IDEAS home Printed from https://ideas.repec.org/f/pma2923.html
   My authors  Follow this author

Matthew A. Masten

Personal Details

First Name:Matthew
Middle Name:A.
Last Name:Masten
Suffix:
RePEc Short-ID:pma2923
[This author has chosen not to make the email address public]
http://www.mattmasten.com
Terminal Degree:2013 Department of Economics; Northwestern University (from RePEc Genealogy)

Affiliation

Department of Economics
Duke University

Durham, North Carolina (United States)
http://www.econ.duke.edu/
RePEc:edi:dedukus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Matthew A. Masten & Alexandre Poirier, 2022. "The Effect of Omitted Variables on the Sign of Regression Coefficients," Papers 2208.00552, arXiv.org, revised Feb 2023.
  2. Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2022. "Assessing Omitted Variable Bias when the Controls are Endogenous," Papers 2206.02303, arXiv.org, revised Jul 2023.
  3. Matthew A. Masten & Alexandre Poirier, 2022. "Choosing Exogeneity Assumptions in Potential Outcome Models," Papers 2205.02288, arXiv.org.
  4. David A. Benson & Matthew A. Masten & Alexander Torgovitsky, 2020. "ivcrc: An Instrumental Variables Estimator for the Correlated Random Coefficients Model," Finance and Economics Discussion Series 2020-046r1, Board of Governors of the Federal Reserve System (U.S.), revised 04 Apr 2022.
  5. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2020. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Papers 2012.15716, arXiv.org.
  6. Matthew Masten & Alexandre Poirier, 2019. "tesensitivity: A Stata Package for Assessing the Unconfoundedness Assumption," 2019 Stata Conference 51, Stata Users Group.
  7. Matthew A. Masten & Alexandre Poirier, 2018. "Salvaging Falsified Instrumental Variable Models," Papers 1812.11598, arXiv.org, revised Jan 2020.
  8. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
  9. Matthew A. Masten & Alexandre Poirier, 2017. "Inference on Breakdown Frontiers," Papers 1705.04765, arXiv.org, revised Feb 2019.
  10. Matthew A. Masten & Alexandre Poirier, 2017. "Identification of Treatment Effects under Conditional Partial Independence," Papers 1707.09563, arXiv.org.
  11. Matthew Masten & Alexandre Poirier, 2016. "Partial independence in nonseparable models," CeMMAP working papers 26/16, Institute for Fiscal Studies.
  12. Joachim Freyberger & Matthew Masten, 2016. "Compactness of infinite dimensional parameter spaces," CeMMAP working papers 01/16, Institute for Fiscal Studies.
  13. Matthew Masten & Alexander Torgovitsky, 2014. "Instrumental variables estimation of a generalized correlated random coefficients model," CeMMAP working papers 02/14, Institute for Fiscal Studies.
  14. Matthew Masten, 2014. "Random coefficients on endogenous variables in simultaneous equations models," CeMMAP working papers 01/14, Institute for Fiscal Studies.

Articles

  1. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
  2. Matthew A. Masten, 2023. "Minimax-regret treatment rules with many treatments," The Japanese Economic Review, Springer, vol. 74(4), pages 501-537, October.
  3. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
  4. David Benson & Matthew A. Masten & Alexander Torgovitsky, 2022. "ivcrc: An instrumental-variables estimator for the correlated random-coefficients model," Stata Journal, StataCorp LP, vol. 22(3), pages 469-495, September.
  5. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
  6. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
  7. Joachim Freyberger & Matthew A. Masten, 2019. "A practical guide to compact infinite dimensional parameter spaces," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 979-1006, October.
  8. Matthew A. Masten & Alexandre Poirier, 2018. "Identification of Treatment Effects Under Conditional Partial Independence," Econometrica, Econometric Society, vol. 86(1), pages 317-351, January.
  9. Matthew A Masten, 2018. "Random Coefficients on Endogenous Variables in Simultaneous Equations Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 1193-1250.
  10. Matthew A. Masten & Alexander Torgovitsky, 2016. "Identification of Instrumental Variable Correlated Random Coefficients Models," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 1001-1005, December.
  11. Chicu, Mark & Masten, Matthew A., 2013. "A specification test for discrete choice models," Economics Letters, Elsevier, vol. 121(2), pages 336-339.
  12. Jose Miguel Abito & Katarina Borovickova & Hays Golden & Jacob Goldin & Matthew A. Masten & Miguel Morin & Alexandre Poirier & Vincent Pons & Israel Romem & Tyler Williams & Chamna Yoon, 2011. "How Should the Graduate Economics Core be Changed?," The Journal of Economic Education, Taylor & Francis Journals, vol. 42(4), pages 414-417, October.

Software components

  1. Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2022. "REGSENSITIVITY: Stata module for regression sensitivity analysis," Statistical Software Components S459088, Boston College Department of Economics, revised 07 Aug 2022.
  2. Linqi Zhang & Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2021. "TESENSITIVITY: Stata module for assessing sensitivity to the unconfoundedness assumption," Statistical Software Components S458896, Boston College Department of Economics.
  3. David Benson & Matt Masten & Alexander Torgovitsky, 2020. "IVCRC: Stata module to implement the instrumental variables correlated random coefficients estimator," Statistical Software Components S458797, Boston College Department of Economics, revised 06 Dec 2022.

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. Matthew A Masten, 2018. "Random Coefficients on Endogenous Variables in Simultaneous Equations Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 1193-1250.

    Mentioned in:

    1. Random Coefficients on Endogenous Variables in Simultaneous Equations Models (REStud 2018) in ReplicationWiki ()

Working papers

  1. Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2022. "Assessing Omitted Variable Bias when the Controls are Endogenous," Papers 2206.02303, arXiv.org, revised Jul 2023.

    Cited by:

    1. Montagnoli, Alberto & Taylor, Karl, 2024. "Who Cares about Investing Responsibly? Attitudes and Financial Decisions," IZA Discussion Papers 16952, Institute of Labor Economics (IZA).
    2. João Martins & Linda Veiga & Bruno Fernandes, 2023. "Are electronic government innovations helpful to deter corruption? Evidence from across the world," Economics and Politics, Wiley Blackwell, vol. 35(3), pages 1177-1203, November.
    3. Jeremy Clark & Abel François & Olivier Gergaud, 2024. "Social capital, social heterogeneity, and electoral turnout," Kyklos, Wiley Blackwell, vol. 77(4), pages 1142-1168, November.
    4. Kaicheng Chen & Kyoo il Kim, 2024. "Identification of Nonseparable Models with Endogenous Control Variables," Papers 2401.14395, arXiv.org.

  2. David A. Benson & Matthew A. Masten & Alexander Torgovitsky, 2020. "ivcrc: An Instrumental Variables Estimator for the Correlated Random Coefficients Model," Finance and Economics Discussion Series 2020-046r1, Board of Governors of the Federal Reserve System (U.S.), revised 04 Apr 2022.

    Cited by:

    1. Paul Carrillo & Dave Donaldson & Dina Pomeranz & Monica Singhal, 2023. "Misallocation in Firm Production: A Nonparametric Analysis Using Procurement Lotteries," NBER Working Papers 31311, National Bureau of Economic Research, Inc.

  3. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2020. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Papers 2012.15716, arXiv.org.

    Cited by:

    1. Jacob Dorn & Kevin Guo, 2021. "Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing," Papers 2102.04543, arXiv.org, revised Aug 2023.
    2. Miklin Nikolai & Gachechiladze Mariami & Moreno George & Chaves Rafael, 2022. "Causal inference with imperfect instrumental variables," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 45-63, January.
    3. Santiago Acerenza & Vitor Possebom & Pedro H. C. Sant'Anna, 2023. "Was Javert right to be suspicious? Unpacking treatment effect heterogeneity of alternative sentences on time-to-recidivism in Brazil," Papers 2311.13969, arXiv.org, revised May 2024.
    4. Montagnoli, Alberto & Taylor, Karl, 2024. "Who Cares about Investing Responsibly? Attitudes and Financial Decisions," IZA Discussion Papers 16952, Institute of Labor Economics (IZA).
    5. Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2022. "Assessing Omitted Variable Bias when the Controls are Endogenous," Papers 2206.02303, arXiv.org, revised Jul 2023.
    6. Adamecz, Anna & Lovász, Anna & Vujic, Suncica, 2024. "Beyond the Degree: Fertility Outcomes of 'First in Family' Graduates," IZA Discussion Papers 17216, Institute of Labor Economics (IZA).
    7. Tabe-Ojong, Martin Paul Jr. & Nshakira-Rukundo, Emmanuel, 2021. "Religiosity and parental educational aspirations for children in Kenya," World Development Perspectives, Elsevier, vol. 23(C).

  4. Matthew A. Masten & Alexandre Poirier, 2018. "Salvaging Falsified Instrumental Variable Models," Papers 1812.11598, arXiv.org, revised Jan 2020.

    Cited by:

    1. Han, Sukjin & Yang, Shenshen, 2024. "A computational approach to identification of treatment effects for policy evaluation," Journal of Econometrics, Elsevier, vol. 240(1).
    2. Strulik, Holger, 2021. "Testing Unified Growth Theory: Technological Progress and the Child Quantity--Quality Trade-off," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242329, Verein für Socialpolitik / German Economic Association.
    3. Kyunghoon Ban & D'esir'e K'edagni, 2021. "Nonparametric Bounds on Treatment Effects with Imperfect Instruments," Papers 2109.14785, arXiv.org.
    4. Lixiong Li & D'esir'e K'edagni & Ismael Mourifi'e, 2020. "Discordant Relaxations of Misspecified Models," Papers 2012.11679, arXiv.org, revised Apr 2024.
    5. Timothy B. Armstrong & Michal Kolesár, 2020. "Sensitivity Analysis using Approximate Moment Condition Models," Working Papers 2020-28, Princeton University. Economics Department..
    6. Moyu Liao, 2020. "Estimating Economic Models with Testable Assumptions: Theory and Applications," Papers 2002.10415, arXiv.org, revised Mar 2022.
    7. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2021. "Rationalizing Rational Expectations: Characterizations and Tests," TSE Working Papers 21-1211, Toulouse School of Economics (TSE).
    8. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    9. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Nicolas Apfel & Frank Windmeijer, 2022. "The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exclusion or Conditional Exogeneity Restriction," Papers 2212.04814, arXiv.org, revised Apr 2024.
    11. Charles F. Manski, 2019. "Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald," Papers 1912.08726, arXiv.org, revised Feb 2021.
    12. Nicolas Apfel & Julia Hatamyar & Martin Huber & Jannis Kueck, 2024. "Learning control variables and instruments for causal analysis in observational data," Papers 2407.04448, arXiv.org.
    13. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    14. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Adnan M.S. Fakir & Tushar Bharati, 2022. "Health Costs of a "Healthy Democracy": The Impact of Peaceful Political Protests on Healthcare Utilization," Working Paper Series 0522, Department of Economics, University of Sussex Business School.
    16. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.

  5. Matthew A. Masten & Alexandre Poirier, 2017. "Inference on Breakdown Frontiers," Papers 1705.04765, arXiv.org, revised Feb 2019.

    Cited by:

    1. Brantly Callaway, 2020. "Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement," Papers 2008.08117, arXiv.org.
    2. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    3. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    4. Firpo, Sergio & Galvao, Antonio F. & Parker, Thomas, 2023. "Uniform inference for value functions," Journal of Econometrics, Elsevier, vol. 235(2), pages 1680-1699.
    5. Kamat, Vishal, 2024. "Identifying the effects of a program offer with an application to Head Start," Journal of Econometrics, Elsevier, vol. 240(1).
    6. St'ephane Bonhomme & Martin Weidner, 2018. "Minimizing Sensitivity to Model Misspecification," Papers 1807.02161, arXiv.org, revised Oct 2021.
    7. 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.
    8. Sergio Firpo & Antonio F. Galvao & Martyna Kobus & Thomas Parker & Pedro Rosa-Dias, 2020. "Loss aversion and the welfare ranking of policy interventions," Papers 2004.08468, arXiv.org, revised Sep 2023.
    9. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2020. "Uncertain Identification," CeMMAP working papers CWP33/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Candelaria, Luis E. & Ura, Takuya, 2023. "Identification and inference of network formation games with misclassified links," Journal of Econometrics, Elsevier, vol. 235(2), pages 862-891.
    11. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    12. Hiroaki Kaido & Jiaxuan Li & Marc Rysman, 2018. "Moment Inequalities in the Context of Simulated and Predicted Variables," Papers 1804.03674, arXiv.org.
    13. Harsh Parikh & Marco Morucci & Vittorio Orlandi & Sudeepa Roy & Cynthia Rudin & Alexander Volfovsky, 2023. "A Double Machine Learning Approach to Combining Experimental and Observational Data," Papers 2307.01449, arXiv.org, revised Apr 2024.
    14. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    15. Vitor Possebom, 2021. "Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification," Papers 2106.00536, arXiv.org, revised Jul 2023.
    16. Daniel Ober-Reynolds, 2024. "Robustness to Missing Data: Breakdown Point Analysis," Papers 2406.06804, arXiv.org.
    17. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    18. 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.
    19. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    20. Tamara Broderick & Ryan Giordano & Rachael Meager, 2020. "An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?," Papers 2011.14999, arXiv.org, revised Jul 2023.
    21. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org, revised Aug 2024.
    22. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

  6. Matthew A. Masten & Alexandre Poirier, 2017. "Identification of Treatment Effects under Conditional Partial Independence," Papers 1707.09563, arXiv.org.

    Cited by:

    1. Tenglong Li & Kenneth A. Frank, 2020. "The probability of a robust inference for internal validity and its applications in regression models," Papers 2005.12784, arXiv.org.
    2. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
    3. Arkadiusz Szydłowski, 2019. "Endogenous censoring in the mixed proportional hazard model with an application to optimal unemployment insurance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1086-1101, November.
    4. Nathan Canen & Kyungchul Song, 2019. "Counterfactual Analysis under Partial Identification Using Locally Robust Refinement," Papers 1906.00003, arXiv.org, revised Jan 2021.
    5. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    6. Matthew A. Masten & Alexandre Poirier, 2022. "The Effect of Omitted Variables on the Sign of Regression Coefficients," Papers 2208.00552, arXiv.org, revised Feb 2023.
    7. Tenglong Li & Kenneth A. Frank & Mingming Chen, 2024. "A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study," Mathematics, MDPI, vol. 12(3), pages 1-14, January.
    8. Paul Diegert & Matthew A. Masten & Alexandre Poirier, 2022. "Assessing Omitted Variable Bias when the Controls are Endogenous," Papers 2206.02303, arXiv.org, revised Jul 2023.
    9. Sakaue, Katsuki & Wokadala, James, 2022. "Effects of including refugees in local government schools on pupils’ learning achievement: Evidence from West Nile, Uganda," International Journal of Educational Development, Elsevier, vol. 90(C).
    10. Yiwei Sun, 2023. "Extrapolating Away from the Cutoff in Regression Discontinuity Designs," Papers 2311.18136, arXiv.org.
    11. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    12. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    13. Christophe Bruneel-Zupanc, 2023. "Don't (fully) exclude me, it's not necessary! Identification with semi-IVs," Papers 2303.12667, arXiv.org, revised Jul 2023.
    14. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.

  7. Matthew Masten & Alexandre Poirier, 2016. "Partial independence in nonseparable models," CeMMAP working papers 26/16, Institute for Fiscal Studies.

    Cited by:

    1. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    2. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    3. Matthew Masten & Alexandre Poirier, 2017. "Inference on breakdown frontiers," CeMMAP working papers 20/17, Institute for Fiscal Studies.
    4. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    5. Alexander Torgovitsky, 2019. "Partial identification by extending subdistributions," Quantitative Economics, Econometric Society, vol. 10(1), pages 105-144, January.
    6. Matthew A. Masten & Alexandre Poirier, 2017. "Identification of Treatment Effects under Conditional Partial Independence," Papers 1707.09563, arXiv.org.

  8. Joachim Freyberger & Matthew Masten, 2016. "Compactness of infinite dimensional parameter spaces," CeMMAP working papers 01/16, Institute for Fiscal Studies.

    Cited by:

    1. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    2. Sungwon Lee & Joon H. Ro, 2020. "Nonparametric Tests for Conditional Quantile Independence with Duration Outcomes," Working Papers 2013, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    3. Laurent Davezies & Xavier D'Haultfoeuille & Yannick Guyonvarch, 2018. "Asymptotic results under multiway clustering," Papers 1807.07925, arXiv.org, revised Aug 2018.

  9. Matthew Masten & Alexander Torgovitsky, 2014. "Instrumental variables estimation of a generalized correlated random coefficients model," CeMMAP working papers 02/14, Institute for Fiscal Studies.

    Cited by:

    1. Fernandez-Val , Ivan & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules," Working Papers in Economics 716, University of Gothenburg, Department of Economics.
    2. Stefan Hoderlein & Hajo Holzmann & Alexander Meister, 2015. "The triangular model with random coefficients," CeMMAP working papers CWP33/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 225, Courant Research Centre PEG.
    4. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis & Peracchi, Franco, 2022. "Selection and the Distribution of Female Hourly Wages in the U.S," IZA Discussion Papers 15028, Institute of Labor Economics (IZA).
    5. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    6. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules: An Application to Wage Decompositions," IZA Discussion Papers 11294, Institute of Labor Economics (IZA).
    7. 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.
    8. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis, 2018. "Decomposing Real Wage Changes in the United States," IZA Discussion Papers 12044, Institute of Labor Economics (IZA).

  10. Matthew Masten, 2014. "Random coefficients on endogenous variables in simultaneous equations models," CeMMAP working papers 01/14, Institute for Fiscal Studies.

    Cited by:

    1. Dunker, Fabian & Hoderlein, Stefan & Kaido, Hiroaki, 2014. "Nonparametric Identification of Endogenous and Heterogeneous Aggregate Demand Models: Complements, Bundles and the Market Level," Economics Series 307, Institute for Advanced Studies.
    2. Christoph Breunig, 2018. "Varying Random Coefficient Models," Papers 1804.03110, arXiv.org, revised Aug 2020.
    3. Zhou, Yiwei & Wang, Xiaokun & Holguín-Veras, José, 2016. "Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 440-455.
    4. Stefan Hoderlein & Hajo Holzmann & Alexander Meister, 2015. "The triangular model with random coefficients," CeMMAP working papers CWP33/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 225, Courant Research Centre PEG.
    6. 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.
    7. Arthur Lewbel & Krishna Pendakur, 2017. "Unobserved Preference Heterogeneity in Demand Using Generalized Random Coefficients," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1100-1148.
    8. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    9. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Feb 2024.
    10. 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.
    11. Rokhaya Dieye & Bernard Fortin, 2017. "Gender Peer Effects Heterogeneity in Obesity," CIRANO Working Papers 2017s-03, CIRANO.
    12. Breunig, Christoph & Hoderlein, Stefan, 2018. "Specification Testing in Random Coefficient Models," Rationality and Competition Discussion Paper Series 77, CRC TRR 190 Rationality and Competition.
    13. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2019. "Peer Effects in Networks: a Survey," Working Papers halshs-02440709, HAL.
    14. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Christophe Gaillac & Eric Gautier, 2021. "Nonparametric classes for identification in random coefficients models when regressors have limited variation," Working Papers hal-03231392, HAL.
    16. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    17. Sida Peng, 2019. "Heterogeneous Endogenous Effects in Networks," Papers 1908.00663, arXiv.org.
    18. Steven T. Berry & Philip A. Haile, 2015. "Identification of Nonparametric Simultaneous Equations Models with a Residual Index Structure," Cowles Foundation Discussion Papers 2008, Cowles Foundation for Research in Economics, Yale University.
    19. Konstantinidi, Antri & Kourtellos, Andros & Sun, Yiguo, 2023. "Social threshold regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2057-2081.
    20. Fabian Dunker & Stefan Hoderlein & Hiroaki Kaido, 2013. "Random coefficients in static games of complete information," CeMMAP working papers 12/13, Institute for Fiscal Studies.
    21. Jeremy T. Fox, 2021. "A Note on Nonparametric Identification of Distributions of Random Coefficients in Multinomial Choice Models," Annals of Economics and Statistics, GENES, issue 142, pages 305-310.
    22. Breunig, Christoph, 2021. "Varying random coefficient models," Journal of Econometrics, Elsevier, vol. 221(2), pages 381-408.
    23. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    24. Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.
    25. Juan Carlos Escanciano, 2020. "Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity," Papers 2005.08611, arXiv.org.
    26. Giovanni Compiani & Yuichi Kitamura, 2016. "Using mixtures in econometric models: a brief review and some new results," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 95-127, October.
    27. Ming Li, 2021. "A Time-Varying Endogenous Random Coefficient Model with an Application to Production Functions," Papers 2110.00982, arXiv.org.
    28. Gao, Z. & Pesaran, M. H., 2022. "Identification and Estimation of Categorical Random Coeficient Models," Cambridge Working Papers in Economics 2228, Faculty of Economics, University of Cambridge.
    29. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    30. Samuele Centorrino & Aman Ullah & Jing Xue, 2019. "Semiparametric Estimation of Correlated Random Coefficient Models without Instrumental Variables," Papers 1911.06857, arXiv.org.
    31. Christoph Breunig & Stefan Hoderlein, 2016. "Nonparametric Specification Testing in Random Parameter Models," Boston College Working Papers in Economics 897, Boston College Department of Economics.
    32. Songnian Chen & Shakeeb Khan & Xun Tang, 2022. "Endogeneity in Weakly Separable Models without Monotonicity," Papers 2208.05047, arXiv.org.
    33. Roy Allen & John Rehbeck, 2021. "Obstacles to Redistribution Through Markets and One Solution," Papers 2111.09910, arXiv.org.
    34. 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.
    35. Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
    36. Chen, Songnian & Khan, Shakeeb & Tang, Xun, 2024. "Endogeneity in weakly separable models without monotonicity," Journal of Econometrics, Elsevier, vol. 238(1).
    37. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
    38. Dunker, Fabian & Hoderlein, Stefan & Kaido, Hiroaki & Sherman, Robert, 2018. "Nonparametric identification of the distribution of random coefficients in binary response static games of complete information," Journal of Econometrics, Elsevier, vol. 206(1), pages 83-102.

Articles

  1. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2024. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 1-13, January.
    See citations under working paper version above.
  2. David Benson & Matthew A. Masten & Alexander Torgovitsky, 2022. "ivcrc: An instrumental-variables estimator for the correlated random-coefficients model," Stata Journal, StataCorp LP, vol. 22(3), pages 469-495, September.
    See citations under working paper version above.
  3. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    See citations under working paper version above.
  4. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
    See citations under working paper version above.
  5. Joachim Freyberger & Matthew A. Masten, 2019. "A practical guide to compact infinite dimensional parameter spaces," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 979-1006, October.

    Cited by:

    1. Federico Zincenko, 2019. "Testing for Risk Aversion in First-Price Sealed-Bid Auctions," Working Paper 6641, Department of Economics, University of Pittsburgh.
    2. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Sukjin Han & Sungwon Lee, 2018. "Estimation in a Generalization of Bivariate Probit Models with Dummy Endogenous Regressors," Papers 1808.05792, arXiv.org, revised Mar 2019.
    4. Isaac Loh, 2024. "Inference under partial identification with minimax test statistics," Papers 2401.13057, arXiv.org, revised Apr 2024.
    5. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    6. Ben Deaner, 2019. "Nonparametric Instrumental Variables Estimation Under Misspecification," Papers 1901.01241, arXiv.org, revised Dec 2022.
    7. An, Yonghong & Hong, Shengjie & Zhang, Daiqiang, 2023. "A structural analysis of simple contracts," Journal of Econometrics, Elsevier, vol. 236(2).
    8. Fan, Yanqin & Shi, Xuetao & Tao, Jing, 2023. "Partial identification and inference in moment models with incomplete data," Journal of Econometrics, Elsevier, vol. 235(2), pages 418-443.

  6. Matthew A. Masten & Alexandre Poirier, 2018. "Identification of Treatment Effects Under Conditional Partial Independence," Econometrica, Econometric Society, vol. 86(1), pages 317-351, January.
    See citations under working paper version above.
  7. Matthew A Masten, 2018. "Random Coefficients on Endogenous Variables in Simultaneous Equations Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 1193-1250.
    See citations under working paper version above.
  8. Matthew A. Masten & Alexander Torgovitsky, 2016. "Identification of Instrumental Variable Correlated Random Coefficients Models," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 1001-1005, December.

    Cited by:

    1. Douglas Gollin & Christopher Udry, 2019. "Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture," CSAE Working Paper Series 2019-01, Centre for the Study of African Economies, University of Oxford.
    2. Whitney Newey & Sami Stouli, 2018. "Control Variables, Discrete Instruments, and Identification of Structural Functions," Bristol Economics Discussion Papers 18/702, School of Economics, University of Bristol, UK.
    3. Louise Laage, 2020. "A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity," Papers 2003.09367, arXiv.org, revised Nov 2022.
    4. Paul Carrillo & Dave Donaldson & Dina Pomeranz & Monica Singhal, 2023. "Misallocation in Firm Production: A Nonparametric Analysis Using Procurement Lotteries," NBER Working Papers 31311, National Bureau of Economic Research, Inc.
    5. Dionissi Aliprantis & Francisca G.-C. Richter, 2020. "Evidence of Neighborhood Effects from Moving to Opportunity: Lates of Neighborhood Quality," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 633-647, October.
    6. Carolina Caetano & Gregorio Caetano & Juan Carlos Escanciano, 2023. "Regression discontinuity design with multivalued treatments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 840-856, September.
    7. Juan Carlos Escanciano, 2020. "Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity," Papers 2005.08611, arXiv.org.
    8. Breen, Richard & Ermisch, John, 2021. "Instrumental Variable Estimation in Demographic Studies: The LATE interpretation of the IV estimator with heterogenous effects," SocArXiv vx9m7, Center for Open Science.
    9. Bora Kim, 2020. "Analysis of Randomized Experiments with Network Interference and Noncompliance," Papers 2012.13710, arXiv.org.
    10. Francis J. DiTraglia & Camilo Garcia-Jimeno & Rossa O'Keeffe-O'Donovan & Alejandro Sanchez-Becerra, 2020. "Identifying Causal Effects in Experiments with Spillovers and Non-compliance," Papers 2011.07051, arXiv.org, revised Jan 2023.
    11. Zhu, Xun & Jin, Zequn, 2023. "Some identification results in a correlated random coefficients sample selection model," Economics Letters, Elsevier, vol. 233(C).
    12. Samuele Centorrino & Aman Ullah & Jing Xue, 2019. "Semiparametric Estimation of Correlated Random Coefficient Models without Instrumental Variables," Papers 1911.06857, arXiv.org.
    13. 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).
    14. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    15. DiTraglia, Francis J. & García-Jimeno, Camilo & O’Keeffe-O’Donovan, Rossa & Sánchez-Becerra, Alejandro, 2023. "Identifying causal effects in experiments with spillovers and non-compliance," Journal of Econometrics, Elsevier, vol. 235(2), pages 1589-1624.
    16. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.

  9. Jose Miguel Abito & Katarina Borovickova & Hays Golden & Jacob Goldin & Matthew A. Masten & Miguel Morin & Alexandre Poirier & Vincent Pons & Israel Romem & Tyler Williams & Chamna Yoon, 2011. "How Should the Graduate Economics Core be Changed?," The Journal of Economic Education, Taylor & Francis Journals, vol. 42(4), pages 414-417, October.

    Cited by:

Software components

    Sorry, no citations of software components recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Rankings

This author is among the top 5% authors according to these criteria:
  1. Number of Journal Pages, Weighted by Recursive Impact Factor
  2. Number of Journal Pages, Weighted by Number of Authors and Simple Impact Factors
  3. Number of Journal Pages, Weighted by Number of Authors and Recursive Impact Factors

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 12 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (11) 2014-02-21 2016-06-25 2016-07-16 2017-11-05 2018-05-07 2019-01-14 2020-07-27 2021-02-01 2022-06-13 2022-07-18 2022-09-05. Author is listed
  2. NEP-DCM: Discrete Choice Models (5) 2016-07-16 2017-11-05 2019-08-26 2020-07-27 2021-02-01. Author is listed
  3. NEP-DEM: Demographic Economics (1) 2022-07-18
  4. NEP-GER: German Papers (1) 2016-07-16
  5. NEP-NET: Network Economics (1) 2016-06-25
  6. NEP-ORE: Operations Research (1) 2020-07-27
  7. NEP-RMG: Risk Management (1) 2018-05-07

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Matthew A. Masten should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.