Panel Data Quantile Regression for Treatment Effect Models

References

• Abadie, A. (2002), “Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models,” Journal of the American Statistical Association, 97, 284–292. DOI: 10.1198/016214502753479419.
   Web of Science ®Google Scholar
• Abadie, A., Angrist, J., and Imbens, G. (2002), “Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings,” Econometrica, 70, 91–117. DOI: 10.1111/1468-0262.00270.
   Web of Science ®Google Scholar
• Angrist, J., Chernozhukov, V., and Fernández-Val, I. (2006), “Quantile Regression Under Misspecification, with an Application to the US Wage Structure,” Econometrica, 74, 539–563. DOI: 10.1111/j.1468-0262.2006.00671.x.
   Web of Science ®Google Scholar
• Athey, S., and Imbens, G. W. (2006), “Identification and Inference in Nonlinear Difference-in-Differences Models,” Econometrica, 74, 431–497. DOI: 10.1111/j.1468-0262.2006.00668.x.
   Web of Science ®Google Scholar
• Bitler, M. P., Gelbach, J. B., and Hoynes, H. W. (2006), “What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments,” American Economic Review, 96, 988–1012. DOI: 10.1257/aer.96.4.988.
   Web of Science ®Google Scholar
• Brown, D. J., and Wegkamp, M. H. (2002), “Weighted Minimum Mean–Square Distance from Independence Estimation,” Econometrica, 70, 2035–2051. DOI: 10.1111/1468-0262.00362.
   Web of Science ®Google Scholar
• Cai, J. (2016), “The Impact of Insurance Provision on Household Production and Financial Decisions,” American Economic Journal: Economic Policy, 8, 44–88. DOI: 10.1257/pol.20130371.
   Web of Science ®Google Scholar
• Callaway, B., and Li, T. (2019), “Quantile Treatment Effects in Difference in Differences Models with Panel Data,” Quantitative Economics, 10, 1579–1618. DOI: 10.3982/QE935.
   Web of Science ®Google Scholar
• Chamberlain, G. (1982), “Multivariate Regression Models for Panel Data,” Journal of Econometrics, 18, 5–46. DOI: 10.1016/0304-4076(82)90094-X.
   Web of Science ®Google Scholar
• Chen, X., Linton, O., and Van Keilegom, I. (2003), “Estimation of Semiparametric Models When the Criterion Function is not Smooth,” Econometrica, 71, 1591–1608. DOI: 10.1111/1468-0262.00461.
   Web of Science ®Google Scholar
• Chernozhukov, V., Fernández-Val, I., Hahn, J., and Newey, W. (2013), “Average and Quantile Effects in Nonseparable Panel Models,” Econometrica, 81, 535–580.
   Web of Science ®Google Scholar
• Chernozhukov, V., Fernandez-Val, I., Hoderlein, S., Holzmann, H., and Newey, W. (2015), “Nonparametric Identification in Panels Using Quantiles,” Journal of Econometrics, 188, 378–392. DOI: 10.1016/j.jeconom.2015.03.006.
   Web of Science ®Google Scholar
• Chernozhukov, V., and Hansen, C. (2004), “The Effects of 401 (k) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis,” The Review of Economics and Statistics, 86, 735–751. DOI: 10.1162/0034653041811734.
   Web of Science ®Google Scholar
• Chernozhukov, V., and Hansen, C. (2005), “An IV Model of Quantile Treatment Effects,” Econometrica, 73, 245–261.
   Web of Science ®Google Scholar
• Chernozhukov, V., and Hansen, C. (2006), “Instrumental Quantile Regression Inference for Structural and Treatment Effect Models,” Journal of Econometrics, 132, 491–525.
   Web of Science ®Google Scholar
• D’Haultfoeuille, X., and Février, P. (2015), “Identification of Nonseparable Triangular Models with Discrete Instruments,” Econometrica, 83, 1199–1210. DOI: 10.3982/ECTA10038.
   Web of Science ®Google Scholar
• D’Haultfoeuille, X., Hoderlein, S., and Sasaki, Y. (2013), “Nonlinear Difference-in-Differences in Repeated Cross Sections with Continuous Treatments,” Technical report, Boston College Department of Economics.
   Google Scholar
• Evdokimov, K. (2010), “Identification and Estimation of a Nonparametric Panel Data Model with Unobserved Heterogeneity,” Department of Economics, Princeton University.
   Google Scholar
• Feng, Q., Vuong, Q., and Xu, H. (2020), “Estimation of Heterogeneous Individual Treatment Effects with Endogenous Treatments,” Journal of the American Statistical Association, 115, 231–240. DOI: 10.1080/01621459.2018.1543121.
   Web of Science ®Google Scholar
• Firpo, S. (2007), “Efficient Semiparametric Estimation of Quantile Treatment Effects,” Econometrica, 75, 259–276. DOI: 10.1111/j.1468-0262.2007.00738.x.
   Web of Science ®Google Scholar
• Frölich, M., and Melly, B. (2013), “Unconditional Quantile Treatment Effects Under Endogeneity,” Journal of Business & Economic Statistics, 31, 346–357.
   Web of Science ®Google Scholar
• Graham, B. S., and Powell, J. L. (2012), “Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models,” Econometrica, 80, 2105–2152.
   Web of Science ®Google Scholar
• Havnes, T., and Mogstad, M. (2015), “Is Universal Child Care Leveling the Playing Field?” Journal of Public Economics, 127, 100–114. DOI: 10.1016/j.jpubeco.2014.04.007.
   Web of Science ®Google Scholar
• Hoderlein, S., and White, H. (2012), “Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects,” Journal of Econometrics, 168, 300–314. DOI: 10.1016/j.jeconom.2012.01.033.
   Web of Science ®Google Scholar
• Huang, F., and Lee, M.-J. (2010), “Dynamic Treatment Effect Analysis of TV Effects on Child Cognitive Development,” Journal of Applied Econometrics, 25, 392–419. DOI: 10.1002/jae.1165.
   Web of Science ®Google Scholar
• Ishihara, T. (2020), “Identification and Estimation of Time-Varying Nonseparable Panel Data Models Without Stayers,” Journal of Econometrics, 215, 184–208. DOI: 10.1016/j.jeconom.2019.08.008.
   Web of Science ®Google Scholar
• Koenker, R., and Bassett, G. (1978), “Regression Quantiles,” Econometrica, 46, 33–50. DOI: 10.2307/1913643.
   Web of Science ®Google Scholar
• Kottelenberg, M. J., and Lehrer, S. F. (2017), “Targeted or Universal Coverage? Assessing Heterogeneity in the Effects of Universal Child Care,” Journal of Labor Economics, 35, 609–653. DOI: 10.1086/690652.
   Web of Science ®Google Scholar
• Martincus, C. V., and Carballo, J. (2010), “Beyond the Average Effects: The Distributional Impacts of Export Promotion Programs in Developing Countries,” Journal of Development Economics, 92, 201–214. DOI: 10.1016/j.jdeveco.2009.02.007.
   Web of Science ®Google Scholar
• Matzkin, R. L. (2003), “Nonparametric Estimation of Nonadditive Random Functions,” Econometrica, 71, 1339–1375. DOI: 10.1111/1468-0262.00452.
   Web of Science ®Google Scholar
• Melly, B. M., and Santangelo, G. (2015), “The Changes-in-Changes Model with Covariates,” Universität Bern, Bern.
   Google Scholar
• Sawada, M. (2019), “Noncompliance in Randomized Control Trials Without Exclusion Restrictions,” arXiv preprint arXiv:1910.03204.
   Google Scholar
• Stinchcombe, M. B., and White, H. (1998), “Consistent Specification Testing with Nuisance Parameters Present Only Under the Alternative,” Econometric Theory, 14, 295–325. DOI: 10.1017/S0266466698143013.
   Web of Science ®Google Scholar
• Torgovitsky, A. (2015), “Identification of Nonseparable Models Using Instruments with Small Support,” Econometrica, 83, 1185–1197. DOI: 10.3982/ECTA9984.
   Web of Science ®Google Scholar
• Torgovitsky, A. (2017), “Minimum Distance from Independence Estimation of Nonseparable Instrumental Variables Models,” Journal of Econometrics, 199, 35–48.
   Web of Science ®Google Scholar
• van der Vaart, A., and Wellner, J. (1996), Weak Convergence and Empirical Processes: With Applications to Statistics, New York: Springer.
   Google Scholar