[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-202021-105":59,"doc-detail-202021-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","estimating-quantile-treatment-effects-for-panel-data","Estimating Quantile Treatment Effects for Panel Data","","Motivated by Hsiao, Ching and Wan (2012), the paper develops a quantile treatment effect model for panel data to capture distributional consequences of a treatment. Counterfactual quantiles for treated units are estimated via the relationship between conditional and unconditional distributions. The work proves asymptotic properties of the proposed estimator and discusses how to choose control units and covariates. A simulation study validates the method and an application analyzes CSI 300 index futures trading effects on stock returns and volatility in China.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/estimating-quantile-treatment-effects-for-panel-data/202021/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/estimating-quantile-treatment-effects-for-panel-data/202021.png","ImageObject",300,407,{"name":92,"@type":93},"Chloe Bennett","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the paper aim to estimate using panel data?","Question",{"text":113,"@type":114},"It estimates quantile treatment effects to characterize how a treatment changes not just the mean outcome but the outcome distribution.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are counterfactual quantiles for treated units obtained?",{"text":118,"@type":114},"They are estimated using the relationship between conditional and unconditional distributions for the treated unit.",{"name":120,"@type":111,"acceptedAnswer":121},"What do the authors do to support the proposed method?",{"text":122,"@type":114},"They establish asymptotic properties for the estimator, conduct a simulation study, and apply the method to CSI 300 index futures trading impacts on returns and volatility in China.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},202021,1788541125,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},962084925782,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Estimating Quantile Treatment Effects for  \nPanel Data ∗† ‡  \nZongwu Caia, Ying Fangb;c , Ming Linb;c , and Mingfeng Zhanb  \na Department of Economics, University of Kansas, Lawrence, KS 66045, USA. bWang Yanan Institute for Studies in Economics and Fujian Key Laboratory of Statistical Sciences,  \nXiamen University, Xiamen, Fujian 361005, China.  \ncDepartment of Statistics and Data Science, School of Economics, Xiamen University,  \nXiamen, Fujian 361005, China.  \nFebruary 4, 2022  \nAbstract: Motivated by the paper by Hsiao, Ching and Wan (2012), which proposed a factorbased model to estimate the average treatment effect with panel data, this paper proposes a quantile treatment effect model for panel data to characterize the distributional effect of a treatment. We propose to estimate the counterfactual quantile for the treated unit by using the relationship between conditional and unconditional distributions. Also, the asymptotic properties for the proposed quantile treatment effect estimator are established, together with discussing the choice of control units and covariates. A simulation study is conducted to illustrate our method. Finally, the proposed method is applied to estimate the quantile treatment effects of introducing CSI 300 index futures trading on both the log-return and volatility of the stock market in China.  \nKeywords: LASSO method; Panel data; Nonparametric estimation; Quantile regression; Treatment effects.  \nJEL classification: C13; C14; C33; C52; C54  \n􀀃 The authors acknowledge the partially financial supports from the National Science Fund of China (NSFC) for Distinguished Scholars with the grant number 71625001, the NSFC key projects with grant numbers 71631004 and 72033008, and the Science Foundation of Ministry of Education of China with the grant number 19YJA910003 .  \ny Corresponding author: Y. Fang (E-mail: [yifst1@xmu.edu.cn](yifst1@xmu.edu.cn))  \nzE-mail addresses: [caiz@ku.edu](caiz@ku.edu) (Z. Cai), [yifst1@xmu.edu.cn](yifst1@xmu.edu.cn) (Y. Fang), [linming50@xmu.edu.cn](linming50@xmu.edu.cn) (M. Lin),  \n[zhanmf@stu.xmu.edu.cn](zhanmf@stu.xmu.edu.cn) (M. Zhan).  \n1 Introduction  \nSince the seminal work by Hsiao, Ching and Wan (2012, hereafter HCW), estimating average treatment effects (ATE) using panel data approach (PDA) has become widespread in empirical economics. HCW proposed to construct the counterfactual outcome of a treated unit by exploiting the cross-sectional dependence driven by a common factor structure for all units. Under a commonly adopted identification assumption that a policy intervention does not affect outcomes in the control group, HCW estimated the counterfactual outcome by a simple linear regression method without estimating factors and loadings. Moreover, the HCW’s method has an advantage of least data demand which is a very desirable virtue in some empirical applications. The counterfactual outcome can be estimated using outcome variables of cross-sectional units and not necessary to use other covariates. For example, HCW estimated the impact on economic growth of political and economic integration of Hong Kong with mainland China only using the quarterly real GDP growth rates of 22 countries and regions. Recently, Bai, Li and Ouyang (2014), Ouyang and Peng (2015), and Li and Bell (2017) relaxed some assumptions made implicitly in HCW and instead of using AIC type approach to select control units, Li and Bell (2017) and Carvalho, Masini and Medeiros (2018) suggested using a LASSO type method to select control units when the number of control units is large with sparsity. Also, Li and Bell (2017) derived the asymptotic distribution of 1 which facilitates inference and Carvalho, Masini and Medeiros (2018) provided a theoretical justification for the LASSO estimate.  \nBy virtue of the aforementioned desirable properties, such as weak assumptions, least data demand and easy-to-implementation, the HCW’s PDA has received increasing interests in policy evaluation literature. For example,","cbCair5kirqb8ZMV","https://ap.wps.com/l/cbCair5kirqb8ZMV","pdf",553923,35,"English","# Introduction\n## Background and motivation\n## Related literature\n## Limitations of ATE and need for quantile effects","[{\"question\":\"What does the paper aim to estimate using panel data?\",\"answer\":\"It estimates quantile treatment effects to characterize how a treatment changes not just the mean outcome but the outcome distribution.\"},{\"question\":\"How are counterfactual quantiles for treated units obtained?\",\"answer\":\"They are estimated using the relationship between conditional and unconditional distributions for the treated unit.\"},{\"question\":\"What do the authors do to support the proposed method?\",\"answer\":\"They establish asymptotic properties for the estimator, conduct a simulation study, and apply the method to CSI 300 index futures trading impacts on returns and volatility in China.\"}]","Estimating Quantile Treatment Effects for Panel Data | PDF",88]