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Yuya Sasaki

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Yuya Sasaki, 2025. "GMM and M Estimation under Network Dependence," Papers 2503.00290, arXiv.org, revised Mar 2026.

    Cited by:

    1. Lin Chen & Yuya Sasaki, 2025. "Heterogeneous Effects of Endogenous Treatments with Interference and Spillovers in a Large Network," Papers 2512.14515, arXiv.org.

  2. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.

    Cited by:

    1. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2025. "Panel Data Estimation and Inference: Homogeneity versus Heterogeneity," Papers 2502.03019, arXiv.org, revised Jul 2025.

  3. Harold D Chiang & Yuya Sasaki, 2023. "On Using The Two-Way Cluster-Robust Standard Errors," Papers 2301.13775, arXiv.org.

    Cited by:

    1. Ruonan Xu & Luther Yap, 2024. "Clustering with Potential Multidimensionality: Inference and Practice," Papers 2411.13372, arXiv.org.
    2. Chiang, Harold D. & Matsushita, Yukitoshi & Otsu, Taisuke, 2025. "Multiway empirical likelihood," Journal of Econometrics, Elsevier, vol. 249(PA).
    3. Laurent Davezies & Xavier D'Haultf{oe}uille & Yannick Guyonvarch, 2025. "Analytic inference with two-way clustering," Papers 2506.20749, arXiv.org, revised Feb 2026.
    4. Yap, Luther, 2025. "Asymptotic theory for two-way clustering," Journal of Econometrics, Elsevier, vol. 249(PB).

  4. Harold D. Chiang & Yuya Sasaki & Yulong Wang, 2023. "Genuinely Robust Inference for Clustered Data," Papers 2308.10138, arXiv.org, revised Oct 2025.

    Cited by:

    1. Bastien Buchwalter & Francis X. Diebold & Kamil Yilmaz, 2025. "Clustered Network Connectedness:A New Measurement Frameworkwith Application to Global Equity Markets," PIER Working Paper Archive 25-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  5. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.

    Cited by:

    1. Ruonan Xu, 2023. "Difference-in-Differences with Interference," Papers 2306.12003, arXiv.org, revised Jan 2025.
    2. Langen, Henrika & Laine, Liisa, 2025. "Heterogeneous Effects of a Teacher Strike on Education and Labor Market Outcomes," IZA Discussion Papers 17937, IZA Network @ LISER.
    3. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    4. García Vázquez, C.A. & Cotfas, D.T. & González Santos, A.I. & Cotfas, P.A. & León Ávila, B.Y., 2024. "Reduction of electricity consumption in an AHU using mathematical modelling for controller tuning," Energy, Elsevier, vol. 293(C).
    5. Andrew Baker & Brantly Callaway & Scott Cunningham & Andrew Goodman-Bacon & Pedro H. C. Sant'Anna, 2025. "Difference-in-Differences Designs: A Practitioner's Guide," Papers 2503.13323, arXiv.org, revised Jun 2025.
    6. Jacob Dorn, 2025. "How Much Weak Overlap Can Doubly Robust T-Statistics Handle?," Papers 2504.13273, arXiv.org, revised Apr 2025.

  6. Ji Hyung Lee & Yuya Sasaki & Alexis Akira Toda & Yulong Wang, 2022. "Capital and Labor Income Pareto Exponents in the United States, 1916-2019," Papers 2206.04257, arXiv.org.

    Cited by:

    1. Harmenberg, Karl, 2024. "A simple theory of Pareto-distributed earnings," Economics Letters, Elsevier, vol. 234(C).
    2. Lee, Ji Hyung & Sasaki, Yuya & Toda, Alexis Akira & Wang, Yulong, 2024. "Tuning parameter-free nonparametric density estimation from tabulated summary data," Journal of Econometrics, Elsevier, vol. 238(1).

  7. Yuya Sasaki & Yulong Wang, 2022. "Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method," Papers 2210.16991, arXiv.org, revised Jan 2025.

    Cited by:

    1. Cruces, Guillermo & Tortarolo, Dario & Vazquez-Bare, Gonzalo, 2025. "Design of Partial Population Experiments with an Application to Spillovers in Tax Compliance," IZA Discussion Papers 17692, IZA Network @ LISER.
    2. Harold D. Chiang & Yuya Sasaki & Yulong Wang, 2023. "Genuinely Robust Inference for Clustered Data," Papers 2308.10138, arXiv.org, revised Oct 2025.
    3. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2024. "Inference in the presence of unknown rates," LSE Research Online Documents on Economics 126066, London School of Economics and Political Science, LSE Library.

  8. Yuya Sasaki & Yulong Wang, 2022. "Extreme Changes in Changes," Papers 2211.14870, arXiv.org, revised May 2023.

    Cited by:

    1. Ghanem, Dalia & Hirshleifer, Sarojini & Kédagni, Désiré & Ortiz-Becerra, Karen, 2024. "Correcting attrition bias using changes-in-changes," Journal of Econometrics, Elsevier, vol. 241(2).
    2. Louis Gleyo, 2025. "Quotas for scholarship recipients: an efficient race-neutral alternative to affirmative action?," Papers 2507.17191, arXiv.org, revised Aug 2025.

  9. Harold D Chiang & Bruce E Hansen & Yuya Sasaki, 2022. "Standard errors for two-way clustering with serially correlated time effects," Papers 2201.11304, arXiv.org, revised Dec 2023.

    Cited by:

    1. Kaicheng Chen & Timothy J. Vogelsang, 2023. "Fixed-b Asymptotics for Panel Models with Two-Way Clustering," Papers 2309.08707, arXiv.org, revised Aug 2024.
    2. Jorge A. Arroyo, 2025. "Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico," Papers 2511.19469, arXiv.org, revised Nov 2025.

  10. Xavier D'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2021. "Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments," Papers 2104.14458, arXiv.org, revised May 2022.

    Cited by:

    1. Onil Boussim, 2024. "Changes-In-Changes For Discrete Treatment," Papers 2411.01617, arXiv.org.
    2. Xin Liu, 2024. "A quantile-based nonadditive fixed effects model," Papers 2405.03826, arXiv.org, revised Dec 2025.
    3. Le, Minh-Phuong & Chauvet, Lisa & Marouani, Mohamed Ali, 2024. "The Great Lockdown and the Small Business: Impact, Channels and Adaptation to the Covid Pandemic," World Development, Elsevier, vol. 182(C).
    4. Francesca Micocci & Armando Rungi & Giovanni Cerulli, 2025. "Learning by exporting with a dose-response function," Papers 2505.03328, arXiv.org, revised May 2025.
    5. Baraldi, Anna Laura & Cantabene, Claudia & De Iudicibus, Alessandro, 2024. "Fighting crime to improve recycling: Evaluating an anti-mafia policy on source separation of waste," Ecological Economics, Elsevier, vol. 224(C).
    6. Yan Zhao & Ehsan Elahi & Zainab Khalid & Xuegang Sun & Fang Sun, 2023. "Environmental, Social and Governance Performance: Analysis of CEO Power and Corporate Risk," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    7. Carolina Caetano & Gregorio Caetano & Hao Fe & Eric R. Nielsen, 2021. "A Dummy Test of Identification in Models with Bunching," Finance and Economics Discussion Series 2021-068, Board of Governors of the Federal Reserve System (U.S.).
    8. Yin, Zhujia & Deng, Rantian & Xia, Jiejin & Zhao, Lili, 2024. "Climate risk and corporate ESG performance: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    9. Cl'ement de Chaisemartin & Xavier D'Haultf{oe}uille & F'elix Pasquier & Doulo Sow & Gonzalo Vazquez-Bare, 2022. "Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period," Papers 2201.06898, arXiv.org, revised Aug 2025.
    10. Shun Yiu & Rob Seamans & Manav Raj & Ted Liu, 2024. "Strategic Responses to Technological Change: Evidence from Online Labor Markets," Papers 2403.15262, arXiv.org, revised May 2025.
    11. Lucas Z. Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org, revised Dec 2025.

  11. Yuya Sasaki & Takuya Ura, 2021. "Slow Movers in Panel Data," Papers 2110.12041, arXiv.org.

    Cited by:

    1. M. Hashem Pesaran & Liying Yang, 2023. "Trimmed Mean Group Estimation of Average Treatment Effects in Ultra Short T Panels under Correlated Heterogeneity," CESifo Working Paper Series 10725, CESifo.
    2. Cl'ement de Chaisemartin & Diego Ciccia & Xavier D'Haultf{oe}uille & Felix Knau, 2024. "Difference-in-Differences Estimators When No Unit Remains Untreated," Papers 2405.04465, arXiv.org, revised Aug 2025.
    3. Laage, Louise, 2024. "A Correlated Random Coefficient panel model with time-varying endogeneity," Journal of Econometrics, Elsevier, vol. 242(2).
    4. Muris, Chris & Wacker, Konstantin M., 2025. "Estimating interaction effects with panel data," GLO Discussion Paper Series 1583, Global Labor Organization (GLO).

  12. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.

    Cited by:

    1. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    2. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    3. Mauricio Villamizar‐Villegas & Freddy A. Pinzon‐Puerto & Maria Alejandra Ruiz‐Sanchez, 2022. "A comprehensive history of regression discontinuity designs: An empirical survey of the last 60 years," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1130-1178, September.

  13. Yuya Sasaki & Takuya Ura, 2020. "Welfare Analysis via Marginal Treatment Effects," Papers 2012.07624, arXiv.org.

    Cited by:

    1. Yan Liu, 2022. "Policy Learning under Endogeneity Using Instrumental Variables," Papers 2206.09883, arXiv.org, revised Jan 2026.
    2. Toru Kitagawa & Hugo Lopez & Jeff Rowley, 2022. "Stochastic Treatment Choice with Empirical Welfare Updating," Papers 2211.01537, arXiv.org, revised Feb 2023.
    3. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.

  14. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2020. "Inference for high-dimensional exchangeable arrays," Papers 2009.05150, arXiv.org, revised Jul 2021.

    Cited by:

    1. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," Papers 2108.04852, arXiv.org, revised Aug 2024.
    2. Susan Athey & Guido Imbens, 2025. "Identification of Average Treatment Effects in Nonparametric Panel Models," Papers 2503.19873, arXiv.org.
    3. Chiang, Harold D. & Matsushita, Yukitoshi & Otsu, Taisuke, 2025. "Multiway empirical likelihood," Journal of Econometrics, Elsevier, vol. 249(PA).
    4. Harold D. Chiang & Jiatong Li & Yuya Sasaki, 2021. "Algorithmic subsampling under multiway clustering," Papers 2103.00557, arXiv.org, revised Oct 2022.
    5. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    6. Harold D Chiang & Bruce E Hansen & Yuya Sasaki, 2022. "Standard errors for two-way clustering with serially correlated time effects," Papers 2201.11304, arXiv.org, revised Dec 2023.
    7. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    8. Lu, Zhentong & Shi, Xiaoxia & Tao, Jing, 2023. "Semi-nonparametric estimation of random coefficients logit model for aggregate demand," Journal of Econometrics, Elsevier, vol. 235(2), pages 2245-2265.
    9. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2024. "Jackknife Inference with Two-Way Clustering," Working Paper 1516, Economics Department, Queen's University.
    10. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," STICERD - Econometrics Paper Series 617, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

  15. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.

    Cited by:

    1. Jikai Jin & Vasilis Syrgkanis, 2025. "Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation," Papers 2512.17341, arXiv.org, revised Jan 2026.
    2. Alejo, Javier & Galvao, Antonio F. & Martinez-Iriarte, Julian & Montes-Rojas, Gabriel, 2025. "Unconditional quantile partial effects via conditional quantile regression," Journal of Econometrics, Elsevier, vol. 249(PA).
    3. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    4. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    5. Martinez-Iriarte, Julian & Sun, Yixiao, 2024. "Identification and estimation of unconditional policy effects of an endogenous binary treatment: An unconditional MTE approach," Journal of Econometrics, Elsevier, vol. 244(1).
    6. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
    7. Javier Alejo & Antonio Galvao & Julián Martínez-Iriarte & Gabriel Montes-Rojas, 2025. "Generalized Recentered Influence Function Regressions," Econometrics, MDPI, vol. 13(2), pages 1-14, April.

  16. Taisuke Otsu & Martin Pesendorfer & Yuya Sasaki & Yuya Takahashi, 2020. "Estimation of (static or dynamic) games under equilibrium multiplicity," STICERD - Econometrics Paper Series 611, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    Cited by:

    1. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).

  17. Yuya Sasaki & Yulong Wang, 2019. "Fixed-k Inference for Conditional Extremal Quantiles," Papers 1909.00294, arXiv.org, revised Jul 2020.

    Cited by:

    1. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    2. Nicolau, João & Rodrigues, Paulo M.M. & Stoykov, Marian Z., 2023. "Tail index estimation in the presence of covariates: Stock returns’ tail risk dynamics," Journal of Econometrics, Elsevier, vol. 235(2), pages 2266-2284.
    3. Yuya Sasaki & Yulong Wang, 2022. "Extreme Changes in Changes," Papers 2211.14870, arXiv.org, revised May 2023.

  18. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.

    Cited by:

    1. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    2. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    3. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
    4. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2022. "Cluster-Robust Inference: A Guide to Empirical Practice," Papers 2205.03285, arXiv.org.
    5. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    6. Kangqi Jiang & Lulu Chen & Jiayun Li & Xinyi Du, 2025. "The risk effects of corporate digitalization: exacerbate or mitigate?," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-19, December.
    7. Harold D. Chiang & Jiatong Li & Yuya Sasaki, 2021. "Algorithmic subsampling under multiway clustering," Papers 2103.00557, arXiv.org, revised Oct 2022.
    8. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
    9. Wu, Liangpeng & Tang, Yujing & Meng, Lei & Zhu, Qingyuan & Zhou, Dequn, 2025. "Navigating ESG rating divergence: Implications for labor investment efficiency and firm adaptation strategy," Global Finance Journal, Elsevier, vol. 67(C).
    10. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python," Papers 2104.03220, arXiv.org, revised Dec 2021.
    11. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    12. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    13. Aslan Bakirov & Francesco Del Prato & Paolo Zacchia, 2026. "TWICE: Tree-based Wage Inference with Clustering and Estimation," Papers 2601.00776, arXiv.org.
    14. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2024. "Jackknife Inference with Two-Way Clustering," Working Paper 1516, Economics Department, Queen's University.
    15. Mo, Shu & Liu, Xinghua, 2025. "Strengthening the resilience of urban energy systems amid renewable energy transition: A new method based on double machine learning," Energy Policy, Elsevier, vol. 206(C).
    16. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2020. "Inference for high-dimensional exchangeable arrays," Papers 2009.05150, arXiv.org, revised Jul 2021.
    17. Bryan S. Graham & Fengshi Niu & James L. Powell, 2020. "Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression," Papers 2012.08444, arXiv.org, revised Mar 2021.

  19. Harold D. Chiang & Joel Rodrigue & Yuya Sasaki, 2019. "Post-Selection Inference in Three-Dimensional Panel Data," Papers 1904.00211, arXiv.org, revised Apr 2019.

    Cited by:

    1. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    2. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    3. Jin, Sainan & Lu, Xun & Su, Liangjun, 2025. "Three-dimensional heterogeneous panel data models with multi-level interactive fixed effects," Journal of Econometrics, Elsevier, vol. 249(PB).

  20. Harold D. Chiang & Yuya Sasaki, 2019. "Lasso under Multi-way Clustering: Estimation and Post-selection Inference," Papers 1905.02107, arXiv.org, revised Aug 2019.

    Cited by:

    1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    2. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.
    3. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2020. "Inference for high-dimensional exchangeable arrays," Papers 2009.05150, arXiv.org, revised Jul 2021.

  21. Kengo Kato & Yuya Sasaki & Takuya Ura, 2018. "Inference based on Kotlarski's Identity," Papers 1808.09375, arXiv.org, revised Sep 2019.

    Cited by:

    1. Adusumilli, Karun & Kurisu, Daisies & Otsu, Taisuke & Whang, Yoon-Jae, 2020. "Inference on distribution functions under measurement error," LSE Research Online Documents on Economics 102692, London School of Economics and Political Science, LSE Library.
    2. Li, Siran & Zheng, Xunjie, 2020. "A generalization of Lemma 1 in Kotlarski (1967)," Statistics & Probability Letters, Elsevier, vol. 165(C).
    3. William Morrison & Dmitry Taubinsky, 2019. "Rules of Thumb and Attention Elasticities: Evidence from Under- and Overreaction to Taxes," NBER Working Papers 26180, National Bureau of Economic Research, Inc.

  22. Tong Li & Yuya Sasaki, 2017. "Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function," Papers 1711.10031, arXiv.org.

    Cited by:

    1. Yu Hao & Hiroyuki Kasahara, 2025. "Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity," Papers 2506.09666, arXiv.org.
    2. Victor H. Aguiar & Nail Kashaev & Roy Allen, 2022. "Prices, Profits, Proxies, and Production," University of Western Ontario, Departmental Research Report Series 20226, University of Western Ontario, Department of Economics.
    3. Hiroyuki Kasahara & Paul Schrimpf & Michio Suzuki, 2023. "Identification and Estimation of Production Function with Unobserved Heterogeneity," Papers 2305.12067, arXiv.org.
    4. Ming Li, 2021. "Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model," Papers 2110.00982, arXiv.org, revised Feb 2026.
    5. Yu Hao & Hiroyuki Kasahara, 2022. "Testing the Number of Components in Finite Mixture Normal Regression Model with Panel Data," Papers 2210.02824, arXiv.org, revised Jun 2023.

  23. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers 40/13, Institute for Fiscal Studies.

    Cited by:

    1. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    2. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2014. "Fuzzy Changes-in-Changes," Working Papers 2014-18, Center for Research in Economics and Statistics.
    3. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    4. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    5. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers CWP31/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Carolina Caetano & Juan Carlos Escaniano, 2015. "Identifying Multiple Marginal Effects with a Single Binary Instrument or by Regression Discontinuity," CAEPR Working Papers 2015-009, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    7. Michela Maria Tincani, 2017. "Heterogeneous Peer Effects and Rank Concerns: Theory and Evidence," CESifo Working Paper Series 6331, CESifo.
    8. Ishihara, Takuya, 2020. "Identification and estimation of time-varying nonseparable panel data models without stayers," Journal of Econometrics, Elsevier, vol. 215(1), pages 184-208.
    9. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    10. Michela Tincani, 2017. "Heterogeneous Peer Effects and Rank Concerns: Theory and Evidence," Working Papers 2017-006, Human Capital and Economic Opportunity Working Group.
    11. Takuya Ishihara, 2020. "Panel Data Quantile Regression for Treatment Effect Models," Papers 2001.04324, arXiv.org, revised Nov 2021.
    12. de Chaisemartin, Clement & D'Haultfoeuille, Xavier, "undated". "Fuzzy Differences-in-Differences," Economic Research Papers 270218, University of Warwick - Department of Economics.

  24. Stefan Hoderlein & Yuya Sasaki, 2013. "Outcome conditioned treatment effects," CeMMAP working papers 39/13, Institute for Fiscal Studies.

    Cited by:

    1. Xavier D'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear Difference-in-Differences in Repeated Cross Sections with Continuous Treatments," Boston College Working Papers in Economics 839, Boston College Department of Economics.
    2. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    3. Stefan Hoderlein & Hajo Holzmann & Maximilian Kasy & Alexander Meister, 2015. "Erratum regarding “Instrumental variables with unrestricted heterogeneity and continuous treatment”," Boston College Working Papers in Economics 896, Boston College Department of Economics, revised 01 Feb 2016.
    4. D’Haultfœuille, Xavier & Hoderlein, Stefan & Sasaki, Yuya, 2024. "Testing and relaxing the exclusion restriction in the control function approach," Journal of Econometrics, Elsevier, vol. 240(2).
    5. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1614-1636.

Articles

  1. Liu, Nan & Liu, Yanbo & Sasaki, Yuya, 2026. "Estimation and inference for causal functions with multi-way clustered data," Journal of Econometrics, Elsevier, vol. 253(C).
    See citations under working paper version above.
  2. Li, Tong & Sasaki, Yuya, 2024. "Identification of heterogeneous elasticities in gross-output production functions," Journal of Econometrics, Elsevier, vol. 238(2).

    Cited by:

    1. Michele Battisti & Valentino Dardanoni & Stefano Demichelis, 2024. "Inter-firm Heterogeneity in Production," Papers 2411.15980, arXiv.org.

  3. Yuya Sasaki & Yulong Wang, 2024. "Extreme Changes in Changes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 812-824, April.
    See citations under working paper version above.
  4. D’Haultfœuille, Xavier & Hoderlein, Stefan & Sasaki, Yuya, 2024. "Testing and relaxing the exclusion restriction in the control function approach," Journal of Econometrics, Elsevier, vol. 240(2).

    Cited by:

    1. Latif Apaassongo Ibrahim & Aidoo Robert & Osei Mensah James, 2024. "City governance, urban livelihoods, and food security: insights from street food trade in Kumasi, Ghana," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 16(5), pages 1081-1098, October.
    2. Alessio Carrozzo Magli & Giovanni Righetto & Antonio Schiavone, 2025. "The Quiet Payoff: Mafia Electoral Support and Policy Inaction," CESifo Working Paper Series 12330, CESifo.
    3. Soonwoo Kwon & Jonathan Roth, 2024. "Testing Mechanisms," Papers 2404.11739, arXiv.org, revised Feb 2026.
    4. Le, Ha Chi & Bhattacharya, Mita & Smyth, Russell & Zhang, Xibin, 2025. "The impact of R&D on energy consumption: Evidence from nonlinear semi-parametric estimations for selected OECD countries," Energy Economics, Elsevier, vol. 149(C).
    5. Xuechao Qian, 2025. "A Nation of Two Tales: The Dual Intergenerational Economic Transmission in Rural and Urban China," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 44(3), pages 1-38, June.

  5. D’Haultfœuille, Xavier & Hoderlein, Stefan & Sasaki, Yuya, 2023. "Nonparametric difference-in-differences in repeated cross-sections with continuous treatments," Journal of Econometrics, Elsevier, vol. 234(2), pages 664-690.
    See citations under working paper version above.
  6. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2023. "Inference for High-Dimensional Exchangeable Arrays," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1595-1605, July.
    See citations under working paper version above.
  7. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.

    Cited by:

    1. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    2. Zhewen Pan & Yifan Zhang, 2024. "Locally robust semiparametric estimation of sample selection models without exclusion restrictions," Papers 2412.01208, arXiv.org.
    3. Martinez-Iriarte, Julian & Sun, Yixiao, 2024. "Identification and estimation of unconditional policy effects of an endogenous binary treatment: An unconditional MTE approach," Journal of Econometrics, Elsevier, vol. 244(1).

  8. Chiang, Harold D. & Rodrigue, Joel & Sasaki, Yuya, 2023. "Post-Selection Inference In Three-Dimensional Panel Data," Econometric Theory, Cambridge University Press, vol. 39(3), pages 623-658, June.
    See citations under working paper version above.
  9. Yuya Sasaki & Yulong Wang, 2023. "Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-N Asymptotic Normality of the GMM and M Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 339-348, April.

    Cited by:

    1. Jean-Jacques Forneron, 2023. "Occasionally Misspecified," Papers 2312.05342, arXiv.org.
    2. Karlsson, Martin & Wang, Yulong & Ziebarth, Nicolas R., 2024. "Getting the right tail right: Modeling tails of health expenditure distributions," Journal of Health Economics, Elsevier, vol. 97(C).
    3. Virginia Blanco-Mazagatos & M. Elena Romero-Merino & Marcos Santamaría-Mariscal & Juan Bautista Delgado-García, 2024. "One more piece of the family firm debt puzzle: the influence of socioemotional wealth dimensions," Small Business Economics, Springer, vol. 63(2), pages 831-849, August.

  10. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).

    Cited by:

    1. Hu, Yingyao & Xin, Yi, 2024. "Identification and estimation of dynamic structural models with unobserved choices," Journal of Econometrics, Elsevier, vol. 242(2).

  11. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
    See citations under working paper version above.
  12. Taisuke Otsu & Martin Pesendorfer & Yuya Sasaki & Yuya Takahashi, 2022. "Estimation Of (Static Or Dynamic) Games Under Equilibrium Multiplicity," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(3), pages 1165-1188, August.
    See citations under working paper version above.
  13. Sasaki, Yuya & Ura, Takuya, 2022. "Estimation And Inference For Moments Of Ratios With Robustness Against Large Trimming Bias," Econometric Theory, Cambridge University Press, vol. 38(1), pages 66-112, February.

    Cited by:

    1. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    2. Andrew Baker & Brantly Callaway & Scott Cunningham & Andrew Goodman-Bacon & Pedro H. C. Sant'Anna, 2025. "Difference-in-Differences Designs: A Practitioner's Guide," Papers 2503.13323, arXiv.org, revised Jun 2025.
    3. Soonwoo Kwon & Liyang Sun, 2025. "Estimating Treatment Effects Under Bounded Heterogeneity," Papers 2510.05454, arXiv.org.
    4. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2024. "Inference in the presence of unknown rates," LSE Research Online Documents on Economics 126066, London School of Economics and Political Science, LSE Library.
    5. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
    6. Stefan Tübbicke, 2023. "When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college," Empirical Economics, Springer, vol. 65(6), pages 2979-2999, December.
    7. Jacob Dorn, 2025. "How Much Weak Overlap Can Doubly Robust T-Statistics Handle?," Papers 2504.13273, arXiv.org, revised Apr 2025.

  14. Yuya Sasaki & Yulong Wang, 2022. "Fixed-k Inference for Conditional Extremal Quantiles," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 829-837, April.
    See citations under working paper version above.
  15. 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.
    See citations under working paper version above.
  16. Arie Beresteanu & Yuya Sasaki, 2021. "Quantile regression with interval data," Econometric Reviews, Taylor & Francis Journals, vol. 40(6), pages 562-583, July.

    Cited by:

    1. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.

  17. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.

    Cited by:

    1. Hao Dong & Yuya Sasaki, 2022. "Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving," Papers 2209.05914, arXiv.org.
    2. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
    3. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. JoonHwan Cho & Yao Luo & Ruli Xiao, 2024. "Deconvolution from two order statistics," Papers 2403.17777, arXiv.org.
    5. JoonHwan Cho & Yao Luo & Ruli Xiao, 2022. "Deconvolution from Two Order Statistics," Working Papers tecipa-739, University of Toronto, Department of Economics.
    6. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.
    7. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," LSE Research Online Documents on Economics 112676, London School of Economics and Political Science, LSE Library.

  18. Chen, Heng & Chiang, Harold D. & Sasaki, Yuya, 2020. "Quantile Treatment Effects In Regression Kink Designs," Econometric Theory, Cambridge University Press, vol. 36(6), pages 1167-1191, December.

    Cited by:

    1. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    2. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.

  19. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.

    Cited by:

    1. Ziran Ding & Jose Garcia‐Louzao & Valentin Jouvanceau, 2025. "The dynamics of product and labour market power: Evidence from Lithuania," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 33(1), pages 165-194, January.
    2. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Proxy variable estimation of productivity and efficiency," Southern Economic Journal, John Wiley & Sons, vol. 89(3), pages 885-923, January.
    3. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    4. Nilsson, Pia & Andersson, Hans & Heldt, Tobias, 2025. "The Role of Specialized Knowledge and ‘Know-How’ for Firm Total Factor Productivity: Evidence from Breeding and Racing in the Equine Industry," Working Paper Series 1522, Research Institute of Industrial Economics, revised 19 May 2025.
    5. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data methods for dynamic heterogeneous agent models," CeMMAP working papers 51/16, Institute for Fiscal Studies.
    6. Tong Li & Yuya Sasaki, 2017. "Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function," Papers 1711.10031, arXiv.org.
    7. Alvaro Aguirre & Matias Tapia & Lucciano Villacorta, 2021. "Production, Investment and Wealth Dynamics under Financial Frictions: An Empirical Investigation of the Selffinancing Channel," Working Papers Central Bank of Chile 904, Central Bank of Chile.
    8. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.
    9. Daniel A. Ackerberg, 2023. "Timing Assumptions and Efficiency: Empirical Evidence in a Production Function Context," Journal of Industrial Economics, Wiley Blackwell, vol. 71(3), pages 644-674, September.
    10. Li, Tong & Sasaki, Yuya, 2024. "Identification of heterogeneous elasticities in gross-output production functions," Journal of Econometrics, Elsevier, vol. 238(2).
    11. Dibyendu Maiti & Chiranjib Neogi, 2024. "Endogeneity-corrected stochastic frontier with market imperfections," Empirical Economics, Springer, vol. 67(3), pages 1149-1183, September.
    12. Chen Yeh & Claudia Macaluso & Brad Hershbein, 2022. "Monopsony in the US Labor Market," American Economic Review, American Economic Association, vol. 112(7), pages 2099-2138, July.
    13. Kim, Kyoo il & Petrin, Amil & Song, Suyong, 2016. "Estimating production functions with control functions when capital is measured with error," Journal of Econometrics, Elsevier, vol. 190(2), pages 267-279.
    14. Abito, Jose Miguel, 2019. "Estimating Production Functions with Fixed Effects," MPRA Paper 97825, University Library of Munich, Germany.
    15. Chen, Hanxue & Wang, Shuhong & Song, Malin, 2021. "Global Environmental Value Chain Embeddedness and Enterprise Production Efficiency Improvement," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 278-290.
    16. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
    17. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    18. Ainagul T. Mamyralieva & Aziza B. Karbekova & Gulchehra B. Abdyrahmanova, 2022. "Analysis of the economic sectors? sustainability of the Kyrgyz Republic," RIVISTA DI STUDI SULLA SOSTENIBILITA', FrancoAngeli Editore, vol. 0(2), pages 185-204.
    19. Emir Malikov & Jingfang Zhang & Shunan Zhao & Subal C. Kumbhakar, 2023. "Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity," Papers 2302.13430, arXiv.org.

  20. Yingyao Hu & Robert Moffitt & Yuya Sasaki, 2019. "Semiparametric estimation of the canonical permanent‐transitory model of earnings dynamics," Quantitative Economics, Econometric Society, vol. 10(4), pages 1495-1536, November.

    Cited by:

    1. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    2. Hao Dong & Yuya Sasaki, 2022. "Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving," Papers 2209.05914, arXiv.org.
    3. Ivan Fernandez-Val & Wayne Yuan Gao & Yuan Liao & Francis Vella, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," Papers 2202.04154, arXiv.org, revised Jul 2025.
    4. Silvia Sarpietro & Yuya Sasaki & Yulong Wang, 2022. "Non-Existent Moments of Earnings Growth," Papers 2203.08014, arXiv.org, revised Feb 2024.
    5. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    6. Costanza Naguib & Patrick Gagliardini, 2023. "A Semi-nonparametric Copula Model for Earnings Mobility," Diskussionsschriften dp2302, Universitaet Bern, Departement Volkswirtschaft.
    7. Costanza Naguib, 2022. "Financial Turmoil and Earnings Mobility," Diskussionsschriften dp2208, Universitaet Bern, Departement Volkswirtschaft.
    8. Costanza Naguib, 2022. "Did earnings mobility change after minimum wage introduction? Evidence from parametric and semi‐nonparametric methods in Germany," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1379-1402, November.
    9. Joseph Altonji & Disa Hynsjo & Ivan Vidangos, 2023. "Individual Earnings and Family Income: Dynamics and Distribution," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 49, pages 225-250, July.
    10. Kartik B. Athreya & Grey Gordon & John Bailey Jones & Urvi Neelakantan, 2021. "Incarceration, Earnings, and Race," Working Paper 21-11`, Federal Reserve Bank of Richmond.

  21. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.

    Cited by:

    1. Long Mark C. & Rooklyn Jordan, 2024. "Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-21, January.
    2. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    3. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    4. Carolina Caetano & Gregorio Caetano & Leonard Goff & Eric Nielsen, 2025. "Identification of Causal Effects with a Bunching Design," Papers 2507.05210, arXiv.org.
    5. Mehmet Caner, 2021. "Generalized Linear Models with Structured Sparsity Estimators," Papers 2104.14371, arXiv.org.
    6. Mehmet Caner & Kfir Eliaz, 2021. "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers 2101.01144, arXiv.org, revised Sep 2021.
    7. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    8. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.
    9. Haitian Xie, 2022. "Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs," Papers 2204.08168, arXiv.org, revised Jul 2022.

  22. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.

    Cited by:

    1. Hao Dong & Taisuke Otsu & Luke Taylor, 2021. "Bandwidth Selection for Nonparametric Regression with Errors-in-Variables," Departmental Working Papers 2104, Southern Methodist University, Department of Economics.
    2. Hao Dong & Yuya Sasaki, 2022. "Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving," Papers 2209.05914, arXiv.org.
    3. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Feb 2023.
    4. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
    5. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    6. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," Departmental Working Papers 2013, Southern Methodist University, Department of Economics.
    7. Katharina Proksch & Nicolai Bissantz & Hajo Holzmann, 2022. "Simultaneous inference for Berkson errors-in-variables regression under fixed design," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 773-800, August.
    8. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    9. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," LSE Research Online Documents on Economics 112676, London School of Economics and Political Science, LSE Library.
    10. Jun Ma & Vadim Marmer & Artyom Shneyerov, 2019. "Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's Estimator," Papers 1903.06401, arXiv.org.
    11. Hao Dong & Luke Taylor, 2020. "Nonparametric Significance Testing in Measurement Error Models," Departmental Working Papers 2003, Southern Methodist University, Department of Economics.

  23. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.

    Cited by:

    1. Long Mark C. & Rooklyn Jordan, 2024. "Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-21, January.
    2. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifie & Yuanyuan Wan, 2018. "Testing Identifying Assumptions In Fuzzy Regression Discontinuity Designs," Working Papers tecipa-623, University of Toronto, Department of Economics.
    3. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    4. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
    5. Yu-Chin Hsu & Ji-Liang Shiu & Yuanyuan Wan, 2023. "Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs," Working Papers tecipa-761, University of Toronto, Department of Economics.
    6. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    7. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    8. Zhixin Wang & Zhengyu Zhang, 2025. "A Unified Framework for Identification and Inference of Local Treatment Effects in Sharp Regression Kink Designs," Papers 2506.11663, arXiv.org, revised Oct 2025.
    9. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    10. Mauricio Villamizar‐Villegas & Freddy A. Pinzon‐Puerto & Maria Alejandra Ruiz‐Sanchez, 2022. "A comprehensive history of regression discontinuity designs: An empirical survey of the last 60 years," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1130-1178, September.
    11. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    12. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2024.

  24. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.

    Cited by:

    1. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    2. Hao Dong & Taisuke Otsu & Luke Taylor, 2021. "Bandwidth Selection for Nonparametric Regression with Errors-in-Variables," Departmental Working Papers 2104, Southern Methodist University, Department of Economics.
    3. Hao Dong & Yuya Sasaki, 2022. "Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving," Papers 2209.05914, arXiv.org.
    4. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
    5. Andrii Babii, 2016. "Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models," Papers 1611.03015, arXiv.org, revised Dec 2020.
    6. Hao Dong & Taisuke Otsu & Luke Taylor, 2019. "Average Derivative Estimation Under Measurement Error," Departmental Working Papers 1901, Southern Methodist University, Department of Economics.
    7. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    8. Ma, Jun & Marmer, Vadim & Shneyerov, Artyom, 2019. "Inference for first-price auctions with Guerre, Perrigne, and Vuong’s estimator," Journal of Econometrics, Elsevier, vol. 211(2), pages 507-538.
    9. Adusumilli, Karun & Kurisu, Daisies & Otsu, Taisuke & Whang, Yoon-Jae, 2020. "Inference on distribution functions under measurement error," LSE Research Online Documents on Economics 102692, London School of Economics and Political Science, LSE Library.
    10. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," Departmental Working Papers 2013, Southern Methodist University, Department of Economics.
    11. Katharina Proksch & Nicolai Bissantz & Hajo Holzmann, 2022. "Simultaneous inference for Berkson errors-in-variables regression under fixed design," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 773-800, August.
    12. Sid Kankanala, 2023. "Quasi-Bayes in Latent Variable Models," Papers 2311.06831, arXiv.org, revised Aug 2025.
    13. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    14. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," LSE Research Online Documents on Economics 112676, London School of Economics and Political Science, LSE Library.
    15. Kengo Kato & Yuya Sasaki & Takuya Ura, 2018. "Inference based on Kotlarski's Identity," Papers 1808.09375, arXiv.org, revised Sep 2019.
    16. Hao Dong & Luke Taylor, 2020. "Nonparametric Significance Testing in Measurement Error Models," Departmental Working Papers 2003, Southern Methodist University, Department of Economics.

  25. Botosaru, Irene & Sasaki, Yuya, 2018. "Nonparametric heteroskedasticity in persistent panel processes: An application to earnings dynamics," Journal of Econometrics, Elsevier, vol. 203(2), pages 283-296.

    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    3. Artūras Juodis & Vasilis Sarafidis, 2023. "New results on asymptotic properties of likelihood estimators with persistent data for small and large T," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 14(3), pages 435-461, December.
    4. Dan Ben-Moshe, 2023. "Identifying An Earning Process With Dependent Contemporaneous Income Shock," Working Papers 2301, Ben-Gurion University of the Negev, Department of Economics.
    5. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2015. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Working Papers wp2015_1506, CEMFI.
    6. Botosaru, Irene, 2023. "Time-varying unobserved heterogeneity in earnings shocks," Journal of Econometrics, Elsevier, vol. 235(2), pages 1378-1393.
    7. Irene Botosaru, 2017. "Identifying Distributions in a Panel Model with Heteroskedasticity: An Application to Earnings Volatility," Discussion Papers dp17-11, Department of Economics, Simon Fraser University.
    8. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    9. Silvia Sarpietro & Yuya Sasaki & Yulong Wang, 2022. "Non-Existent Moments of Earnings Growth," Papers 2203.08014, arXiv.org, revised Feb 2024.
    10. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    11. Costanza Naguib & Patrick Gagliardini, 2023. "A Semi-nonparametric Copula Model for Earnings Mobility," Diskussionsschriften dp2302, Universitaet Bern, Departement Volkswirtschaft.
    12. Grigory Franguridi & Hyungsik Roger Moon, 2025. "Kotlarski's lemma for dyadic models," Papers 2502.02734, arXiv.org, revised Mar 2026.

  26. Hu, Yingyao & Sasaki, Yuya, 2018. "Closed-Form Identification Of Dynamic Discrete Choice Models With Proxies For Unobserved State Variables," Econometric Theory, Cambridge University Press, vol. 34(1), pages 166-185, February.

    Cited by:

    1. Hu, Yingyao & Xin, Yi, 2024. "Identification and estimation of dynamic structural models with unobserved choices," Journal of Econometrics, Elsevier, vol. 242(2).
    2. Bunting, Jackson & Diegert, Paul & Maurel, Arnaud, 2025. "Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation," IZA Discussion Papers 17977, IZA Network @ LISER.
    3. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).
    4. Schneider, Ulrich, 2019. "Identification of Time Preferences in Dynamic Discrete Choice Models: Exploiting Choice Restrictions," MPRA Paper 102137, University Library of Munich, Germany, revised 29 Jul 2020.

  27. Kato, Ryutah & Sasaki, Yuya, 2017. "On Using Linear Quantile Regressions For Causal Inference," Econometric Theory, Cambridge University Press, vol. 33(3), pages 664-690, June.

    Cited by:

    1. Tymon S{l}oczy'nski, 2018. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," Papers 1810.01576, arXiv.org, revised May 2020.
    2. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, IZA Network @ LISER.
    3. Arie Beresteanu, 2020. "Quantile Regression with Interval Data," Working Paper 6899, Department of Economics, University of Pittsburgh.
    4. Carolina Caetano & Gregorio Caetano & Leonard Goff & Eric Nielsen, 2025. "Identification of Causal Effects with a Bunching Design," Papers 2507.05210, arXiv.org.
    5. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.

  28. Sasaki, Yuya & Xin, Yi, 2017. "Unequal spacing in dynamic panel data: Identification and estimation," Journal of Econometrics, Elsevier, vol. 196(2), pages 320-330.

    Cited by:

    1. Zhang, Xiaoge & Chen, Maolong, 2023. "Indirect inference approach to estimating dynamic panel data models with irregular spacing," Economics Letters, Elsevier, vol. 226(C).
    2. Montes-Rojas Gabriel & Sosa-Escudero Walter & Zincenko Federico, 2020. "Level-Based Estimation of Dynamic Panel Models," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-23, January.
    3. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Discussion Paper 2023-028, Tilburg University, Center for Economic Research.
    4. Khalaf, Lynda & Kichian, Maral & Saunders, Charles J. & Voia, Marcel, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Journal of Econometrics, Elsevier, vol. 220(2), pages 589-605.
    5. Fiona Steele & Emily Grundy, 2021. "Random effects dynamic panel models for unequally spaced multivariate categorical repeated measures: an application to child–parent exchanges of support," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 3-23, January.
    6. Steele, Fiona & Grundy, Emily, 2021. "Random effects dynamic panel models for unequally-spaced multivariate categorical repeated measures: an application to child-parent exchanges of support," LSE Research Online Documents on Economics 106255, London School of Economics and Political Science, LSE Library.
    7. Chen, Maolong & Myers, Robert J. & Hu, Chaoran, 2020. "Estimating dynamic binary choice models using irregularly spaced panel data," Economics Letters, Elsevier, vol. 192(C).

  29. Hu, Yingyao & Sasaki, Yuya, 2017. "Identification Of Paired Nonseparable Measurement Error Models," Econometric Theory, Cambridge University Press, vol. 33(4), pages 955-979, August.

    Cited by:

    1. Dongwoo Kim & Daniel Wilhelm, 2017. "Powerful t-Tests in the presence of nonclassical measurement error," CeMMAP working papers CWP57/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Emmanuel Guerre & Yao Luo, 2019. "Nonparametric Identification of First-Price Auction with Unobserved Competition: A Density Discontinuity Framework," Papers 1908.05476, arXiv.org, revised Dec 2024.
    3. Cheng Chou & Ruoyao Shi, 2021. "What time use surveys can (and cannot) tell us about labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 917-937, November.
    4. Yao Luo & Ruli Xiao, 2019. "Identification of Auction Models Using Order Statistics," Working Papers tecipa-630, University of Toronto, Department of Economics.
    5. Yingyao Hu & Zhongjian Lin, 2018. "Misclassification and the hidden silent rivalry," CeMMAP working papers CWP12/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    7. Grundl, Serafin & Zhu, Yu, 2024. "Two results on auctions with endogenous entry," Economics Letters, Elsevier, vol. 234(C).
    8. Grundl, Serafin & Zhu, Yu, 2019. "Identification and estimation of risk aversion in first-price auctions with unobserved auction heterogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 363-378.
    9. Arthur Lewbel & Xi Qu & Xun Tang, 2025. "Estimating Social Network Models with Link Misclassification," Papers 2509.07343, arXiv.org.

  30. Mavroeidis, Sophocles & Sasaki, Yuya & Welch, Ivo, 2015. "Estimation of heterogeneous autoregressive parameters with short panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 219-235.

    Cited by:

    1. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    2. Pesaran, M. H. & Yang, L., 2023. "Heterogeneous Autoregressions in Short T Panel Data Models," Cambridge Working Papers in Economics 2342, Faculty of Economics, University of Cambridge.
    3. M. Hashem Pesaran & Liying Yang, 2024. "Heterogeneous autoregressions in short T panel data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1359-1378, November.
    4. Mario J. Crucini & Nam T. Vu, 2017. "Did the American Recovery and Reinvestment Act Help Counties Most Affected by the Great Recession?," NBER Working Papers 24093, National Bureau of Economic Research, Inc.
    5. Durand, Robert B. & Greene, William H. & Harris, Mark N. & Khoo, Joye, 2022. "Heterogeneity in speed of adjustment using finite mixture models," Economic Modelling, Elsevier, vol. 107(C).
    6. Zhang, Yue-Jun & Liu, Zhao & Qin, Chang-Xiong & Tan, Tai-De, 2017. "The direct and indirect CO2 rebound effect for private cars in China," Energy Policy, Elsevier, vol. 100(C), pages 149-161.
    7. Ryo Okui & Takahide Yanagi, 2014. "Panel Data Analysis with Heterogeneous Dynamics," KIER Working Papers 906, Kyoto University, Institute of Economic Research.
    8. Hoskins, Stephen & Johnston, David W. & Kunz, Johannes S. & Shields, Michael A. & Staub, Kevin E., 2024. "Heterogeneity in the Persistence of Health: Evidence from a Monthly Micro Panel," IZA Discussion Papers 17023, IZA Network @ LISER.

  31. Sasaki, Yuya, 2015. "What Do Quantile Regressions Identify For General Structural Functions?," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1102-1116, October.

    Cited by:

    1. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    2. Xiaohong Chen & Wayne Yuan Gao, 2025. "Semiparametric Learning of Integral Functionals on Submanifolds," Cowles Foundation Discussion Papers 2450, Cowles Foundation for Research in Economics, Yale University.
    3. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    4. Ruofan Xu & Jiti Gao & Tatsushi Oka & Yoon-Jae Whang, 2022. "Quantile Random-Coefficient Regression with Interactive Fixed Effects: Heterogeneous Group-Level Policy Evaluation," Papers 2208.03632, arXiv.org, revised Nov 2024.
    5. Ying-Ying Lee, 2015. "Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification," Econometrics, MDPI, vol. 4(1), pages 1-14, December.
    6. Carolina Caetano & Gregorio Caetano & Leonard Goff & Eric Nielsen, 2025. "Identification of Causal Effects with a Bunching Design," Papers 2507.05210, arXiv.org.
    7. Chalak, Karim, 2019. "A note on the robustness of quantile treatment effect estimands," Economics Letters, Elsevier, vol. 185(C).
    8. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    9. Creemers, Sarah & Peeters, Ludo & Quiroz Castillo, Juan Luis & Vancauteren, Mark & Voordeckers, Wim, 2023. "Family firms and the labor productivity controversy: A distributional analysis of varying labor productivity gaps," Journal of Family Business Strategy, Elsevier, vol. 14(2).
    10. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    11. Leonard Goff, 2022. "Identifying causal effects with subjective ordinal outcomes," Papers 2212.14622, arXiv.org, revised Jan 2025.
    12. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    13. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.
    14. Ruofan Xu & Jiti Gao & Tatsushi Oka & Yoon-Jae Whang, 2022. "Estimation of Heterogeneous Treatment Effects Using Quantile Regression with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 13/22, Monash University, Department of Econometrics and Business Statistics.
    15. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
    16. Xiaohong Chen & Wayne Yuan Gao, 2025. "Thin Sets Are Not Equally Thin: Minimax Learning of Submanifold Integrals," Papers 2507.12673, arXiv.org, revised Mar 2026.

  32. Sasaki, Yuya, 2015. "Heterogeneity and selection in dynamic panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 236-249.

    Cited by:

    1. Sergi Jiménez-Martín & José M. Labeaga & Majid al Sadoon, 2020. "Consistent estimation of panel data sample selection models," Working Papers 2020-06, FEDEA.
    2. Xavier D'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear Difference-in-Differences in Repeated Cross Sections with Continuous Treatments," Boston College Working Papers in Economics 839, Boston College Department of Economics.
    3. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.
    4. Oliver Cassagneau-Francis & Robert Gary-Bobo & Julie Pernaudet & Jean-Marc Robin, 2022. "A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages," Sciences Po Economics Publications (main) hal-03869547, HAL.
    5. Gobillon, Laurent & Magnac, Thierry & Roux, Sébastien, 2025. "Lifecycle Wages and Human Capital Investments: Selection and Missing Data," IZA Discussion Papers 17838, IZA Network @ LISER.
    6. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    7. Chirok Han & Goeun Lee, 2017. "Efficient Estimation of Linear Panel Data Models with Sample Selection and Fixed Effects," Discussion Paper Series 1707, Institute of Economic Research, Korea University.
    8. José M Labeaga & Sergi Jiménez-Martín & Majid M. Al-Sadoon, 2019. "Simple Methods for Consistent Estimation of Dynamic Panel Data Sample Selection Models," Working Papers 1069, Barcelona School of Economics.
    9. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    10. Bunting, Jackson & Diegert, Paul & Maurel, Arnaud, 2025. "Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation," IZA Discussion Papers 17977, IZA Network @ LISER.
    11. Yamana Kazufumi, 2020. "Monte Carlo Evidence on the Estimation Method for Industry Dynamics," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-12, January.
    12. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    13. Meango, Romuald & Girsberger, Esther Mirjam, 2024. "Identification of Ex Ante Returns Using Elicited Choice Probabilities: An Application to Preferences for Public-Sector Jobs," IZA Discussion Papers 17174, IZA Network @ LISER.
    14. Sergi Jiménez-Martín & José María Labeaga, 2016. "Monte Carlo evidence on the estimation of AR(1) panel data sample selection models," Working Papers 2016-01, FEDEA.
    15. Kenichi Nagasawa, 2018. "Treatment Effect Estimation with Noisy Conditioning Variables," Papers 1811.00667, arXiv.org, revised Sep 2022.
    16. Nagasawa, Kenichi, 2020. "Identification and Estimation of Group-Level Partial Effects," The Warwick Economics Research Paper Series (TWERPS) 1243, University of Warwick, Department of Economics.
    17. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).
    18. Klimis Vogiatzoglou & Lien Phuong Nguyen, 2018. "Generation Of Tax Revenues And Economic Development: A Panel-Analysis For Emerging Economies In Asia," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 21, pages 9-30, June.
    19. Laurent Davezies & Xavier d'Haultfoeuille, 2013. "Endogenous Attrition in Panels," Working Papers 2013-17, Center for Research in Economics and Statistics.
    20. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    21. Yingyao Hu, 2015. "Microeconomic models with latent variables: applications of measurement error models in empirical industrial organization and labor economics," CeMMAP working papers CWP03/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  33. Hu, Yingyao & Sasaki, Yuya, 2015. "Closed-form estimation of nonparametric models with non-classical measurement errors," Journal of Econometrics, Elsevier, vol. 185(2), pages 392-408.

    Cited by:

    1. Yonghong An & Wang Le & Ruli Xiao, 2015. "Your American Dream is Not Mine! A New Approach to Estimating Intergenerational Mobility Elasticities," CAEPR Working Papers 2015-016, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    3. Firpo, Sergio & Galvao, Antonio F. & Song, Suyong, 2017. "Measurement errors in quantile regression models," Journal of Econometrics, Elsevier, vol. 198(1), pages 146-164.
    4. Hao Dong & Yuya Sasaki, 2022. "Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving," Papers 2209.05914, arXiv.org.
    5. Felt, Marie-Hélène, 2020. "On the identification of joint distributions using marginals and aggregates," Economics Letters, Elsevier, vol. 194(C).
    6. Adusumilli, Karun & Kurisu, Daisies & Otsu, Taisuke & Whang, Yoon-Jae, 2020. "Inference on distribution functions under measurement error," LSE Research Online Documents on Economics 102692, London School of Economics and Political Science, LSE Library.
    7. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    8. Li, Siran & Zheng, Xunjie, 2020. "A generalization of Lemma 1 in Kotlarski (1967)," Statistics & Probability Letters, Elsevier, vol. 165(C).
    9. Jiti Gao & Bin Peng & Zhao Ren & Xiaohui Zhang, 2015. "Variable Selection for a Categorical Varying-Coefficient Model with Identifications for Determinants of Body Mass Index," Monash Econometrics and Business Statistics Working Papers 21/15, Monash University, Department of Econometrics and Business Statistics.
    10. Andrei Zeleneev & Kirill Evdokimov, 2023. "Simple estimation of semiparametric models with measurement errors," CeMMAP working papers 10/23, Institute for Fiscal Studies.
    11. Kirill S. Evdokimov & Andrei Zeleneev, 2023. "Simple Estimation of Semiparametric Models with Measurement Errors," Papers 2306.14311, arXiv.org, revised Nov 2025.
    12. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    13. Takahide Yanagi, 2018. "Inference on Local Average Treatment Effects for Misclassified Treatment," Papers 1804.03349, arXiv.org.
    14. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2022. "Nonparametric estimation of additive models with errors-in-variables," LSE Research Online Documents on Economics 116007, London School of Economics and Political Science, LSE Library.
    15. Yingyao Hu, 2015. "Microeconomic models with latent variables: applications of measurement error models in empirical industrial organization and labor economics," CeMMAP working papers CWP03/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Rubo Zhao & Yixiang Tian & Ao Lei & Francis Boadu & Ze Ren, 2019. "The Effect of Local Government Debt on Regional Economic Growth in China: A Nonlinear Relationship Approach," Sustainability, MDPI, vol. 11(11), pages 1-22, May.

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