PPT-Regression Discontinuity Designs in Applied Economic Research Dr. Kamiljon T. Akramov

Dr Kamiljon T Akramov IFPRI Washington DC USA Regional Training Course on Applied Econometric Analysis June 1223 2017 WIUT Tashkent Uzbekistan Outline Introduction and origins of RDD. Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model isavectorofparameterstobeestimatedand x isavectorofpredictors forthe thof observationstheerrors areassumedtobenormallyandindependentlydistributedwith mean 0 and constant variance The function relating the average value of the response to the pred We argue that estimators for causal e64256ects based on such methods can be misleading and we recommend researchers do not use them and instead use estimators based on local linear or quadratic polynomials or other smooth functions Keywords identi64 56 License Apache License 20 Title Regression Discontinuity Estimation Description This package provides the tools to undertake estimation in Regression Discontinuity Designs Both sharp and fuzzy designs are supported Estimation is accomplished usin Imbens Thomas Lemieux b Department of Economics Harvard University and NBER M24 Littauer Center Cambridge MA 02138 USA Department of Economics University of British Columbia and NBER 9971873 East Mall Vancouver BC V6T 1Z1 Canada Abstract In regress Gertler. , P. J.; Martinez, S., . Premand. , P., Rawlings, L. B. and . Christel. M. J. . Vermeersch. , 2010, Impact Evaluation in Practice: Ancillary Material, The World Bank, Washington DC (www.worldbank.org/ieinpractice). The content of this presentation reflects the views of the authors and not necessarily those of the World Bank. . Applied Regression -- Prof. Juran. 2. Outline for Session 2. More Simple Regression. Bottom Part of the Output . Hypothesis Testing. Significance of the slope and intercept parameters . Interval Estimation. Applied Regression -- Prof. Juran. 2. Outline for Session 4. Summary Measures for the Full Model . Top Section of the Output. Interval Estimation . More Multiple Regression. Movers. Nonlinear Regression. This Talk Will:. I. ntroduce the history and logic of RDD,. Consider conditions for its internal validity,. Considers its sample size requirements, . Consider its dependence on functional form,. Illustrate some specification tests for it,. Design. Basics. Two potential outcomes . Yi(0) . and. Yi(1), . causal effect . Yi(1) − Yi(0), . binary treatment indicator . Wi. , . covariate. Xi, . and the observed outcome equal to:. At . Xi = c . (FY14). Research and analysis completed by: . About Tripp Umbach. Tripp Umbach has experience throughout North America in completing economic impact studies for major research universities, hospitals, . Regression Discontinuity Designs . for . Program Evaluation: . An Institutional Simulation Using R. Gabe . Avakian. Orona, M.P.H.. CAIR 2016. November 17, 2016. Goals for today:. Introduce Regression Discontinuity Design (RDD). The World . Bank. REGRESSION . DISCONTINUITY. Technical Track Session V. (RD). Main Objective of an Evaluation . (Reminder). For example:. What is the effect . of an increase in the minimum wage on employment?. &. Double Machine learning. Review of Causal Inference. Office of the Chief Economist |. 2. Review of Causal Inference. Natural of Science: uncovering cause-and-effect relationships. Experiments are important tools to uncover causal relationship.