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Local regression distribution estimators

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  • Cattaneo, Matias D
  • Jansson, Michael
  • Ma, Xinwei

Abstract

This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on “redundant” regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local L2 estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2021. "Local regression distribution estimators," Department of Economics, Working Paper Series qt7416d3x8, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    • Handle: RePEc:cdl:econwp:qt7416d3x8
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    Cited by:

    1. Jorge Alé-Chilet & Sarah Moshary, 2022. "Beyond Consumer Switching: Supply Responses to Food Packaging and Advertising Regulations," Marketing Science, INFORMS, vol. 41(2), pages 243-270, March.
    2. Filmer, Deon, 2023. "Long-lived consequences of rapid scale-up? The case of free primary education in six Sub-Saharan African countries," International Journal of Educational Development, Elsevier, vol. 102(C).
    3. Masaki Takahashi, 2023. "Insurance coverage, long-term care utilization, and health outcomes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(8), pages 1383-1397, November.
    4. Günther, Jutta (Ed.) & Wedemeier, Jan (Ed.), 2020. "Struktureller Umbruch durch COVID-19: Implikationen für die Innovationspolitik im Land Bremen," HWWI Policy Papers 128, Hamburg Institute of International Economics (HWWI).
    5. Albanese, Andrea & Fallucchi, Francesco & Verheyden, Bertrand, 2021. "Can a supranational medicines agency restore trust after vaccine suspensions? The case of Vaxzevria," GLO Discussion Paper Series 878, Global Labor Organization (GLO).
    6. Nakamura, Ryota & Albanese, Andrea & Coombes, Emma & Suhrcke, Marc, 2021. "Do Economic Incentives Promote Physical Activity? Evidence from the London Congestion Charge," GLO Discussion Paper Series 1006, Global Labor Organization (GLO).
    7. Collin, Matthew & Talbot, Theodore, 2023. "Are age-of-marriage laws enforced? Evidence from developing countries," Journal of Development Economics, Elsevier, vol. 160(C).
    8. Lim, Yuree, 2025. "Disclosure in corporate pension plans using a regression discontinuity design," The Quarterly Review of Economics and Finance, Elsevier, vol. 99(C).
    9. John M. Barrios & Thomas G. Wollmann, 2024. "A New Era of Midnight Mergers: Antitrust Risk and Investor Disclosures," American Economic Journal: Microeconomics, American Economic Association, vol. 16(4), pages 77-111, November.
    10. Aparajita Dasgupta & Devvrat Raghav, 2024. "Rural Roads, Climate Change, and the Dynamics of Structural Transformation: Evidence from India," Working Papers 122, Ashoka University, Department of Economics.

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