inequalities. J. Econometr. 179:2112–27
    [Google Scholar]
  24. Bonhomme S. 2020. Econometric analysis of bipartite networks. The Econometric Analysis of Network Data B Graham, A de Paula 83–121. Amsterdam: Elsevier
    [Google Scholar]
  25. Bontemps C, Magnac T. 2017. Set identification, moment restrictions, and inference. Annu. Rev. Econ. 9:103–29
    [Google Scholar]
  26. Canay IA, Shaikh AM 2017. Practical and theoretical advances in inference for partially identified models. Advances in Economics and Econometrics, Vol. 2 B Honoré, A Pakes, M Piazzesi, L Samuelson 271–306. Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  27. Canova F, De Nicolo G. 2002. Monetary disturbances matter for business fluctuations in the G-7. J. Monet. Econ. 49:61131–59
    [Google Scholar]
  28. Cattaneo MD, Ma X, Masatlioglu Y, Suleymanov E. 2020. A random attention model. J. Political Econ. 128:72796–836
    [Google Scholar]
  29. Chalak K. 2019. Identification of average effects under magnitude and sign restrictions on confounding. Quant. Econ. 10:41619–57
    [Google Scholar]
  30. Chandrasekhar A 2016. Econometrics of network formation. The Oxford Handbook of the Economics of Networks Y Bramoullé, A Galeotti, BW Rogers 303–57. Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  31. Chen X, Christensen TM, Tamer E. 2018. Monte Carlo confidence sets for identified sets. Econometrica 86:61965–2018
    [Google Scholar]
  32. Cherchye L, De Rock B, Lewbel A, Vermeulen F. 2015. Sharing rule identification for general collective consumption models. Econometrica 83:52001–41
    [Google Scholar]
  33. Cherchye L, Demuynck T, De Rock B. 2019. Bounding counterfactual demand with unobserved heterogeneity and endogenous expenditures. J. Econometr. 211:2483–506
    [Google Scholar]
  34. Chernozhukov V, Fernández-Val I, Hahn J, Newey W. 2013. Average and quantile effects in nonseparable panel models. Econometrica 81:2535–80
    [Google Scholar]
  35. Chernozhukov V, Hong H, Tamer E. 2007. Estimation and confidence regions for parameter sets in econometric models. Econometrica 75:51243–84
    [Google Scholar]
  36. Chesher A, Rosen AM. 2017. Generalized instrumental variable models. Econometrica 85:3959–89
    [Google Scholar]
  37. Chesher A, Rosen AM 2020. Generalized instrumental variable models, methods, and applications. Handbook of Econometrics, Vol. 7 SN Durlauf, LP Hansen, JJ Heckman, RL Matzkin 1–110. Amsterdam: Elsevier
    [Google Scholar]
  38. Chesher A, Rosen AM, Smolinski K. 2013. An instrumental variable model of multiple discrete choice. Quant. Econ. 4:2157–96
    [Google Scholar]
  39. Chetverikov D, Santos A, Shaikh AM. 2018. The econometrics of shape restrictions. Annu. Rev. Econ. 10:31–63
    [Google Scholar]
  40. Ciliberto F, Murry C, Tamer E. 2021. Market structure and competition in airline markets. J. Political Econ. 129:112995–3038
    [Google Scholar]
  41. Ciliberto F, Tamer E. 2009. Market structure and multiple equilibria in airline markets. Econometrica 77:61791–828
    [Google Scholar]
  42. Crawford I, De Rock B. 2014. Empirical revealed preference. Annu. Rev. Econ. 6:503–24
    [Google Scholar]
  43. Echenique F. 2020. New developments in revealed preference theory: decisions under risk, uncertainty, and intertemporal choice. Annu. Rev. Econ. 12:299–316
    [Google Scholar]
  44. Fan Y, Sherman R, Shum M. 2014. Identifying treatment effects under data combination. Econometrica 82:2811–22
    [Google Scholar]
  45. Fang Z, Santos A, Shaikh AM, Torgovitsky A. 2023. Inference for large-scale linear systems with known coefficients. Econometrica 91:1299–327
    [Google Scholar]
  46. Faust J. 1998. The robustness of identified VAR conclusions about money. Carnegie-Rochester Conf. Ser. Public Policy 49:207–44
    [Google Scholar]
  47. Fréchet M. 1951. Sur les tableaux de corrélation dont les marges sont données. Ann. Univ. Lyon Sect. A 14:53–77
    [Google Scholar]
  48. Frisch R. 1934. Statistical Confluence Analysis by Means of Complete Regression Systems Oslo, Nor.: Univ. Inst. Econ.
    [Google Scholar]
  49. Fry R, Pagan A. 2011. Sign restrictions in structural vector autoregressions: a critical review. J. Econ. Lit. 49:4938–60
    [Google Scholar]
  50. Gafarov B, Meier M, Olea JLM. 2018. Delta-method inference for a class of set-identified SVARs. J. Econometr. 203:2316–27
    [Google Scholar]
  51. Galichon A. 2018. Optimal Transport Methods in Economics Princeton, NJ: Princeton Univ. Press
    [Google Scholar]
  52. Galichon A, Henry M. 2009. A test of non-identifying restrictions and confidence regions for partially identified parameters. J. Econometr. 152:2186–96
    [Google Scholar]
  53. Galichon A, Henry M. 2011. Set identification in models with multiple equilibria. Rev. Econ. Stud. 78:41264–98
    [Google Scholar]
  54. Giacomini R, Kitagawa T. 2021. Robust Bayesian inference for set-identified models. Econometrica 89:41519–56
    [Google Scholar]
  55. Giacomini R, Kitagawa T, Read M. 2022. Robust Bayesian inference in proxy SVARs. J. Econometr. 228:1107–26
    [Google Scholar]
  56. Gini C. 1921. Sull'interpolazione di una retta quando i valori della variabile indipendente sono affetti da errori accidentali [On the interpolation of a straight line when the values of the independent variable are affected by accidental errors]. Metron 1:363–82
    [Google Scholar]
  57. Giustinelli P. 2011. Non-parametric bounds on quantiles under monotonicity assumptions: with an application to the Italian education returns. J. Appl. Econometr. 26:5783–824
    [Google Scholar]
  58. Graham BS. 2015. Methods of identification in social networks. Annu. Rev. Econ. 7:465–85
    [Google Scholar]
  59. Graham BS. 2020a. Dyadic regression. The Econometric Analysis of Network Data B Graham, Á de Paula 23–40. Amsterdam: Elsevier
    [Google Scholar]
  60. Graham BS 2020b. Network data. Handbook of Econometrics, Vol. 7 SN Durlauf, LP Hansen, JJ Heckman, RL Matzkin 111–218. Amsterdam: Elsevier
    [Google Scholar]
  61. Granziera E, Moon HR, Schorfheide F. 2018. Inference for VARs identified with sign restrictions. Quant. Econ. 9:31087–121
    [Google Scholar]
  62. Grieco PL. 2014. Discrete games with flexible information structures: an application to local grocery markets. RAND J. Econ. 45:2303–40
    [Google Scholar]
  63. Gualdani C. 2021. An econometric model of network formation with an application to board interlocks between firms. J. Econometr. 224:2345–70
    [Google Scholar]
  64. Hausman JA, Newey WK. 2016. Individual heterogeneity and average welfare. Econometrica 84:31225–48
    [Google Scholar]
  65. Ho K, Rosen AM 2017. Partial identification in applied research: benefits and challenges. Advances in Economics and Econometrics, Vol. 2 B Honoré, A Pakes, M Piazzesi, L Samuelson 307–59. Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  66. Hoderlein S, Stoye J. 2014. Revealed preferences in a heterogeneous population. Rev. Econ. Stat. 96:2197–213
    [Google Scholar]
  67. Hoeffding W. 1940. Masstabinvariante korrelationstheorie [Scale-invariant correlation theory]. Schr. Math. Inst. Inst. Angew. Math. Univ. Berlin 5:3181–233
    [Google Scholar]
  68. Honoré BE, Tamer E. 2006. Bounds on parameters in panel dynamic discrete choice models. Econometrica 74:3611–29
    [Google Scholar]
  69. Horowitz JL, Manski CF. 1995. Identification and robustness with contaminated and corrupted data. Econometrica 63:2281–302
    [Google Scholar]
  70. Ishihara T. 2021. Partial identification of nonseparable models using binary instruments. Econometr. Theory 37:4817–48
    [Google Scholar]
  71. Jackson MO. 2010. Social and Economic Networks Princeton, NJ: Princeton Univ. Press
    [Google Scholar]
  72. Jackson MO, Wolinsky A. 1996. A strategic model of social and economic networks. J. Econ. Theory 71:144–74
    [Google Scholar]
  73. Khan S, Ponomareva M, Tamer E. 2016. Identification of panel data models with endogenous censoring. J. Econometr. 194:157–75
    [Google Scholar]
  74. Khan S, Ponomareva M, Tamer E. 2020. Identification of dynamic binary response models Work. Pap., Boston Coll. Chestnut Hill, MA:
    [Google Scholar]
  75. Khan S, Tamer E. 2009. Inference on endogenously censored regression models using conditional moment inequalities. J. Econometr. 152:2104–19
    [Google Scholar]
  76. Kim W, Kwon K, Kwon S, Lee S 2018. The identification power of smoothness assumptions in models with counterfactual outcomes. Quant. Econ. 9:2617–42
    [Google Scholar]
  77. Kitamura Y, Stoye J. 2018. Nonparametric analysis of random utility models. Econometrica 86:61883–909
    [Google Scholar]
  78. Kline B. 2011. The Bayesian and frequentist approaches to testing a one-sided hypothesis about a multivariate mean. J. Stat. Plan. Inference 141:93131–41
    [Google Scholar]
  79. Kline B. 2015. Identification of complete information games. J. Econometr. 189:1117–31
    [Google Scholar]
  80. Kline B. 2016a. Identification of the direction of a causal effect by instrumental variables. J. Bus. Econ. Stat. 34:2176–84
    [Google Scholar]
  81. Kline B. 2016b. The empirical content of games with bounded regressors. Quant. Econ. 7:137–81
    [Google Scholar]
  82. Kline B, Pakes A, Tamer E 2021. Moment inequalities and partial identification in industrial organization. Handbook of Industrial Organization, Vol. 4 K Ho, A Hortaçsu, A Lizzeri 345–431. Amsterdam: Elsevier
    [Google Scholar]
  83. Kline P, Santos A. 2013. Sensitivity to missing data assumptions: theory and an evaluation of the US wage structure. Quant. Econ. 4:2231–67
    [Google Scholar]
  84. Kline B, Tamer E. 2012. Bounds for best response functions in binary games. J. Econometr. 166:192–105
    [Google Scholar]
  85. Kline B, Tamer E. 2016. Bayesian inference in a class of partially identified models. Quant. Econ. 7:2329–66
    [Google Scholar]
  86. Kline B, Tamer E. 2018. Identification of treatment effects with selective participation in a randomized trial. Econometr. J. 21:3332–53
    [Google Scholar]
  87. Kline B, Tamer E. 2020. Econometric analysis of models with social interactions. The Econometric Analysis of Network Data B Graham, Á de Paula 149–81. Amsterdam: Elsevier
    [Google Scholar]
  88. Komarova T. 2013. Binary choice models with discrete regressors: identification and misspecification. J. Econometr. 177:114–33
    [Google Scholar]
  89. Leamer EE. 1981. Is it a demand curve, or is it a supply curve? Partial identification through inequality constraints. Rev. Econ. Stat. 63:3319–27
    [Google Scholar]
  90. Lee DS. 2009. Training, wages, and sample selection: estimating sharp bounds on treatment effects. Rev. Econ. Stud. 76:31071–102
    [Google Scholar]
  91. Lewbel A. 2019. The identification zoo: meanings of identification in econometrics. J. Econ. Lit. 57:4835–903
    [Google Scholar]
  92. Liao Y, Jiang W. 2010. Bayesian analysis in moment inequality models. Ann. Stat. 38:1275–316
    [Google Scholar]
  93. Liao Y, Simoni A. 2019. Bayesian inference for partially identified smooth convex models. J. Econometr. 211:2338–60
    [Google Scholar]
  94. Liu N, Vuong Q, Xu H. 2017. Rationalization and identification of binary games with correlated types. J. Econometr. 201:2249–68
    [Google Scholar]
  95. Magnolfi L, Roncoroni C. 2022. Estimation of discrete games with weak assumptions on information. Rev. Econ. Stud. In press. https://doi.org/10.1093/restud/rdac058
    [Google Scholar]
  96. Manski CF. 1975. Maximum score estimation of the stochastic utility model of choice. J. Econometr. 3:3205–28
    [Google Scholar]
  97. Manski CF. 1989. Anatomy of the selection problem. J. Hum. Resour. 24:3343–60
    [Google Scholar]
  98. Manski CF. 1990. Nonparametric bounds on treatment effects. Am. Econ. Rev. 80:2319–23
    [Google Scholar]
  99. Manski CF. 1997. Monotone treatment response. Econometrica 65:61311–34
    [Google Scholar]
  100. Manski CF. 2003. Partial Identification of Probability Distributions. New York: Springer
    [Google Scholar]
  101. Manski CF. 2009. Identification for Prediction and Decision Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  102. Manski CF. 2014. Identification of income–leisure preferences and evaluation of income tax policy. Quant. Econ. 5:1145–74
    [Google Scholar]
  103. Manski CF, Pepper JV. 2000. Monotone instrumental variables: with an application to the returns to schooling. Econometrica 68:4997–1010
    [Google Scholar]
  104. Manski CF, Pepper JV. 2009. More on monotone instrumental variables. Econometr. J. 12:S200–16
    [Google Scholar]
  105. Manski CF, Tamer E. 2002. Inference on regressions with interval data on a regressor or outcome. Econometrica 70:2519–46
    [Google Scholar]
  106. Marschak J, Andrews WH. 1944. Random simultaneous equations and the theory of production. Econometrica 12:3/4143–205
    [Google Scholar]
  107. Masten MA, Poirier A. 2018. Identification of treatment effects under conditional partial independence. Econometrica 86:1317–51
    [Google Scholar]
  108. Masten MA, Poirier A. 2020. Inference on breakdown frontiers. Quant. Econ. 11:141–111
    [Google Scholar]
  109. Masten MA, Poirier A. 2021. Salvaging falsified instrumental variable models. Econometrica 89:31449–69
    [Google Scholar]
  110. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. Handbook of Econometrics, Vol. 4 RF Engle, DL McFadden 2523–58. Amsterdam: Elsevier
    [Google Scholar]
  111. Matzkin RL 2007. Nonparametric identification. Handbook of Econometrics, Vol. 6B JJ Heckman, EE Leamer 5307–68. Amsterdam: Elsevier
    [Google Scholar]
  112. Matzkin RL. 2013. Nonparametric identification in structural economic models. Annu. Rev. Econ. 5:457–86
    [Google Scholar]
  113. Miyauchi Y. 2016. Structural estimation of pairwise stable networks with nonnegative externality. J. Econometr. 195:2224–35
    [Google Scholar]
  114. Molchanov I. 2005. Theory of Random Sets New York: Springer
    [Google Scholar]
  115. Molchanov I, Molinari F. 2014. Applications of random set theory in econometrics. Annu. Rev. Econ. 6:229–51
    [Google Scholar]
  116. Molchanov I, Molinari F. 2018. Random Sets in Econometrics Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  117. Molinari F 2020. Microeconometrics with partial identification. Handbook of Econometrics, Vol. 7 SN Durlauf, LP Hansen, JJ Heckman, RL Matzkin 355–486. Amsterdam: Elsevier
    [Google Scholar]
  118. Moon HR, Schorfheide F. 2012. Bayesian and frequentist inference in partially identified models. Econometrica 80:2755–82
    [Google Scholar]
  119. Mourifie I, Henry M, Meango R. 2020. Sharp bounds and testability of a Roy model of STEM major choices. J. Political Econ. 128:83220–83
    [Google Scholar]
  120. Nevo A, Rosen AM. 2012. Identification with imperfect instruments. Rev. Econ. Stat. 94:3659–71
    [Google Scholar]
  121. Okumura T, Usui E. 2014. Concave-monotone treatment response and monotone treatment selection: with an application to the returns to schooling. Quant. Econ. 5:1175–94
    [Google Scholar]
  122. Pakes A. 2010. Alternative models for moment inequalities. Econometrica 78:61783–822
    [Google Scholar]
  123. Pakes A, Porter J, Ho K, Ishii J. 2015. Moment inequalities and their application. Econometrica 83:1315–34
    [Google Scholar]
  124. Pakes A, Porter JR, Shepard M, Calder-Wang S. 2021. Unobserved heterogeneity, state dependence, and health plan choices NBER Work. Pap. 29025
    [Google Scholar]
  125. de Paula Á. 2013. Econometric analysis of games with multiple equilibria. Annu. Rev. Econ. 5:107–31
    [Google Scholar]
  126. de Paula Á 2017. Econometrics of network models. Advances in Economics and Econometrics, Vol. 1 B Honoré, A Pakes, M Piazzesi, L Samuelson 268–323. Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  127. de Paula Á. 2020a. Econometric models of network formation. Annu. Rev. Econ. 12:775–99
    [Google Scholar]
  128. de Paula Á 2020b. Strategic network formation. The Econometric Analysis of Network Data B Graham, Á de Paula 41–61. Amsterdam: Elsevier
    [Google Scholar]
  129. de Paula Á, Richards-Shubik S, Tamer E. 2018. Identifying preferences in networks with bounded degree. Econometrica 86:1263–88
    [Google Scholar]
  130. de Paula Á, Tang X. 2012. Inference of signs of interaction effects in simultaneous games with incomplete information. Econometrica 80:1143–72
    [Google Scholar]
  131. Poirier DJ. 1998. Revising beliefs in nonidentified models. Econometr. Theory 14:4483–509
    [Google Scholar]
  132. Ponomareva M, Tamer E. 2011. Misspecification in moment inequality models: back to moment equalities?. Econometr. J. 14:2186–203
    [Google Scholar]
  133. Romano JP, Shaikh AM. 2008. Inference for identifiable parameters in partially identified econometric models. J. Stat. Plan. Inference 138:92786–807
    [Google Scholar]
  134. Romano JP, Shaikh AM. 2010. Inference for the identified set in partially identified econometric models. Econometrica 78:1169–211
    [Google Scholar]
  135. Rubio-Ramirez JF, Waggoner DF, Zha T. 2010. Structural vector autoregressions: theory of identification and algorithms for inference. Rev. Econ. Stud. 77:2665–96
    [Google Scholar]
  136. Samuelson PA. 1938. A note on the pure theory of consumer's behaviour. Economica 5:1761–71
    [Google Scholar]
  137. Sheng S. 2020. A structural econometric analysis of network formation games through subnetworks. Econometrica 88:51829–58
    [Google Scholar]
  138. Stoye J. 2010. Partial identification of spread parameters. Quant. Econ. 1:2323–57
    [Google Scholar]
  139. Syrgkanis V, Tamer E, Ziani J. 2021. Inference on auctions with weak assumptions on information Work. Pap., Harvard Univ. Cambridge, MA:
    [Google Scholar]
  140. Tamer E. 2003. Incomplete simultaneous discrete response model with multiple equilibria. Rev. Econ. Stud. 70:1147–65
    [Google Scholar]
  141. Tamer E. 2010. Partial identification in econometrics. Annu. Rev. Econ. 2:167–95
    [Google Scholar]
  142. Torgovitsky A. 2019. Nonparametric inference on state dependence in unemployment. Econometrica 87:51475–505
    [Google Scholar]
  143. Twinam T. 2017. Complementarity and identification. Econometr. Theory 33:51154–85
    [Google Scholar]
  144. Uhlig H. 2005. What are the effects of monetary policy on output? Results from an agnostic identification procedure. J. Monet. Econ. 52:2381–419
    [Google Scholar]
  145. Uhlig H 2017. Shocks, sign restrictions, and identification. Advances in Economics and Econometrics, Vol. 2, eds. B Honoré, A Pakes, M Piazzesi, L Samuelson 95–127. Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  146. Wan Y, Xu H. 2014. Semiparametric identification of binary decision games of incomplete information with correlated private signals. J. Econometr. 182:2235–46
    [Google Scholar]
  147. Xiao R. 2018. Identification and estimation of incomplete information games with multiple equilibria. J. Econometr. 203:2328–43
    [Google Scholar]
  148. Xu H. 2014. Estimation of discrete games with correlated types. Econometr. J. 17:3241–70
    [Google Scholar]
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