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The package supports estimation of QTEs with uniform confidence bands, hypothesis testing for treatment significance and directionality, and bandwidth selection via cross-validation or MSE-optimal rules. Visualization tools summarize treatment effects and conditional quantile processes.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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does the QTE.RD package provide for sharp regression discontinuity designs?","Question",{"text":63,"@type":64},"It provides methods to test and estimate quantile treatment effects (QTEs) and to conduct uniform inference, including options with covariates and robust bias correction.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which functions are included for QTE estimation, testing, and visualization?",{"text":68,"@type":64},"rd.qte estimates QTEs and uniform confidence bands, rdq.test performs tests on treatment-related hypotheses, and plot.qte generates plots summarizing QTE estimates and confidence bands.",{"name":70,"@type":61,"acceptedAnswer":71},"How are bandwidths selected in rdq.bandwidth?",{"text":72,"@type":64},"Two bandwidth selection rules are implemented: cross-validation bandwidth and MSE-optimal bandwidth selection.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},201801,1788538817,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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\nDesign  \nVersion 1.2.0  \nAuthor Zhongjun Qu [aut, cph], Jungmo Yoon [aut, cre, cph]  \nMaintainer Jungmo Yoon \u003C[jmyoon@hanyang.ac.kr](jmyoon@hanyang.ac.kr)>  \nDescription Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) \u003Cdoi:10 . 1162/rest_a_01168> .  \nEncoding UTF-8 RoxygenNote 7.3.2 Imports quantreg, plotrix, stats License GPL (>= 3)  \nSuggests knitr, rmarkdown, spelling, testthat (>= 3.0.0)  \nLanguage en-US  \nDepends R (>= 2.10) LazyData true  \nConfig/testthat/edition 3 VignetteBuilder knitr NeedsCompilation no Repository CRAN  \nDate/Publication 2025-08-30 04:20:02 UTC  \nContents  \nQTE.RD-package ...................................... 2  \nddk_2011 .......................................... 3  \nplot.qte ........................................... 4  \nrd.qte ............................................ 5  \n2 QTE.RD-package  \nrdq .............................................. 7  \n[rdq.band ...........................................](rdq.band ........................................... 8)[ 8](rdq.band ........................................... 8)  \n[rdq.bandwidth ........................................](rdq.bandwidth ........................................ 10)[ 10](rdq.bandwidth ........................................ 10)  \n[rdq.bias ...........................................](rdq.bias ........................................... 11)[ 11](rdq.bias ........................................... 11)  \n[rdq.condf ..........................................](rdq.condf .......................................... 12)[ 12](rdq.condf .......................................... 12)  \n[rdq.test ...........................................](rdq.test ........................................... 13)[ 13](rdq.test ........................................... 13)  \n[summary.qte .........................................](summary.qte ......................................... 15)[ 15](summary.qte ......................................... 15)  \nIndex 17  \n\n| QTE.RD-package | QTE. RD: Quantile Treatment Effects under the Regression Discontinuity Design |\n| --- | --- |\n\nDescription  \nProvides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) doi:10.1162/ rest_a_01168.  \nDetails  \nThe package QTE .RD includes four main functions:  \n• rd.qte estimates QTEs and provides uniform confidence bands, with or without covariates, and with or without robust bias correction.  \n• rdq.test conducts tests for three hypotheses, related to the significance of treatment effects, homogeneous treatment effects, and uniformly positive or negative treatment effects.  \n• rdq.bandwidth implements two bandwidth selection rules: the cross-validation bandwidth and the MSE optimal bandwidth.  \n• plot.qte generates figures summarizing the treatment effects along with their confidence bands.  \nAuthor(s)  \n• [Zhongjun Qu](Zhongjun Qu qu@bu.edu)[ qu@bu.edu](Zhongjun Qu qu@bu.edu)  \n• [Jungmo Yoon](Jungmo Yoon jmyoon@hanyang.ac.kr)[ jmyoon@hanyang.ac.kr](Jungmo Yoon jmyoon@hanyang.ac.kr)  \nReferences  \nZhongjun Qu, Jungmo Yoon, Pierre Perron (2024), \"Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits,\" The Review of Economics and Statistics; doi:10 . 1162/rest_a_01168  \nZhongjun Qu and Jungmo Yoon (2019), \"Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs,\" Journal of Business and Economic Statistics, 37(4), 625–647;  \ndoi:10.1080/07350015.2017.","cbCaiiTsy9R27Nq6","https://ap.wps.com/l/cbCaiiTsy9R27Nq6","pdf",161290,17,"English","# QTE.RD-package\n## ddk_2011\n## plot.qte\n## rd.qte\n## rdq\n## rdq.band\n## rdq.bandwidth\n## rdq.bias\n## rdq.condf\n## rdq.test\n## summary.qte\n## Index","[{\"question\":\"What does the QTE.RD package provide for sharp regression discontinuity designs?\",\"answer\":\"It provides methods to test and estimate quantile treatment effects (QTEs) and to conduct uniform inference, including options with covariates and robust bias correction.\"},{\"question\":\"Which functions are included for QTE estimation, testing, and visualization?\",\"answer\":\"rd.qte estimates QTEs and uniform confidence bands, rdq.test performs tests on treatment-related hypotheses, and plot.qte generates plots summarizing QTE estimates and confidence bands.\"},{\"question\":\"How are bandwidths selected in rdq.bandwidth?\",\"answer\":\"Two bandwidth selection rules are implemented: cross-validation bandwidth and MSE-optimal bandwidth selection.\"}]","QTE.RD - Quantile Treatment Effects under the Regression Discontinuity Design - Methods and inference | PDF",43]