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Local Polynomial Order in Regression Discontinuity Designs. (2014). Weber, Andrea ; Pei, Zhuan ; Card, David ; Lee, David S..
In: Working Papers.
RePEc:brd:wpaper:81.

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Cited: 7

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Cites: 14

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Cocites: 50

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Citations received by this document

  1. Short?term effect of retirement on health: Evidence from nonparametric fuzzy regression discontinuity design. (2023). Oguzoglu, Umut ; Ebeid, Mohamed.
    In: Health Economics.
    RePEc:wly:hlthec:v:32:y:2023:i:6:p:1323-1343.

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  2. Do Cellphone Bans Save Lives? Evidence From Handheld Laws on Traffic Fatalities. (2022). Dorilas, Ernest ; Wright, Nicholas A.
    In: Journal of Health Economics.
    RePEc:eee:jhecon:v:85:y:2022:i:c:s0167629622000789.

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  3. Higher education and fertility: Evidence from reforms in Greece. (2020). Kountouris, Yiannis.
    In: Economics of Education Review.
    RePEc:eee:ecoedu:v:79:y:2020:i:c:s0272775720305458.

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  4. When does regression discontinuity design work? Evidence from random election outcomes. (2018). Tukiainen, Janne ; Saarimaa, Tuukka ; Meriläinen, Jaakko ; Toivanen, Otto ; Merilainen, Jaakko ; Hyytinen, Ari.
    In: Quantitative Economics.
    RePEc:wly:quante:v:9:y:2018:i:2:p:1019-1051.

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  5. The Effects of UI Benefits on Unemployment and Subsequent Outcomes: Evidence from a Kinked Benefit Rule. (2017). Kyyrä, Tomi ; Kyyra, Tomi ; Pesola, Hanna.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp10484.

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  6. The effects of UI benefits on unemployment and subsequent outcomes: Evidence from a kinked benefit rule. (2016). Kyyrä, Tomi ; Pesola, Hanna ; Kyyra, Tomi.
    In: Working Papers.
    RePEc:fer:wpaper:82.

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  7. Inference on Causal Effects in a Generalized Regression Kink Design. (2015). Weber, Andrea ; Pei, Zhuan ; Card, David ; Lee, David S..
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp8757.

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References

References cited by this document

  1. Card, David, David S. Lee, Zhuan Pei, and Andrea Weber, “Nonlinear Policy Rules and the Identification and Estimation of Causal Effects in a Generalized Regression Kink Design,” NBER Working Paper 18564 November 2012.

  2. Cheng, Ming-Yen, Jianqing Fan, and J. S. Marron, “On automatic boundary corrections,” The Annals of Statistics, 08 1997, 25 (4), 1691–1708.
    Paper not yet in RePEc: Add citation now
  3. Fan, Jianqing and Irene Gijbels, Local Polynomial Modelling and Its Applications, Chapman and Hall, 1996.
    Paper not yet in RePEc: Add citation now

  4. Gelman, Andrew and Guido Imbens, “Why High-order Polynomials Should Not Be Used in Regression Discontinuity Designs,” NBER Working Paper 20405 August 2014.

  5. Hahn, Jinyong, Petra Todd, and Wilbert Van der Klaauw, “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design,” Econometrica, 2001, 69 (1), 201–209.

  6. Imbens, Guido and Karthik Kalyanaraman, “Optimal Bandwidth Choice for the Regression Discontinuity Estimator.,” Review of Economic Studies, 2012, 79 (3), 933 – 959.

  7. Lee, David S., “Randomized Experiments from Non-random Selection in U.S. House Elections,” Journal of Econometrics, February 2008, 142 (2), 675–697.

  8. Ludwig, J. and D. Miller, “Does Head Start Improve Children’s Life Chances? Evidence from a Regression Discontinuity Design,” Quarterly Journal of Economics, 2007, 122(1), 159–208.

  9. Nielsen, Helena Skyt, Torben Sørensen, and Christopher R. Taber, “Estimating the Effect of Student Aid on College Enrollment: Evidence from a Government Grant Policy Reform,” American Economic Journal: Economic Policy, 2010, 2 (2), 185–215.

  10. Table A.10: MSE of the Bias-Corrected Estimator and Coverage Rates of 95% Robust CI from Simulations Using the CCT Bandwidth without regularization: Ludwig-Miller DGP MSE, Coverage Rates and CCT Bandwidths for the Uniform Kernel MSE×1000 Coverage Rates ˆ hCCT,noreg ˆ
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  11. Table A.9: MSE of the Bias-Corrected Estimator and Coverage Rates of 95% Robust CI from Simulations Using the CCT Bandwidths without Regularization: Lee DGP MSE, Coverage Rates and CCT Bandwidths w/o Regularization for the Uniform Kernel MSE×1000 Coverage Rates ˆ hCCT,noreg ˆ
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  12. The reported AMSE measure and CCT bandwidth, \ AMSE × 1000 and ˆ hCCT are the averages over repeated simulation samples. Table 12: Estimated AMSE from Simulations Using the CCT Bandwidths: Ludwig-Miller DGP Estimated AMSE, Preferred p and CCT Bandwidths for the Uniform Kernel \
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  13. The reported AMSE measure and CCT bandwidths, \ AMSE × 1000 and ˆ hCCT,noreg, are the averages over repeated simulation samples. Table 14: Estimated AMSE from Simulations Using the CCT Bandwidths: Ludwig-Miller DGP Estimated AMSE, Preferred p and CCT Bandwidths w/o Regularization for the Uniform Kernel \
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  14. The reported AMSE measure and CCT bandwidths, \ AMSE×1000 and ˆ hCCT , are the averages over repeated simulation samples. Table 13: Estimated AMSE from Simulations Using the CCT Bandwidths without Regularization: Lee DGP Estimated AMSE, Preferred p and CCT Bandwidths w/o Regularization for the Uniform Kernel \
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