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Timothy Armstrong

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Timothy B Armstrong & Michal Kolesár, 2018. "A Simple Adjustment for Bandwidth Snooping," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 732-765.

    Mentioned in:

    1. A Simple Adjustment for Bandwidth Snooping (REStud 2018) in ReplicationWiki ()

Working papers

  1. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..

    Cited by:

    1. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    2. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.

  2. Timothy B. Armstrong & Michal Koles'ar & Mikkel Plagborg-M{o}ller, 2020. "Robust Empirical Bayes Confidence Intervals," Papers 2004.03448, arXiv.org, revised May 2022.

    Cited by:

    1. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
    2. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2020. "Inference on winners," CeMMAP working papers CWP43/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.

  3. Timothy B. Armstrong & Michal Koles'ar, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Papers 1808.07387, arXiv.org, revised Jul 2020.

    Cited by:

    1. Timothy Armstrong & Patrick M. Kline & Liyang Sun, 2024. "Adapting to Misspecification," NBER Working Papers 32906, National Bureau of Economic Research, Inc.
    2. Hahn, Jinyong & Hausman, Jerry & Kim, Jeonghwan, 2021. "A small sigma approach to certain problems in errors-in-variables models," Economics Letters, Elsevier, vol. 208(C).
    3. Stefano Della & Jörg Heining & Johannes F Schmieder & Simon Trenkle, 2023. "Evidence on Job Search Models from a Survey of Unemployed Workers in Germany," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(2), pages 1181-1232.
    4. Hwang, Jungbin & Kang, Byunghoon & Lee, Seojeong, 2022. "A doubly corrected robust variance estimator for linear GMM," Journal of Econometrics, Elsevier, vol. 229(2), pages 276-298.
    5. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "On the Informativeness of Descriptive Statistics for Structural Estimates," Econometrica, Econometric Society, vol. 88(6), pages 2231-2258, November.
    6. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    7. Thomas H. Jørgensen, 2021. "Sensitivity to Calibrated Parameters," CEBI working paper series 20-14, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    8. Timothy B. Armstrong & Michal Koles'r, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Cowles Foundation Discussion Papers 2158R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2019.
    9. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
    10. Maximilian Blesch & Philipp Eisenhauer, 2023. "Robust Decision-Making under Risk and Ambiguity," Rationality and Competition Discussion Paper Series 463, CRC TRR 190 Rationality and Competition.
    11. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    12. de Paula, Aureo, 2020. "The Informativeness of Estimation Moments," CEPR Discussion Papers 14298, C.E.P.R. Discussion Papers.
    13. Philipp Eisenhauer & Lena Janys & Christopher Walsh & Janós Gabler, 2023. "Structural Models for Policy-Making," CRC TR 224 Discussion Paper Series crctr224_2023_484, University of Bonn and University of Mannheim, Germany.
    14. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    15. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "Transparency in Structural Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 711-722, October.
    16. Stéphane Bonhomme, 2020. "A Comment on: “On the Informativeness of Descriptive Statistics for Structural Estimates” by Isaiah Andrews, Matthew Gentzkow, and Jesse M. Shapiro," Econometrica, Econometric Society, vol. 88(6), pages 2259-2264, November.
    17. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    19. Raffaella Giacomini & Toru Kitagawa & Harald Uhlig, 2019. "Estimation Under Ambiguity," CeMMAP working papers CWP24/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    21. Naoya Sueishi, 2022. "A Misuse of Specification Tests," Papers 2211.11915, arXiv.org.
    22. Timothy Christensen & Benjamin Connault, 2023. "Counterfactual Sensitivity and Robustness," Econometrica, Econometric Society, vol. 91(1), pages 263-298, January.

  4. Timothy B. Armstrong, 2018. "Adaptation Bounds for Confidence Bands under Self-Similarity," Cowles Foundation Discussion Papers 2146, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Timothy B. Armstrong, 2018. "Adaptation Bounds for Confidence Bands under Self-Similarity," Cowles Foundation Discussion Papers 2146, Cowles Foundation for Research in Economics, Yale University.

  5. Timothy B. Armstrong, 2017. "On the Choice of Test Statistic for Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1960R2, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    2. Le-Yu Chen & Sokbae (Simon) Lee, 2015. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 26/15, Institute for Fiscal Studies.
    3. Timothy B. Armstrong & Hock Peng Chan, 2013. "Multiscale Adaptive Inference on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1885R, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.
    4. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    5. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.

  6. Timothy B. Armstrong & Michal Koles'ar, 2017. "Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness," Papers 1712.04594, arXiv.org, revised Jan 2021.

    Cited by:

    1. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
    2. Cl'ement de Chaisemartin, 2021. "Trading-off Bias and Variance in Stratified Experiments and in Matching Studies, Under a Boundedness Condition on the Magnitude of the Treatment Effect," Papers 2105.08766, arXiv.org, revised Jan 2024.
    3. Laurent Davezies & Xavier D'Haultfoeuille & Louise Laage, 2021. "Identification and Estimation of Average Marginal Effects in Fixed Effects Logit Models," Papers 2105.00879, arXiv.org, revised Oct 2022.
    4. Zichen Deng & Maarten Lindeboom, 2021. "Early-life Famine Exposure, Hunger Recall and Later-life Health," Tinbergen Institute Discussion Papers 21-054/V, Tinbergen Institute.
    5. Zichen Deng & Maarten Lindeboom, 2021. "Early-life Famine Exposure, Hunger Recall and Later-life Health," Papers 2021-04, Centre for Health Economics, Monash University.
    6. Zichen Deng & Maarten Lindeboom, 2022. "Early‐life famine exposure, hunger recall, and later‐life health," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 771-787, June.
    7. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    8. Deng, Zichen & Lindeboom, Maarten, 2021. "Early-Life Famine Exposure, Hunger Recall and Later-Life Health," IZA Discussion Papers 14487, Institute of Labor Economics (IZA).
    9. Ferman, Bruno, 2017. "Matching Estimators with Few Treated and Many Control Observations," MPRA Paper 78940, University Library of Munich, Germany.
    10. Timothy B. Armstrong & Michal Koles'r, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Cowles Foundation Discussion Papers 2158R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2019.
    11. Huiming Zhang & Haoyu Wei & Guang Cheng, 2023. "Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm," Papers 2303.07287, arXiv.org, revised Jan 2024.
    12. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
    13. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    14. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
    15. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    16. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    17. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
    18. Sokbae Lee & Martin Weidner, 2021. "Bounding Treatment Effects by Pooling Limited Information across Observations," Papers 2111.05243, arXiv.org, revised Dec 2023.
    19. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    20. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Oct 2024.
    21. 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.
    22. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2022. "Double Robust Bayesian Inference on Average Treatment Effects," Papers 2211.16298, arXiv.org, revised Oct 2024.

  7. Timothy B. Armstrong & Michal Koles�r, 2016. "Simple and Honest Confidence Intervals in Nonparametric Regression," Cowles Foundation Discussion Papers 2044R2, Cowles Foundation for Research in Economics, Yale University, revised Mar 2018.

    Cited by:

    1. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    2. 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.
    3. Giuseppe Rose & Desiré De Luca, 2024. "Health Concerns And Consumption Expectations During Covid-19: Evidence From A Fuzzy Regression Discontinuity Design," Working Papers 202401, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    4. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    5. Mueller, Clemens, 2023. "Reacting to Early Failure in University: Evidence from a Regression Discontinuity Design," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277620, Verein für Socialpolitik / German Economic Association.
    6. Federico A. Bugni & Ivan A. Canay, 2018. "Testing continuity of a density via g -order statistics in the regression discontinuity design," CeMMAP working papers CWP20/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org.
    8. Matti Sarvimäki & Hanna Pesola, 2022. "Intergenerational Spillovers of Integration Policies: Evidence from Finland’s Integration Plans," RF Berlin - CReAM Discussion Paper Series 2212, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    9. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    10. Pesola, Hanna Onerva & Sarvimäki, Matti, 2022. "Intergenerational Spillovers of Integration Policies: Evidence from Finland's Integration Plans," IZA Discussion Papers 15310, Institute of Labor Economics (IZA).
    11. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    12. Guastavino, Carlos & Miranda, Alvaro & Montero, Rodrigo, 2021. "Rank effect in bureaucrat recruitment," European Journal of Political Economy, Elsevier, vol. 68(C).
    13. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    14. Patrizia Ordine & Giuseppe Rose, 2019. "Early entry, age-at-test, and schooling attainment: evidence from Italian primary schools," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 36(3), pages 761-784, October.
    15. Tomasz Olma, 2021. "Nonparametric Estimation of Truncated Conditional Expectation Functions," Papers 2109.06150, arXiv.org.
    16. 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.
    17. David Figlio & Krzysztof Karbownik & Umut Özek & David N. Figlio, 2023. "Sibling Spillovers May Enhance the Efficacy of Targeted School Policies," CESifo Working Paper Series 10526, CESifo.
    18. Samantha E. Clark & Ruth Etzioni & Jerry Radich & Zachary Marcum & Anirban Basu, 2023. "The price elasticity of Gleevec in patients with Chronic Myeloid Leukemia enrolled in Medicare Part D: Evidence from a regression discontinuity design," Papers 2305.06076, arXiv.org.
    19. Zincenko, Federico, 2024. "Estimation and inference of seller’s expected revenue in first-price auctions," Journal of Econometrics, Elsevier, vol. 241(1).
    20. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    22. Dean Eckles & Nikolaos Ignatiadis & Stefan Wager & Han Wu, 2020. "Noise-Induced Randomization in Regression Discontinuity Designs," Papers 2004.09458, arXiv.org, revised Nov 2023.
    23. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    24. Freyberger, Joachim & Rai, Yoshiyasu, 2018. "Uniform confidence bands: Characterization and optimality," Journal of Econometrics, Elsevier, vol. 204(1), pages 119-130.
    25. Fang, Guanfu & Miao, Liya, 2024. "Expanding boundaries: The Impact of kindergarten availability on women's employment in China," Labour Economics, Elsevier, vol. 88(C).
    26. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    27. Federico Crippa, 2024. "Manipulation Test for Multidimensional RDD," Papers 2402.10836, arXiv.org, revised Jun 2024.
    28. Yingying Dong & Michal Kolesár, 2023. "When can we ignore measurement error in the running variable?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 735-750, August.
    29. Martti Kaila, 2024. "How Do People React to Income-Based Fines? Evidence from Speeding Tickets Discontinuities," CESifo Working Paper Series 11064, CESifo.

  8. Timothy B. Armstrong & Michal Koles�r, 2016. "Optimal Inference in a Class of Regression Models," Cowles Foundation Discussion Papers 2043R, Cowles Foundation for Research in Economics, Yale University, revised May 2017.

    Cited by:

    1. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    2. Gregory Fletcher Cox, 2024. "A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality," Papers 2409.09962, arXiv.org.
    3. Timothy Armstrong & Patrick M. Kline & Liyang Sun, 2024. "Adapting to Misspecification," NBER Working Papers 32906, National Bureau of Economic Research, Inc.
    4. Michael P. Leung, 2023. "Cluster-Randomized Trials with Cross-Cluster Interference," Papers 2310.18836, arXiv.org, revised Nov 2024.
    5. Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Cowles Foundation Discussion Papers 2283, Cowles Foundation for Research in Economics, Yale University.
    6. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
    7. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2020. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," Working Papers 638, Princeton University, Department of Economics, Industrial Relations Section..
    8. Paul Goldsmith-Pinkham & Karen Jiang & Zirui Song & Jacob Wallace, 2022. "Measuring Changes in Disparity Gaps: An Application to Health Insurance," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 356-360, May.
    9. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2020. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," CESifo Working Paper Series 8137, CESifo.
    10. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg-Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Working Papers 2022-27, Princeton University. Economics Department..
    11. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    12. Feng, Jin & Song, Hong & Wang, Zhen, 2020. "The elderly's response to a patient cost-sharing policy in health insurance: Evidence from China," Journal of Economic Behavior & Organization, Elsevier, vol. 169(C), pages 189-207.
    13. 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.
    14. Yi Zhang & Eli Ben-Michael & Kosuke Imai, 2022. "Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs," Papers 2208.13323, arXiv.org, revised Sep 2024.
    15. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    16. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    17. Giuseppe Rose & Desiré De Luca, 2024. "Health Concerns And Consumption Expectations During Covid-19: Evidence From A Fuzzy Regression Discontinuity Design," Working Papers 202401, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    18. Timothy B. Armstrong & Michal Koles'r, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Cowles Foundation Discussion Papers 2158R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2019.
    19. Yoici Arai & Taisuke Otsu & Myung Hwan Seo, 2022. "Regression discontinuity design with potentially many covariates," STICERD - Econometrics Paper Series 626, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    20. Atı̇la Abdulkadı̇roğlu & Joshua D. Angrist & Yusuke Narita & Parag Pathak, 2022. "Breaking Ties: Regression Discontinuity Design Meets Market Design," Econometrica, Econometric Society, vol. 90(1), pages 117-151, January.
    21. Federico A. Bugni & Ivan A. Canay, 2018. "Testing continuity of a density via g -order statistics in the regression discontinuity design," CeMMAP working papers CWP20/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    23. Bertanha, Marinho & Moreira, Marcelo J., 2020. "Impossible inference in econometrics: Theory and applications," Journal of Econometrics, Elsevier, vol. 218(2), pages 247-270.
    24. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    25. Bai, Yuehao, 2023. "Why randomize? Minimax optimality under permutation invariance," Journal of Econometrics, Elsevier, vol. 232(2), pages 565-575.
    26. Alexander Kreiss & Christoph Rothe, 2023. "Inference in regression discontinuity designs with high-dimensional covariates," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 105-123.
    27. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    28. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    29. Chenchuan (Mark) Li & Ulrich K. Müller, 2020. "Linear Regression with Many Controls of Limited Explanatory Power," Working Papers 2020-57, Princeton University. Economics Department..
    30. Yiqi Liu & Yuan Qi, 2023. "Using Forests in Multivariate Regression Discontinuity Designs," Papers 2303.11721, arXiv.org, revised Jul 2024.
    31. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    32. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    33. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
    34. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    35. Xiao Huang & Zhaoguo Zhan, 2020. "Local Composite Quantile Regression for Regression Discontinuity," Papers 2009.03716, arXiv.org, revised Oct 2021.
    36. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion Paper Series 730, Institute of Economic Research, Hitotsubashi University.
    37. Huynh, Nhan, 2023. "Unemployment beta and the cross-section of stock returns: Evidence from Australia," International Review of Financial Analysis, Elsevier, vol. 86(C).
    38. Walter Beckert & Daniel Kaliski, 2019. "Honest inference for discrete outcomes," CeMMAP working papers CWP67/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    39. Tuvaandorj, Purevdorj, 2020. "Regression discontinuity designs, white noise models, and minimax," Journal of Econometrics, Elsevier, vol. 218(2), pages 587-608.
    40. Yingying Dong & Michal Kolesár, 2023. "When can we ignore measurement error in the running variable?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 735-750, August.

  9. Isaiah Andrews & Timothy B. Armstrong, 2015. "Unbiased Instrumental Variables Estimation under Known First-Stage Sign," Cowles Foundation Discussion Papers 1984R5, Cowles Foundation for Research in Economics, Yale University, revised Nov 2016.

    Cited by:

    1. Brantly Callaway & Sonia Karami, 2020. "Treatment Effects in Interactive Fixed Effects Models with a Small Number of Time Periods," Papers 2006.15780, arXiv.org, revised Feb 2022.
    2. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    3. Karthik Rajkumar, 2019. "Ridge regularization for Mean Squared Error Reduction in Regression with Weak Instruments," Papers 1904.08580, arXiv.org.
    4. Pablo Brassiolo & Ricardo Estrada & Gustavo Fajardo & Juan F. Vargas, 2020. "Self-Selection into Corruption: Evidence from the Lab," Documentos de Trabajo 18182, The Latin American and Caribbean Economic Association (LACEA).
    5. Angrist, Joshua & Kolesár, Michal, 2024. "One instrument to rule them all: The bias and coverage of just-ID IV," Journal of Econometrics, Elsevier, vol. 240(2).
    6. Timothy Derdenger & Vineet Kumar, 2019. "Estimating dynamic discrete choice models with aggregate data: Properties of the inclusive value approximation," Quantitative Marketing and Economics (QME), Springer, vol. 17(4), pages 359-384, December.
    7. Matthew C. Harding & Jerry Hausman & Christopher Palmer, 2015. "Finite sample bias corrected IV estimation for weak and many instruments," CeMMAP working papers CWP41/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Sebastián Amador, 2022. "Hysteresis, endogenous growth, and monetary policy," Working Papers 348, University of California, Davis, Department of Economics.
    9. Roach, Travis & Nath, Saheli, 2023. "Counties with More Vietnam Veterans Have Higher Suicide Rates," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 53(1), April.
    10. Müller, Ulrich K. & Wang, Yulong, 2019. "Nearly weighted risk minimal unbiased estimation," Journal of Econometrics, Elsevier, vol. 209(1), pages 18-34.
    11. Khan, Umair & Khalid, Umair & Farooq, Fatima, 2021. "Endogeneity Quagmire Empirical Evidence from Telecommunication Industry of Pakistan," Journal of Accounting and Finance in Emerging Economies, CSRC Publishing, Center for Sustainability Research and Consultancy Pakistan, vol. 7(4), pages 955-967, December.
    12. Bunkanwanicha, Pramuan & Di Giuli, Alberta & Salvade, Federica, 2022. "Bank CEO careers after bailouts: The effects of management turnover on bank risk," Journal of Financial Intermediation, Elsevier, vol. 52(C).
    13. David M. Kaplan, 2019. "Unbiased Estimation as a Public Good," Working Papers 1911, Department of Economics, University of Missouri.
    14. Matthew C. Harding & Jerry Hausman & Christopher Palmer, 2015. "Finite sample bias corrected IV estimation for weak and many instruments," CeMMAP working papers 41/15, Institute for Fiscal Studies.
    15. Michael Keane & Timothy Neal, 2021. "A Practical Guide to Weak Instruments," Discussion Papers 2021-05b, School of Economics, The University of New South Wales.
    16. Tetsuya Kaji, 2021. "Theory of Weak Identification in Semiparametric Models," Econometrica, Econometric Society, vol. 89(2), pages 733-763, March.
    17. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).

  10. Timothy B. Armstrong & Michal Kolesar, 2014. "A Simple Adjustment for Bandwidth Snooping," Cowles Foundation Discussion Papers 1961R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2015.

    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. Susanne M Schennach, 2020. "A Bias Bound Approach to Non-parametric Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(5), pages 2439-2472.
    3. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    4. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    5. Xu, Ke-Li, 2017. "Regression discontinuity with categorical outcomes," Journal of Econometrics, Elsevier, vol. 201(1), pages 1-18.
    6. Chen, Heng & Fan, Yanqin, 2019. "Identification and wavelet estimation of weighted ATE under discontinuous and kink incentive assignment mechanisms," Journal of Econometrics, Elsevier, vol. 212(2), pages 476-502.
    7. Xu, Ke-Li, 2018. "A semi-nonparametric estimator of regression discontinuity design with discrete duration outcomes," Journal of Econometrics, Elsevier, vol. 206(1), pages 258-278.
    8. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2024.

  11. Timothy B. Armstrong, 2014. "A Note on Minimax Testing and Confidence Intervals in Moment Inequality Models," Cowles Foundation Discussion Papers 1975, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.

  12. Timothy B. Armstrong, 2014. "Adaptive Testing on a Regression Function at a Point," Cowles Foundation Discussion Papers 1957, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.

    Cited by:

    1. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    2. Koohyun Kwon & Soonwoo Kwon, 2020. "Inference in Regression Discontinuity Designs under Monotonicity," Papers 2011.14216, arXiv.org.
    3. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    4. Susanne M Schennach, 2020. "A Bias Bound Approach to Non-parametric Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(5), pages 2439-2472.
    5. Patrick M. Kline & Evan K. Rose & Christopher R. Walters, 2021. "Systemic Discrimination Among Large U.S. Employers," NBER Working Papers 29053, National Bureau of Economic Research, Inc.
    6. Timothy B. Armstrong & Michal Kolesar, 2014. "A Simple Adjustment for Bandwidth Snooping," Cowles Foundation Discussion Papers 1961R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2015.
    7. Timothy B. Armstrong & Michal Kolesár, 2018. "Optimal Inference in a Class of Regression Models," Econometrica, Econometric Society, vol. 86(2), pages 655-683, March.
    8. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers CWP59/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    10. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
    11. Koohyun Kwon & Soonwoo Kwon, 2020. "Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity," Papers 2011.14219, arXiv.org.
    12. Kline, Patrick & Walters, Christopher, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Institute for Research on Labor and Employment, Working Paper Series qt3z72m9kn, Institute of Industrial Relations, UC Berkeley.
    13. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    14. 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.

  13. Timothy B. Armstrong & Shu Shen, 2013. "Inference on Optimal Treatment Assignments," Cowles Foundation Discussion Papers 1927R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2014.

    Cited by:

    1. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    2. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    3. Lehrer, Steven F. & Pohl, R. Vincent & Song, Kyungchul, 2018. "Multiple Testing and the Distributional Effects of Accountability Incentives in Education," MPRA Paper 89532, University Library of Munich, Germany.
    4. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    5. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," NBER Working Papers 25636, National Bureau of Economic Research, Inc.
    6. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    7. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    8. Yuya Sasaki & Takuya Ura, 2020. "Welfare Analysis via Marginal Treatment Effects," Papers 2012.07624, arXiv.org.
    9. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Aug 2024.
    10. Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers 10/15, Institute for Fiscal Studies.
    11. Juliano Assuncao & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," Working Papers tecipa-631, University of Toronto, Department of Economics.
    12. Undral Byambadalai, 2022. "Identification and Inference for Welfare Gains without Unconfoundedness," Papers 2207.04314, arXiv.org.
    13. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.

  14. Timothy B. Armstrong & Hock Peng Chan, 2013. "Multiscale Adaptive Inference on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1885R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2015.

    Cited by:

    1. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    2. Le-Yu Chen & Sokbae (Simon) Lee, 2015. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 26/15, Institute for Fiscal Studies.
    3. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. 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.
    5. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    6. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae Lee, 2018. "The identification power of smoothness assumptions in models with counterfactual outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 617-642, July.
    7. Michael Vogt & Oliver Linton, 2018. "Multiscale clustering of nonparametric regression curves," CeMMAP working papers CWP08/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    9. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    10. Donald W.K. Andrews & Xiaoxia Shi, 2011. "Nonparametric Inference Based on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1840R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2013.
    11. João Madeira & Nuno Palma, 2018. "Measuring Monetary Policy Deviations from the Taylor Rule," Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    12. Timothy B. Armstrong & Michal Kolesar, 2014. "A Simple Adjustment for Bandwidth Snooping," Cowles Foundation Discussion Papers 1961R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2015.
    13. Victor Chernozhukov & Wooyoung Kim & Sokbae (Simon) Lee & Adam Rosen, 2013. "Implementing intersection bounds in Stata," CeMMAP working papers 38/13, Institute for Fiscal Studies.
    14. Ivan A. Canay & Azeem M. Shaikh, 2016. "Practical and theoretical advances in inference for partially identified models," CeMMAP working papers CWP05/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers CWP23/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Denis Chetverikov & Daniel Wilhelm & Dongwoo Kim, 2019. "An adaptive test of stochastic monotonicity," CeMMAP working papers CWP49/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Nick Koning & Paul Bekker, 2019. "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers 1904.12775, arXiv.org, revised Jun 2020.
    20. Isaac Loh, 2024. "Inference under partial identification with minimax test statistics," Papers 2401.13057, arXiv.org, revised Apr 2024.
    21. Andrew Chesher & Adam Rosen, 2019. "Generalized Instrumental Variable Models, Methods, and Applications," CeMMAP working papers CWP41/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2016.
    23. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers 53/13, Institute for Fiscal Studies.
    24. Shi, Chengchun & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2021. "An online sequential test for qualitative treatment effects," LSE Research Online Documents on Economics 112521, London School of Economics and Political Science, LSE Library.
    25. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    26. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    27. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.

Articles

  1. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
    See citations under working paper version above.
  2. Timothy B. Armstrong & Michal Kolesár, 2021. "Finite‐Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness," Econometrica, Econometric Society, vol. 89(3), pages 1141-1177, May.
    See citations under working paper version above.
  3. Timothy B. Armstrong & Michal Kolesár, 2020. "Simple and honest confidence intervals in nonparametric regression," Quantitative Economics, Econometric Society, vol. 11(1), pages 1-39, January.
    See citations under working paper version above.
  4. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    See citations under working paper version above.
  5. Timothy B. Armstrong & Michal Kolesár, 2018. "Optimal Inference in a Class of Regression Models," Econometrica, Econometric Society, vol. 86(2), pages 655-683, March.
    See citations under working paper version above.
  6. Timothy B Armstrong & Michal Kolesár, 2018. "A Simple Adjustment for Bandwidth Snooping," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 732-765.
    See citations under working paper version above.
  7. Isaiah Andrews & Timothy B. Armstrong, 2017. "Unbiased instrumental variables estimation under known first‐stage sign," Quantitative Economics, Econometric Society, vol. 8(2), pages 479-503, July.
    See citations under working paper version above.
  8. Armstrong, Timothy B. & Chan, Hock Peng, 2016. "Multiscale adaptive inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 194(1), pages 24-43.
    See citations under working paper version above.
  9. Timothy B. Armstrong, 2016. "Large Market Asymptotics for Differentiated Product Demand Estimators With Economic Models of Supply," Econometrica, Econometric Society, vol. 84, pages 1961-1980, September.

    Cited by:

    1. Susan J. Méndez, 2016. "Parallel Trade of Pharmaceuticals: The Danish Market for Statins," Melbourne Institute Working Paper Series wp2016n08, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    2. Laura Grigolon, 2017. "Blurred boundaries: a flexible approach for segmentation applied to the car market," Department of Economics Working Papers 2017-17, McMaster University.
    3. Bartosz Olesiński, 2020. "The Analysis of the Tobacco Product Bans Using a Random Coefficients Logit Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 113-144, June.
    4. Christopher T. Conlon & Julie Holland Mortimer, 2018. "Empirical Properties of Diversion Ratios," Working Papers 18-16, New York University, Leonard N. Stern School of Business, Department of Economics.
    5. Mogens Fosgerau & Julien Monardo & André de Palma, 2024. "The Inverse Product Differentiation Logit Model," American Economic Journal: Microeconomics, American Economic Association, vol. 16(4), pages 329-370, November.
    6. Otsu, Taisuke & Sunada, Keita, 2024. "On large market asymptotics for spatial price competition models," Economics Letters, Elsevier, vol. 234(C).
    7. Givord, Pauline & Grislain-Letrémy, Céline & Naegele, Helene, 2018. "How do fuel taxes impact new car purchases? An evaluation using French consumer-level data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 74, pages 76-96.
    8. Pietro Tebaldi & Alexander Torgovitsky & Hanbin Yang, 2019. "Nonparametric Estimates of Demand in the California Health Insurance Exchange," NBER Working Papers 25827, National Bureau of Economic Research, Inc.
    9. Fabian Dunker & Stefan Hoderlein & Hiroaki Kaido, 2023. "Nonparametric identification of random coefficients in aggregate demand models for differentiated products," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 279-306.
    10. Otsu, Taisuke & Sunada, Keita, 2024. "On large market asymptotics for spatial price competition models," LSE Research Online Documents on Economics 120588, London School of Economics and Political Science, LSE Library.
    11. Xavier D'Haultfoeuille & Isis Durrmeyer & Philippe Février, 2017. "Automobile Prices in Market Equilibrium with Unobserved Price Discrimination," Working Papers 2017-18, Center for Research in Economics and Statistics.
    12. Masayuki Sawada & Kohei Kawaguchi, 2020. "Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients," Papers 2004.08791, arXiv.org.
    13. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    14. 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.
    15. Lu, Tingmingke, 2023. "Response of new car buyers to alternative energy policies: The role of vehicle use heterogeneity," Economic Modelling, Elsevier, vol. 120(C).
    16. Néstor Duch-Brown & Lukasz Grzybowski & André Romahn & Frank Verboven, 2022. "Evaluating the Impact of Online Market Integration-Evidence from the EU Portable PC Market," Working Papers hal-03780118, HAL.
    17. Hugo Molina, 2024. "Buyer Alliances in Vertically Related Markets," Working Papers hal-03340176, HAL.
    18. Philipp Ketz, 2019. "On asymptotic size distortions in the random coefficients logit model," Post-Print halshs-02302067, HAL.
    19. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," NBER Working Papers 29305, National Bureau of Economic Research, Inc.
    20. Gabaix, Xavier & Laibson, David & Li, Deyuan & Li, Hongyi & Resnick, Sidney & de Vries, Casper G., 2016. "The impact of competition on prices with numerous firms," Journal of Economic Theory, Elsevier, vol. 165(C), pages 1-24.
    21. Isaiah Andrews & Anna Mikusheva, 2022. "GMM is Inadmissible Under Weak Identification," Papers 2204.12462, arXiv.org, revised May 2023.
    22. Donald W. K. Andrews & Patrik Guggenberger, 2015. "Identification- and Singularity-Robust Inference for Moment Condition," Cowles Foundation Discussion Papers 1978R2, Cowles Foundation for Research in Economics, Yale University, revised Jan 2019.
    23. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    24. Wang, Ao, 2020. "Identifying the Distribution of Random Coefficients in BLP Demand Models Using One Single Variation in Product Characteristics," The Warwick Economics Research Paper Series (TWERPS) 1304, University of Warwick, Department of Economics.
    25. Salanié, Bernard & Wolak, Frank, 2018. "Fast, “Robust†, and Approximately Correct: Estimating Mixed Demand Systems," CEPR Discussion Papers 13236, C.E.P.R. Discussion Papers.
    26. Amit Gandhi & Jean-François Houde, 2019. "Measuring Substitution Patterns in Differentiated-Products Industries," NBER Working Papers 26375, National Bureau of Economic Research, Inc.
    27. Wang, Ao, 2021. "A BLP Demand Model of Product-Level Market Shares with Complementarity," The Warwick Economics Research Paper Series (TWERPS) 1351, University of Warwick, Department of Economics.
    28. Bernard Salanie & Frank A. Wolak, 2018. "Fast, "robust", and approximately correct: estimating mixed demand systems," CeMMAP working papers CWP64/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    29. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    30. Kandelhardt, Johannes, 2023. "Flexible estimation of random coefficient logit models of differentiated product demand," DICE Discussion Papers 399, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    31. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
    32. Matthew Weinberg & Gloria Sheu & Nathan Miller, 2019. "Oligopolistic Price Leadership and Mergers: An Empirical Model of the U.S. Beer Industry," 2019 Meeting Papers 1210, Society for Economic Dynamics.
    33. Amit Gandhi & Zhentong Lu & Xiaoxia Shi, 2023. "Estimating demand for differentiated products with zeroes in market share data," Quantitative Economics, Econometric Society, vol. 14(2), pages 381-418, May.

  10. Armstrong, Timothy B., 2015. "Asymptotically exact inference in conditional moment inequality models," Journal of Econometrics, Elsevier, vol. 186(1), pages 51-65.

    Cited by:

    1. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    2. Le-Yu Chen & Sokbae (Simon) Lee, 2015. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 26/15, Institute for Fiscal Studies.
    3. Christian Bontemps & Thierry Magnac, 2017. "Set identification, moment restrictions, and inference," Post-Print hal-01575813, HAL.
    4. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    5. João Madeira & Nuno Palma, 2018. "Measuring Monetary Policy Deviations from the Taylor Rule," Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    6. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers CWP23/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Shosei Sakaguchi, 2024. "Partial identification and inference in duration models with endogenous censoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 308-326, March.
    8. Rui Wang, 2023. "Testing and Identifying Substitution and Complementarity Patterns," Papers 2304.00678, arXiv.org.
    9. Andrew Chesher & Adam Rosen, 2019. "Generalized Instrumental Variable Models, Methods, and Applications," CeMMAP working papers CWP41/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2016.
    11. Aradillas-López, Andrés & Gandhi, Amit & Quint, Daniel, 2016. "A simple test for moment inequality models with an application to English auctions," Journal of Econometrics, Elsevier, vol. 194(1), pages 96-115.
    12. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    13. Lorenz Gschwent & Bjorn Hammarfelt & Martin Karlsson & Mathias Kifmann, 2024. "The Rise of Health Economics: Transforming the Landscape of Economic Research," Papers 2410.06313, arXiv.org.
    14. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.
    15. Breunig, Christoph, 2015. "Testing missing at random using instrumental variables," SFB 649 Discussion Papers 2015-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  11. Armstrong, Timothy B., 2014. "Weighted KS statistics for inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 181(2), pages 92-116.

    Cited by:

    1. Xiaohong Chen & Timothy Christensen & Keith O’Hara & Elie Tamer, 2016. "MCMC Confidence sets for Identified Sets," Cowles Foundation Discussion Papers 2037R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2016.
    2. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    3. Le-Yu Chen & Sokbae (Simon) Lee, 2015. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 26/15, Institute for Fiscal Studies.
    4. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2013. "Specification tests for partially identified models defined by moment inequalities," CeMMAP working papers 01/13, Institute for Fiscal Studies.
    5. 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.
    6. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Christian Bontemps & Thierry Magnac, 2017. "Set identification, moment restrictions, and inference," Post-Print hal-01575813, HAL.
    8. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2014. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers CWP05/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    10. Hiroaki Kaido & Yi Zhang, 2019. "Robust likelihood ratio tests for incomplete economic models," CeMMAP working papers CWP68/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Shosei Sakaguchi, 2021. "Partial Identification and Inference in Duration Models with Endogenous Censoring," Papers 2107.00928, arXiv.org.
    12. João Madeira & Nuno Palma, 2018. "Measuring Monetary Policy Deviations from the Taylor Rule," Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    13. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers 43/17, Institute for Fiscal Studies.
    14. Andrew Chesher & Adam Rosen, 2016. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers CWP44/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers CWP23/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Denis Chetverikov & Daniel Wilhelm & Dongwoo Kim, 2019. "An adaptive test of stochastic monotonicity," CeMMAP working papers CWP49/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Armstrong, Timothy B., 2015. "Asymptotically exact inference in conditional moment inequality models," Journal of Econometrics, Elsevier, vol. 186(1), pages 51-65.
    18. Shosei Sakaguchi, 2024. "Partial identification and inference in duration models with endogenous censoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 308-326, March.
    19. Timothy B. Armstrong & Hock Peng Chan, 2013. "Multiscale Adaptive Inference on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1885R, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.
    20. 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).
    21. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers 28/16, Institute for Fiscal Studies.
    22. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2016.
    23. Aradillas-López, Andrés & Gandhi, Amit & Quint, Daniel, 2016. "A simple test for moment inequality models with an application to English auctions," Journal of Econometrics, Elsevier, vol. 194(1), pages 96-115.
    24. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    25. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    26. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.

  12. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.

    Cited by:

    1. Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2012. "Asymptotic Efficiency of Semiparametric Two-step GMM," Cowles Foundation Discussion Papers 1880, Cowles Foundation for Research in Economics, Yale University.
    2. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    3. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    4. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2020. "A higher-order correct fast moving-average bootstrap for dependent data," Working Papers unige:129395, University of Geneva, Geneva School of Economics and Management.
    5. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. Jean-Jacques Forneron, 2022. "Estimation and Inference by Stochastic Optimization," Papers 2205.03254, arXiv.org.
    7. Forneron, Jean-Jacques, 2024. "Estimation and inference by stochastic optimization," Journal of Econometrics, Elsevier, vol. 238(2).
    8. Jinyong Hahn & Zhipeng Liao, 2021. "Bootstrap Standard Error Estimates and Inference," Econometrica, Econometric Society, vol. 89(4), pages 1963-1977, July.
    9. Chen, Xiaohong & Liao, Zhipeng, 2015. "Sieve semiparametric two-step GMM under weak dependence," Journal of Econometrics, Elsevier, vol. 189(1), pages 163-186.
    10. Jean-Jacques Forneron & Serena Ng, 2020. "Inference by Stochastic Optimization: A Free-Lunch Bootstrap," Papers 2004.09627, arXiv.org, revised Sep 2020.
    11. Hong, Han & Li, Weiming & Wang, Boyu, 2015. "Estimation of dynamic discrete models from time aggregated data," Journal of Econometrics, Elsevier, vol. 188(2), pages 435-446.

  13. Timothy B. Armstrong, 2013. "Bounds in auctions with unobserved heterogeneity," Quantitative Economics, Econometric Society, vol. 4(3), pages 377-415, November.

    Cited by:

    1. Yusuke Matsuki, 2016. "A Distribution-Free Test of Monotonicity with an Application to Auctions," Working Papers e110, Tokyo Center for Economic Research.
    2. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    3. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    4. Giovanni Compiani & Philip Haile & Marcelo Sant’Anna, 2020. "Common Values, Unobserved Heterogeneity, and Endogenous Entry in US Offshore Oil Lease Auctions," Journal of Political Economy, University of Chicago Press, vol. 128(10), pages 3872-3912.
    5. Luo, Yao, 2020. "Unobserved heterogeneity in auctions under restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 216(2), pages 354-374.
    6. Yan, Zhen & Han, Fei & Wang, Holly & Shen, Yun & Zhou, Jiehong, 2024. "Purchasing habits, age effects and Chinese consumers' willingness to pay for chilled pork: Evidence from a random Nth-price auction experiment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(02), January.
    7. Andrew Chesher & Adam Rosen, 2017. "Incomplete English auction models with heterogeneity," CeMMAP working papers CWP27/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Joachim Freyberger & Bradley J. Larsen, 2022. "Identification in ascending auctions, with an application to digital rights management," Quantitative Economics, Econometric Society, vol. 13(2), pages 505-543, May.
    9. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Andrew Chesher & Adam Rosen, 2015. "Identification of the distribution of valuations in an incomplete model of English auctions," CeMMAP working papers 30/15, Institute for Fiscal Studies.
    11. Yao Luo & Yuanyuan Wan, 2015. "Integrated-quantile-based estimation for first price auction models," Working Papers tecipa-539, University of Toronto, Department of Economics.

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