Stata新命令: 多断点 RDD - rdmc

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2020连享会-文本分析与爬虫-现场班

西安, 3月26-29日，司继春-游万海 主讲   (附助教招聘)

连享会-文本分析与爬虫专题班，西北工业大学，2020.3.26-29

简介

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]

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.

Stata 实操演示

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参考文献：

• 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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