Difference-in-Differences with multiple time periods
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abadie, 2005, Semiparametric difference-in-difference estimators, Rev. Econom. Stud., 72, 1, 10.1111/0034-6527.00321
Abadie, A., Athey, S., Imbens, G., Wooldridge, J., 2017. When should you adjust standard errors for clustering? Working Paper. pp. 1–33.
Abbring, 2003, The nonparametric identification of treatment effects in duration models, Econometrica, 71, 1491, 10.1111/1468-0262.00456
Ai, 2003, Efficient estimation of models with conditional moment restrictions containin unknown functions, Econometrica, 71, 1795, 10.1111/1468-0262.00470
Ai, 2007, Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables, J. Econometrics, 141, 5, 10.1016/j.jeconom.2007.01.013
Ai, 2012, The semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions, J. Econometrics, 170, 442, 10.1016/j.jeconom.2012.05.015
Athey, 2006, Identification and inference in nonlinear difference in differences models, Econometrica, 74, 431, 10.1111/j.1468-0262.2006.00668.x
Athey, S., Imbens, G.W., 2018. Design-based analysis in difference-in-differences settings with staggered adoption. Working Paper.
Bailey, 2015, The war on poverty’s experiment in public medicine: Community health centers and the mortality of older Americans, Amer. Econ. Rev., 105, 1067, 10.1257/aer.20120070
Belloni, 2017, Program evaluation and causal inference with high-dimensional data, Econometrica, 85, 233, 10.3982/ECTA12723
Bertrand, 2004, How much should we trust differences-in-differences estimates?, Q. J. Econ., 119, 249, 10.1162/003355304772839588
Bojinov, I., Rambachan, A., Shephard, N., 2020. Panel experiments and dynamic causal effects: A finite population perspective. Working Paper.
Bonhomme, 2011, Recovering distributions in difference-in-differences models: A comparison of selective and comprehensive schooling, Rev. Econ. Stat., 93, 479, 10.1162/REST_a_00164
Botosaru, 2018, Difference-in-differences when the treatment status is observed in only one period, J. Appl. Econometrics, 33, 73, 10.1002/jae.2583
Busso, 2014, New evidence on the finite sample properties of propensity score reweighting and matching estimators, Rev. Econ. Stat., 96, 885, 10.1162/REST_a_00431
Callaway, 2018, Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods, J. Econometrics, 206, 395, 10.1016/j.jeconom.2018.06.008
Card, 1994, Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania, Amer. Econ. Rev., 84, 772
de Chaisemartin, 2017, Fuzzy differences-in-differences, Rev. Econom. Stud., 85, 999, 10.1093/restud/rdx049
de Chaisemartin, 2020, Two-way fixed effects estimators with heterogeneous treatment effects, Amer. Econ. Rev., 110, 2964, 10.1257/aer.20181169
Chen, 2008, Semiparametric efficiency in GMM models with auxiliary data, Ann. Statist., 36, 808, 10.1214/009053607000000947
Chen, 2003, Estimation of semiparametric models when the criterion function is not smooth, Econometrica, 71, 1591, 10.1111/1468-0262.00461
Cheng, 2013, The cluster bootstrap consistency in generalized estimating equations, J. Multivariate Anal., 115, 33, 10.1016/j.jmva.2012.09.003
Chernozhukov, 2013, Average and quantile effects in nonseparable panel models, Econometrica, 81, 535, 10.3982/ECTA8405
Chernozhukov, 2018, The sorted effects method: Discovering heterogeneous effects beyond their averages, Econometrica, 86, 1911, 10.3982/ECTA14415
Conley, 2011, Inference with “difference in differences” with a small number of policy changes, Rev. Econ. Stat., 93, 113, 10.1162/REST_a_00049
Crump, 2009, Dealing with limited overlap in estimation of average treatment effects, Biometrika, 96, 187, 10.1093/biomet/asn055
Dube, 2010, Minimum wage effects across state borders: Estimates using contiguous counties, Rev. Econ. Stat., 92, 945, 10.1162/REST_a_00039
Dube, 2016, Minimum wage shocks, employment flows, and labor market frictions, J. Lab. Econ., 34, 663, 10.1086/685449
Farber, 2017, Employment, hours, and earnings consequences of job loss: US evidence from the Displaced Workers Survey, J. Lab. Econ., 35, S235, 10.1086/692353
Ferman, 2019, Inference in differences-in-differences with few treated groups and heteroskedasticity, Rev. Econ. Stat., 101, 452, 10.1162/rest_a_00759
Freyberger, 2018, Uniform confidence bands: Characterization and optimality, J. Econometrics, 204, 119, 10.1016/j.jeconom.2018.01.006
Goodman-Bacon, A., 2019. Difference-in-differences with variation in treatment timing. NBER Working Paper n. 25018. Working Paper.
Graham, 2012, Inverse probability tilting for moment condition models with missing data, Rev. Econom. Stud., 79, 1053, 10.1093/restud/rdr047
Hájek, 1971, Discussion of ‘An essay on the logical foundations of survey sampling, Part I’, by D. Basu
Han, 2020, Identification in nonparametric models for dynamic treatment effects, J. Econometrics
Heckman, 1998, Characterizing selection bias using experimental data, Econometrica, 66, 1017, 10.2307/2999630
Heckman, 1997, Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme, Rev. Econom. Stud., 64, 605, 10.2307/2971733
Imai, K., Kim, I.S., Wang, E., 2018. Matching methods for causal inference with time-series cross-section data. Working Paper.
Khan, 2010, Irregular identification, support conditions, and inverse weight estimation, Econometrica, 78, 2021, 10.3982/ECTA7372
Kline, 2012, A score based approach to wild bootstrap inference, J. Econometr. Methods, 1, 1, 10.1515/2156-6674.1006
Kosorok, 2008
Laporte, 2005, Estimation of panel data models with binary indicators when treatment effects are not constant over time, Econom. Lett., 88, 389, 10.1016/j.econlet.2005.04.002
MacKinnon, 2018, The wild bootstrap for few (treated) clusters, Econom. J., 21, 114, 10.1111/ectj.12107
MacKinnon, 2020, Randomization inference for difference-in-differences with few treated clusters, J. Econometrics, 218, 435, 10.1016/j.jeconom.2020.04.024
Malani, 2015, Interpreting pre-trends as anticipation: Impact on estimated treatment effects from tort reform, J. Publ. Econ., 124, 1, 10.1016/j.jpubeco.2015.01.001
Mammen, 1993, Bootstrap and wild bootstrap for high dimensional linear models, Ann. Statist., 21, 255, 10.1214/aos/1176349025
Marcus, 2020, The role of parallel trends in event study settings : An application to environmental economics, J. Assoc. Environ. Resour. Econom.
McCrary, 2007, The effect of court-ordered hiring quotas on the composition and quality of police, Amer. Econ. Rev., 97, 318, 10.1257/aer.97.1.318
Meer, 2016, Effects of the minimum wage on employment dynamics, J. Hum. Resour., 51, 500, 10.3368/jhr.51.2.0414-6298R1
Montiel Olea, 2018, Simultaneous confidence bands: Theory, implementation, and an application to SVARs, J. Appl. Econometrics, 1
Murphy, 2003, Optimal dynamic treatment regimes, J. R. Stat. Soc. Ser. B Stat. Methodol., 65, 331, 10.1111/1467-9868.00389
Murphy, 2001, Marginal mean models for dynamic regimes, J. Amer. Statist. Assoc., 96, 1410, 10.1198/016214501753382327
Neumark, 2000, Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania: Comment, Amer. Econ. Rev., 90, 1362, 10.1257/aer.90.5.1362
Neumark, 2008
Newey, 1994, The asymptotic variance of semiparametric estimators, Econometrica, 62, 1349, 10.2307/2951752
Oreopoulos, 2012, The short- and long-term career effects of graduating in a recession, Am. Econ. J.: Appl. Econ., 4, 1
Qin, 2008, Empirical-likelihood-based difference-in-differences estimators, J. R. Stat. Soc. Ser. B Stat. Methodol., 75, 329, 10.1111/j.1467-9868.2007.00638.x
Robins, 1986, A new approach to causal inference in mortality studies with a sustained exposure period - Application to control of the healthy worker survivor effect, Math. Modelling, 7, 1393, 10.1016/0270-0255(86)90088-6
Robins, 1987, Addendum to ‘A new approach to causal inference in mortality studies with a sustained exposure period - Application to control of the healthy worker survivor effect’, Comput. Math. Appl., 14, 923, 10.1016/0898-1221(87)90238-0
Roth, J., 2020. Pre-test with caution: Event-study estimates after testing for parallel trends. Working Paper. pp. 1–84.
Rubin, 2007, The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials, Stat. Med., 26, 20, 10.1002/sim.2739
Rubin, 2008, For objective causal inference, design trumps analysis, Ann. Appl. Stat., 2, 808, 10.1214/08-AOAS187
Sant’Anna, 2019, Specification tests for the propensity score, J. Econometrics, 210, 379, 10.1016/j.jeconom.2019.02.002
Sant’Anna, 2020, Doubly robust difference-in-differences estimators, J. Econometrics, 219, 101, 10.1016/j.jeconom.2020.06.003
Sherman, 2007, A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models, Comm. Statist. Simulation Comput., 26, 901, 10.1080/03610919708813417
Sianesi, 2004, An evaluation of the Swedish system of active labor market programs in the 1990s, Rev. Econ. Stat., 86, 133, 10.1162/003465304323023723
Słoczyński, T., 2018. A general weighted average representation of the ordinary and two-stage least squares estimands. Working Paper.
Sun, L., Abraham, S., 2020. Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Working Paper.
van der Vaart, 1998
van der Vaart, 1996
Wooldridge, 2003, Cluster-sample methods in applied econometrics, Am. Econ. Rev. P P, 93, 133, 10.1257/000282803321946930
Wooldridge, 2005, Fixed-effects and related estimators for correlated random-coefficient and treatment-effect panel data models, Rev. Econ. Stat., 87, 385, 10.1162/0034653053970320
Wooldridge, 2005, Violating ignorability of treatment by controlling for too many factors, Econometr. Theory, 21, 1026, 10.1017/S0266466605050516
Wooldridge, 2007, Inverse probability weighted estimation for general missing data problems, J. Econometrics, 141, 1281, 10.1016/j.jeconom.2007.02.002