Replication Data for: A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Datadoi:10.7910/DVN/ZVC9W5Harvard Dataverse2022-05-021Liu, Licheng; Wang, Ye; Xu, Yiqing, 2022, "Replication Data for: A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data", https://doi.org/10.7910/DVN/ZVC9W5, Harvard Dataverse, V1, UNF:6:gJRdTz84oqmez8xIYKyPUA== [fileUNF]Replication Data for: A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Datadoi:10.7910/DVN/ZVC9W5Liu, LichengWang, YeXu, YiqingXu, YiqingHarvard DataverseXu, YiqingXu, Yiqing2022-03-02Social SciencesImputation methodsCounterfactual estimatorsTwoway fixed effectsParallel trendsInteractive fixed effectsMatrix completionEquivalence testsPlacebo testsTime-series cross-sectional dataPanel dataThis paper introduces a simple framework of counterfactual estimation for causal inference with time-series cross-sectional data, in which we estimate the average treatment effect on the treated by directly imputing counterfactual outcomes for treated observations. We discuss several novel estimators under this framework, including the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. They provide more reliable causal estimates than conventional twoway fixed effects models when treatment effects are heterogeneous or unobserved time-varying confounders exist. Moreover, we propose a new dynamic treatment effects plot, along with several diagnostic tests, to help researchers gauge the validity of the identifying assumptions. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.This dataset underwent an independent verification process that replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of code. The verification process was carried out by the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. <br></br> The associated article has been awarded Open Materials and Open Data Badges. Learn more about the Open Practice Badges from the <a href="https://osf.io/tvyxz/wiki/home/" target="_blank">Center for Open Science</a>.<br></br> <img src="https://odum.unc.edu/files/2020/03/OpenData_PR-1.png" alt="Open Data Badge" height="77" width="80"> <img src="https://odum.unc.edu/files/2020/03/OpenMaterials_PR-1.png" alt="Open Materials Badge" height="77" width="80">Hainmueller, Jens and Dominik Hangartner. 2015. “Does Direct Democracy Hurt Immigrant Minorities? Evidence from Naturalization Decisions in Switzerland.” American Journal of Political Science 63(33): pp. 14–38.<br></br> Fouirnaies, Alexander and Hande Mutlu-Eren. 2015. “English Bacon: Copartisan Bias in Intergovernmental Grant Allocation in England.” The Journal of Politics 77(3):805–817.The <i>American Journal of Political Science</i> and the Odum Institute for Research in Social Science are not responsible for the accuracy or quality of data uploaded within the <i>AJPS</i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.This dataset is made available with limited information on how it can be used. You may wish to communicate with the Contact(s) specified before use.Liu, Licheng, Ye Wang, and Yiqing Xu. [date]. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” <i>American Journal of Political Science</i> Forthcoming. <a href="http://ajps.org/" target="_blank">http://ajps.org/</a>Liu, Licheng, Ye Wang, and Yiqing Xu. [date]. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” <i>American Journal of Political Science</i> Forthcoming. <a href="http://ajps.org/" target="_blank">http://ajps.org/</a>fm2015.tab80596text/tab-separated-valuesUNF:6:IvMmibETutwUCmTsaMjSEw==Data file for Fouirnaies and Mutlu-Eren (2015).hh2015.tab254314text/tab-separated-valuesUNF:6:NH5HjUjeLXrV6y/B92dM6g==Data file for Hainmueller and Hangartner (2015).group(assembly_name type).234.90197294949175466.00.0235.0133.76370040158981.08059.0UNF:6:347MxyPjxAafKU5FrDMhcw==year1992.02002.0.2001.7786325846888059.06.02331740079191750.02012.0UNF:6:aIM4PVCAm6wB1ZAqkqq4WA==2.0702031109084012.7403388657280272.2694187164306647993.07.73755407333374-2.42921781539917.66.0UNF:6:VKlrWeJy+PFyQwpxbBcLog==0.00.44696752571922876.0.01.07758.00.2758442897654041301.0UNF:6:xjIFJRORO3TG1rZ+Vt2TQw==6909.01997.06.911631783684171992.02011.01998.7595889419597.1150.0UNF:6:mo0jMviOBKH4JUPWVJkv2Q==8059.01.65975927534433240.00.96327834973904383.00.02.0.UNF:6:l2epoanM/cmo1+ZSTGFcaQ==Municipality BFS code6800.0.0.02145.774640750971.03183.13212221209325431.03219.0UNF:6:2LtYJIUUSEA9NoqX641uzw==1990.025431.0.6.0554197652374582000.02010.00.02000.0UNF:6:IeHtOs26nNk1k/x2XtP0Kw==4.56970453019332323009.046.666667938232422.4695833677163592422.00.00.39777249097824097.UNF:6:9knNCQVeRiE+/0729Cd4+A==25431.00.477619052231954440.00.0.0.352050646848341640.01.0UNF:6:hab33J/GI3zA36uGX8tvRQ==0_root.RExecute all code files.type/x-r-syntax1_ex_sim0.RPerforms analysis on a simulated dataset.type/x-r-syntax2_ex_HH2015.RPerforms analysis on Hainmueller and Hangartner (2015).type/x-r-syntax3_ex_FM2015.RPerforms analysis on Fouirnaies and Mutlu-Eren (2015).type/x-r-syntax4_sim_tests_n100.RConducts simulations comparing the F test and the equivalence test (n = 100). type/x-r-syntax5_sim_tests_n300.RConducts simulations comparing the F test and the equivalence test (n = 300).type/x-r-syntax6_sim_ife_mc.RConducts simulations comparing IFEct and MC.type/x-r-syntax7_sim_inference.RConduct simulations to investigate the properties of inferential methods.type/x-r-syntax8_plot_sim.RPlot simulation results.type/x-r-syntaxCodebook.pdfCodebook.application/pdfex_FM2015.RDataStored results for Fouirnaies and Mutlu-Eren (2015).application/x-rlang-transportex_HH2015.RDataStored results for Hainmueller and Hangartner (2015).application/x-rlang-transportfastplm_1.0.8.tar.gzSource file for R package “fastplm”.application/gzipfect_0.4.1.tar.gzSource file for R package “fect”.application/gziplog_fm2015.txtLog file for “3_ex_FM2015.R”.text/plainlog_hh2015.txtLog file for “2_ex_HH2015.R”.text/plainlog_inference.txtLog file for “7_sim_inference.R”.text/plainlog_simdata.txtLog file for “1_ex_sim0.R”.text/plainlog_sim_ife_mc.txtLog file for “6_sim_ife_mc.R”.text/plainlog_sim_tests_n100.txtLog file for “4_sim_tests_n100.R”.text/plainlog_sim_tests_n300.txtLog file for “5_sim_tests_n300.R”.text/plainpanelView_1.1.9.tar.gzSource file for R package “panelView”.application/gzipqqplots_N100.RDataStored results for the QQ plot (N = 100).application/x-rlang-transportqqplots_N50.RDataStored results for the QQ plot (N = 50). application/x-rlang-transportReadMe.pdfReadMe file.application/pdfsimexample.RDataStored results for the simulated example.application/x-rlang-transportsim_ife_mc.RDataStored results for the IFE/MC comparison.application/x-rlang-transportsim_tests_n100.RDataStored results for the F/equivalence test comparison (N = 100)application/x-rlang-transportsim_tests_n300.RDataStored results for the simulated example.application/x-rlang-transportsimulateData.RCode to simulate panel data with general treatment structure.type/x-r-syntaxsimulateDID.RCode to simulate panel data with DID or staggered adoption treatment.type/x-r-syntaxzzz.txttext/plain