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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]
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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.
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.
———. 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
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.