Stata 新命令：多断点 RDD 分析 - rdmc

发布时间：2020-02-19 阅读 5758
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简介

在连享会此前发布的推文 「RDD 最新进展：多断点 RDD、多分配变量 RDD」 中，我们提到了 RDD 领域的一个重要的扩展模型：多断点 RDD 模型。今天将为大家介绍这个模型的 Stata 实现命令。ps，令刚刚写好，目前我们还在做实操方面的测试，稍后几天会把完整的实操过程，写成一篇更详细的推文分享给大家。

作者在其项目主页中不但提供了完整的 Stata 命令包，还提供了范例数据和实操所用的 dofiles。

Cattaneo, M. D., R. Titiunik, G. Vazquezbare, 2020, Analysis of regression discontinuity designs with multiple cutoffs or multiple scores, Working Paper, [PDF-Stata实操]

特别说明： 文中包含的链接在微信中无法生效。请点击本文底部左下角的【阅读原文】。

Abstract. We introduce the Stata (and R) package rdmulti, which includes three commands (rdmc, rdmcplot, rdms) for analyzing Regression Discontinuity (RD) designs with multiple cutoffs or multiple scores.

  • The command rdmc applies to non-cummulative and cummulative multi-cutoff RD settings. It calculates pooled and cutoff-specific RD treatment effects, and provides robust bias-corrected inference procedures. Post estimation and inference is allowed.
  • The command rdmcplot offers RD plots for multi-cutoff settings.
  • The command rdms concerns multi-score settings, covering in particular cumulative cutoffs and two running variables contexts. It also calculates pooled and cutoff-specific RD treatment effects, provides robust bias-corrected inference procedures, and allows for post-estimation estimation and inference.

These commands employ the Stata (and R) package rdrobust for plotting, estimation, and inference. Companion R functions with the same syntax and capabilities are provided.

Keywords: regression discontinuity designs, multiple cutoffs, multiple scores,local polynomial methods.

下载和安装

. ssc install github, replace  //安装 github 命令, 若已有，可忽略此步骤
. github install iphone7725/rdmulti
. help rdmc

如果上述方法无法凑效，可以直接访问作者的 Github 主页，手动下载相关的命令，并存放在 plus 文件夹里。有关 plus 文件夹的放置和设定，请参考 「Stata: 外部命令的搜索、安装与使用」，以及 https://gitee.com/arlionn/StataPlus 。

Stata 实操演示

 

参考文献：

特别说明： 文中包含的链接在微信中无法生效。请点击本文底部左下角的【阅读原文】。

  • Cattaneo, M. D., R. Titiunik, G. Vazquezbare, 2020, Analysis of regression discontinuity designs with multiple cutoffs or multiple scores, Working Paper, [PDF-Stata实操]
  • Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2018. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Journal of the American Statistical Association 113(522): 767–779.
  • ———. 2019a. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. arXiv:1808.01398 .
  • ———. 2019b. Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs. Econometrics Journal, forthcoming .
  • Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2017. rdrobust: Software for Regression Discontinuity Designs. Stata Journal 17(2): 372–404.
  • ———. 2019c. Regression Discontinuity Designs Using Covariates. Review of Economics and Statistics 101(3): 442–451.
  • Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014a. Robust Data-Driven Inference in the Regression-Discontinuity Design. Stata Journal 14(4): 909–946.
  • ———. 2014b. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295–2326.
  • ———. 2015a. Optimal Data-Driven Regression Discontinuity Plots. Journal of the American Statistical Association 110(512): 1753–1769.
  • ———. 2015b. rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs. R Journal 7(1): 38–51.
  • Cattaneo, M. D., and J. C. Escanciano. 2017. Regression Discontinuity Designs: Theory and Applications (Advances in Econometrics, volume 38). Emerald Group Publishing.
  • Cattaneo, M. D., N. Idrobo, and R. Titiunik. 2019a. A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press.
  • ———. 2020. A Practical Introduction to Regression Discontinuity Designs: Extensions. Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press (to appear).
  • Cattaneo, M. D., M. Jansson, and X. Ma. 2018. Manipulation Testing based on Density Discontinuity. Stata Journal 18(1): 234–261.
  • Cattaneo, M. D., L. Keele, R. Titiunik, and G. Vazquez-Bare. 2016a. Interpreting Regression Discontinuity Designs with Multiple Cutoffs. Journal of Politics 78(4): 1229–1248
  • ———. 2019b. Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs. arXiv:1808.04416 .
  • Cattaneo, M. D., R. Titiunik, and G. Vazquez-Bare. 2016b. Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331–367.
  • ———. 2017. Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality. Journal of Policy Analysis and Management 36(3): 643–681.
  • ———. 2019c. The Regression Discontinuity Design. In Handbook of Research Methods in Political Science and International Relations, ed. L. Curini and R. J. Franzese. Sage Publications, forthcoming.
  • ———. 2019d. Power Calculations for Regression Discontinuity Designs. Stata Journal 19(1): 210–245.
  • Keele, L. J., and R. Titiunik. 2015. Geographic Boundaries as Regression Discontinuities. Political Analysis 23(1): 127–155.
  • Keele, L. J., R. Titiunik, and J. Zubizarreta. 2015. Enhancing a Geographic Regression Discontinuity Design Through Matching to Estimate the Effect of Ballot Initiatives on Voter Turnout. Journal of the Royal Statistical Society: Series A 178(1): 223–239.
  • Papay, J. P., J. B. Willett, and R. J. Murnane. 2011. Extending the regressiondiscontinuity approach to multiple assignment variables. Journal of Econometrics 161(2): 203–207.
  • Reardon, S. F., and J. P. Robinson. 2012. Regression discontinuity designs with multiple rating-score variables. Journal of Research on Educational Effectiveness 5(1): 83–104.
  • Wong, V. C., P. M. Steiner, and T. D. Cook. 2013. Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables A Comparative Study of Four Estimation Methods. Journal of Educational and Behavioral Statistics 38(2): 107–141.

 

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