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Efficiency Bounds for Missing Data Models With Semiparametric Restrictions

Citations

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Cited by:

  1. Saraswata Chaudhuriy & David T. Frazierz & Eric Renault, 2016. "Indirect Inference with Endogenously Missing Exogenous Variables," CIRANO Working Papers 2016s-15, CIRANO.
  2. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
  3. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
  4. Bryan S. Graham & Guido W. Imbens & Geert Ridder, 2020. "Identification and Efficiency Bounds for the Average Match Function Under Conditionally Exogenous Matching," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 303-316, April.
  5. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
  6. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
  7. Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Sep 2021.
  8. Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.
  9. Graham, Bryan S. & Pinto, Cristine Campos de Xavier, 2022. "Semiparametrically efficient estimation of the average linear regression function," Journal of Econometrics, Elsevier, vol. 226(1), pages 115-138.
  10. Ying-Ying Lee, 2015. "Efficient propensity score regression estimators of multi-valued treatment effects for the treated," Economics Series Working Papers 738, University of Oxford, Department of Economics.
  11. Michael Zimmert, 2018. "Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding," Papers 1809.01643, arXiv.org, revised Aug 2020.
  12. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 288-301, April.
  13. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).
  14. Bryan S. Graham, 2019. "Network Data," NBER Working Papers 26577, National Bureau of Economic Research, Inc.
  15. Karun Adusumilli & Taisuke Otsu & Chen Qiu, 2020. "Reweighted nonparametric likelihood inference for linear functionals," STICERD - Econometrics Paper Series 614, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  16. Bryan S. Graham & Guido Imbens & Geert Ridder, 2016. "Identification and efficiency bounds for the average match function under conditionally exogenous matching," CeMMAP working papers 10/16, Institute for Fiscal Studies.
  17. Thomas MaCurdy & Xiaohong Chen & Han Hong, 2011. "Flexible Estimation of Treatment Effect Parameters," American Economic Review, American Economic Association, vol. 101(3), pages 544-551, May.
  18. Adusumilli, Karun & Otsu, Taisuke & Qiu, Chen, 2023. "Reweighted nonparametric likelihood inference for linear functionals," LSE Research Online Documents on Economics 120198, London School of Economics and Political Science, LSE Library.
  19. Karun Adusumilli & Taisuke Otsu, 2018. "Likelihood ratio inference for missing data models," STICERD - Econometrics Paper Series 599, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  20. M. Hristache & V. Patilea, 2017. "Conditional moment models with data missing at random," Biometrika, Biometrika Trust, vol. 104(3), pages 735-742.
  21. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  22. Hristache, Marian & Patilea, Valentin, 2021. "Equivalent models for observables under the assumption of missing at random," Econometrics and Statistics, Elsevier, vol. 20(C), pages 153-165.
  23. Bang, Minji & Gao, Wayne Yuan & Postlewaite, Andrew & Sieg, Holger, 2023. "Using monotonicity restrictions to identify models with partially latent covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 892-921.
  24. Lee, Ying-Ying, 2018. "Efficient propensity score regression estimators of multivalued treatment effects for the treated," Journal of Econometrics, Elsevier, vol. 204(2), pages 207-222.
  25. Graham, Bryan S., 2020. "Network data," Handbook of Econometrics,, Elsevier.
  26. Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2025. "Moment restrictions for nonlinear panel data models with feedback," CeMMAP working papers 12/25, Institute for Fiscal Studies.
  27. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  28. Victor Chernozhukov & Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models," CeMMAP working papers CWP41/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  29. Heng Chen & Marie-Hélène Felt & Christopher Henry, 2018. "2017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation," Technical Reports 114, Bank of Canada.
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