Oct 05, 2017 this presentation focused on the theory, design, and implementation of regression discontinuity rdd. Software for regressiondiscontinuity designs matias d. The regression discontinuity design rdd is a quasiexperimental approach used to avoid confounding bias in the assessment of new policies and interventions. Although it seems there are specific command such as rd that can be used to apply rdd, i was told that rdd could be carried out using just regular regression model, which in my case it would be logistic model i suppose because the outcome i am interested. However, theres also a dedicated rd command in stata called rdrobust. Section 5 notes instrumental variables and regression.
Austin nichols simulation results indicate that the marginal effects may be less sensitive than the latent index function parameters to biprobit assumption violations. External validity in fuzzy regression discontinuity designs. Using a fuzzy regression discontinuity design in close mixed gender races for the election of mayors in italian municipalities in the period 20002015, we find that female mayors collect more. Quantile treatment effects in the regression discontinuity design. You can find an intro to the command in cattaneo, calonico, and titiuniks stata journal paper robust datadriven inference in the regressiondiscontinuity design.
It can handle the fuzzy design and may be installed with. A practical introduction to regression discontinuity designs. Regression discontinuity design university of colorado denver. Randomized controlled trials in a regression discontinuity framework. It can be obtained using the lowess command in stata. A practical guide to regression discontinuity citeseerx. Extensions, matias cattaneo, nicolas idrobo, and roc. You can find an intro to the command in cattaneo, calonico, and titiuniks stata journal paper robust datadriven inference in the regression discontinuity design. The revised regression discontinuity design standards will replace the pilot regression discontinuity standards in the next release of the wwc handbook, planned for 2016. It provides point estimators, confidence intervals estimators, bandwidth selectors, automatic rd plots and other related features. This presentation focused on the theory, design, and implementation of regression discontinuity rdd. The main new features of this upgraded version are as follows. Polynomials should not be used in regression discontinuity designs \we argue that estimators for causal e ects based on higher order.
Regression discontinuity design rdd is a quasiexperimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution. While the weak identi cation problem is widely recognized in iv. A practical introduction to regression discontinuity. An intuitive introduction to regression discontinuity. It is one of the most credible quasiexperimental research designs for identi. This document contains revised regression discontinuity design rdd standards that have been developed by the wwc in consultation with experts and will be implemented once they become part of a new wwc procedures and standards handbook, planned for 2017 the wwc procedures and standards handbook provides a detailed description of the standards and procedures of the wwc. Willett this is one of the books available for loan from academic technology services see statistics books for loan for. We describe a major upgrade to the stata and r rdrobust package, which provides a wide array of estimation, inference, and falsification methods for the analysis and interpretation of regression discontinuity designs. Plot for a fuzzy regression discontinuity design statalist. External validity in fuzzy regression discontinuity designs marinho bertanha and guido w.
Willett this is one of the books available for loan from academic technology services see statistics books for loan for other such books, and details about borrowing. Regression discontinuity \rd stata section we will use a new dataset. Ppt regressiondiscontinuity design powerpoint presentation. One of rdds main advantages is identification of a local causal effect under minimal functional form assumptions. Regression discontinuity design with many thresholds. Default is deriv0 for sharp rd, or for fuzzy rd if fuzzy is also speci. It is applied specifically in situations where individuals are assigned to a policyintervention based on whether they are above or below a prespecified cutoff on a continuously measured variable, such as birth. Robust inference in fuzzy regression discontinuity designs yang he november 2, 2017 abstract fuzzy regression discontinuity rd design and instrumental variables iv regression share similar identi cation strategies and numerically yield the same results under certain conditions. Polynomials should not be used in regression discontinuity designs. Under parametric heterogeneity, this paper proposes an ate estimator for the fuzzy case that optimally combines observations to. Regression discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment and outcome discontinuities occur. In my case the conventional differs in sign and size from the other two. It is useful to distinguish between two general settings, the sharp and the fuzzy regression discontinuity srd and frd from hereon designs e.
Stata code and data for all examples will be available on chalk. The regression discontinuity rd design is widely used in economics and other social, behavioral, biomedical, and statistical sciences. In this element and its accompanying second element, a practical introduction to regression discontinuity designs. C14 abstract many empirical studies use fuzzy regression discontinuity frd designs to identify treatment effects when the receipt of treatment is potentially correlated to outcomes. If z 0, then the individual is assigned to treatment. Although it seems there are specific command such as rd that can be used to apply rdd, i was told that rdd could be carried out using just regular regression model, which in my case it would be logistic model i suppose because the outcome i am interested in is a binary variable 1drop out, 0 didnt drop out.
Cutoff point increases the probability of treatment but. The regression discontinuity rd design is widely used in applied work. Regression discontinuity notes for matlab and stata regression discontinuity software. We describe a major upgrade to the stata and r rdrobust package. Rdestimate regression discontinuity estimation description rdestimate supports both sharp and fuzzy rdd utilizing the aer package for 2sls regression under the fuzzy design. In this case, the threshold rule is an instrument for treatment. We describe a major upgrade to the stata and r rdrobust package, which provides a wide array of estimation, inference, and falsification methods for the analysis and interpretation of regressiondiscontinuity designs. Regression discontinuity design in stata part 1 stata. More specifically, this command gives a conventional coefficient, a biascorrected one, and a robust one. Robust inference in fuzzy regression discontinuity designs. Stata module to perform regression discontinuity, statistical software components s458238, boston college department of economics. Theres another variable that is randomized and its correlated with the treatment we care about. The wwc continues to refine its processes, develop new standards, and create new products.
The regression discontinuity design rdd was originally developed by thistlethwaite and campbell 1960 as a quasiexperimental design for evaluating the impact of an education program in a setting where exposure to a treatment is determined by exceeding some score threshold. On interpreting the regression discontinuity design as a local experiment. When the circumstances are right, regression discontinuity can be an excellent way to extract causal estimates from observational data. Rd designs are also easy to present, interpret and falsify, features that have contributed to their popularity among. In the srd design the assignment w i is a deterministic function of one of the covariates, the forcing or treatmentdetermining variable x 1. Regression discontinuity design rdd using stata statalist.
Plot for a fuzzy regression discontinuity design 15 oct 2019, 14. Regression discontinuity for causal effect estimation in. For fuzzy design have two local linear or local polynomial regressions. Using a regression discontinuity design rdd to measure educational effectiveness. Regression discontinuity plot with confidence intervals i am trying to build an rd plot but in order to make it more easily readable i would like to add confidence intervals, since in some cases it seems there is a discontinuity but it is non significant. For example, if the assignment variable z follows a uniform distribution over the range 0, 10, patients who are randomly assigned a value of z of. In the srd design the assignment w i is a deterministic function of one of the. Theory and applications advances in econometrics, volume 38, ed. Rdd is one of the most powerful tools in the suite of quasiexperimental methods used to. Journal of econometrics, 1422, february 2008, pages 698. Within the causal inference framework, this design is considered among the most credible nonexperimental strategies because it relies on relatively weak and easy. Quantile treatment effects in the regression discontinuity.
Regression discontinuity rd analysis is a rigorous nonexperimental1 approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cutpoint. Regression discontinuity design with many thresholds by. Rddtools is a new r package under development, designed to offer a set of tools to run all the steps required for a regression discontinuity design rdd analysis, from primary data visualisation to discontinuity estimation, sensitivity and placebo testing. In this article, we introduce three commands to conduct robust datadriven statistical inference in regressiondiscontinuity rd designs. Instrumental variables and regression discontinuity stephen pettigrew november 17, 2015 1 instrumental variables setup. When the assignment variable is a random number that is generated by the researcher, the regression discontinuity design is equivalent to a randomized controlled trial. Regression discontinuity design in stata part 1 stata daily. It is one of the best quasiexperimental research designs for identi cation, estimation and inference of treatment e ects local to the cuto.
Rdd is one of the most powerful tools in the suite of quasiexperimental methods used. Dec 16, 2015 there has been a growing use of regression discontinuity design rdd, introduced by thistlewaite and campbell 1960, in evaluating impacts of development programs. Local linear regressions are performed to either side of the cutpoint using the imbenskalyanaraman optimal bandwidth calculation, ikbandwidth. Regression discontinuity plot with confidence intervals.
There has been a growing use of regression discontinuity design rdd, introduced by thistlewaite and campbell 1960, in evaluating impacts of development programs. Setting deriv1 results in estimation of a kink rd design up to scale, or fuzzy kink rd if fuzzy is also speci. Apr 20, 2020 the rdrobust package provides stata and r implementations of statistical inference and graphical procedures for regression discontinuity designs employing local polynomial and partitioning methods. Estimating causal effects using a regressiondiscontinuity approach stata textbook examples.
Regression discontinuity design rdd exploits the facts that. One sufficient, though not necessary, condition is if the treatment assignment is as good as random at the threshold for. First, we present rdrobust, a command that implements the robust biascorrected confidence intervals proposed in calonico, cattaneo, and titiunik 2014d, econometrica 82. Manipulation of the running variable in the regression discontinuity design. The rdrobust package provides stata and r implementations of. The regressiondiscontinuity rd design is widely used in applied work.
I am using a fuzzy regression discontinuity design for the first time and this maybe a very basic question to some. We have a variable that we want to know the treatment e ect of, but its not randomized. Discontinuity design relative to an otherwise comparable randomized. Regression discontinuity designs employing local polynomial and. Aug 05, 2016 in the fuzzy regression discontinuity design, the effect of treatment among those who take treatment because of the threshold rule can be identified using instrumental variable methods. In this video i give you a prototypical situation where rd. Fuzzy regression discontinuity design in stata cross validated. Basic idea is that the assignment to the treatment is going to be. Part 2 explains how in theory a regression discontinuity analysis can identify an average effect of treatment for a population and how different types of regression discontinuity analyses sharp versus fuzzy can identify average. Identification in the fuzzy case, with multiple cutoffs, is impossible unless heterogeneity follows a finitedimensional function of cutoff characteristics. The prediction, made by campbell and stanley 1963, that the rdd is very limited in its range of. Jan 30, 2015 when the circumstances are right, regression discontinuity can be an excellent way to extract causal estimates from observational data.
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