DRDID: Stata module for the estimation of Doubly Robust Difference-in-Difference models

Fernando Rios-Avila; Pedro H.C. Sant Anna; Syed Ali Asjad Naqvi

DRDID: Stata module for the estimation of Doubly Robust Difference-in-Difference models

Fernando Rios-Avila, Pedro H.C. Sant Anna, Syed Ali Asjad Naqvi

Ecological Economics

Publikation: Elektronische/multimediale Veröffentlichungen › Software

Abstract

ssc install drdid, replace

DRDID implements Sant'Anna and Zhao (2020) proposed estimators for the Average Treatment Effect on the Treated (ATT) in Difference-in-Differences (DID) setups where the parallel trends assumption holds after conditioning on a vector of pre-treatment covariates. For a generalization to multiple periods see CSDID. The main estimators in DRDID are locally efficient and doubly-robust estimators, because they combine Inverse probability weighting and outcome regression to estimate ATT's. DRDID can be applied to both balanced/unbalanced panel data, or repeated cross-section.

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 5 Aug. 2021

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

102009 Computersimulation
502042 Umweltökonomie
502025 Ökonometrie
502022 Nachhaltiges Wirtschaften

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Zitat

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title = "DRDID: Stata module for the estimation of Doubly Robust Difference-in-Difference models",

abstract = "ssc install drdid, replace DRDID implements Sant'Anna and Zhao (2020) proposed estimators for the Average Treatment Effect on the Treated (ATT) in Difference-in-Differences (DID) setups where the parallel trends assumption holds after conditioning on a vector of pre-treatment covariates. For a generalization to multiple periods see CSDID. The main estimators in DRDID are locally efficient and doubly-robust estimators, because they combine Inverse probability weighting and outcome regression to estimate ATT's. DRDID can be applied to both balanced/unbalanced panel data, or repeated cross-section.",

author = "Fernando Rios-Avila and {H.C. Sant Anna}, Pedro and Naqvi, {Syed Ali Asjad}",

year = "2021",

month = aug,

day = "5",

language = "English",

}

TY - ADVS

T1 - DRDID: Stata module for the estimation of Doubly Robust Difference-in-Difference models

AU - Rios-Avila, Fernando

AU - H.C. Sant Anna, Pedro

AU - Naqvi, Syed Ali Asjad

PY - 2021/8/5

Y1 - 2021/8/5

N2 - ssc install drdid, replace DRDID implements Sant'Anna and Zhao (2020) proposed estimators for the Average Treatment Effect on the Treated (ATT) in Difference-in-Differences (DID) setups where the parallel trends assumption holds after conditioning on a vector of pre-treatment covariates. For a generalization to multiple periods see CSDID. The main estimators in DRDID are locally efficient and doubly-robust estimators, because they combine Inverse probability weighting and outcome regression to estimate ATT's. DRDID can be applied to both balanced/unbalanced panel data, or repeated cross-section.

AB - ssc install drdid, replace DRDID implements Sant'Anna and Zhao (2020) proposed estimators for the Average Treatment Effect on the Treated (ATT) in Difference-in-Differences (DID) setups where the parallel trends assumption holds after conditioning on a vector of pre-treatment covariates. For a generalization to multiple periods see CSDID. The main estimators in DRDID are locally efficient and doubly-robust estimators, because they combine Inverse probability weighting and outcome regression to estimate ATT's. DRDID can be applied to both balanced/unbalanced panel data, or repeated cross-section.

M3 - Software

ER -