연구동향 분석

이중차분분석의 ‘금지된 비교’ 넘어서기: 이중차분분석의 최신 동향을 반영한 산정특례제도 효과 분석

김진환 2022년

논문상세정보
인용/피인용
' 이중차분분석의 ‘금지된 비교’ 넘어서기: 이중차분분석의 최신 동향을 반영한 산정특례제도 효과 분석' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Causal inference
  • DID ≠ TWFE
  • Two-way fixed effect estimator
  • difference-in-difference
  • 양방향고정효과추정량
  • 이중차분분석
  • 인과추론
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
60 0

0.0%

' 이중차분분석의 ‘금지된 비교’ 넘어서기: 이중차분분석의 최신 동향을 반영한 산정특례제도 효과 분석' 의 참고문헌

  • Why propensity scores should not be used for matching
    King, G. [2019]
  • What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature
    Roth, J. [2022]
  • Using synthetic controls: Feasibility, data requirements, and methodological aspects
    Abadie, A. [2021]
  • Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators
  • The triple difference estimator
    Olden, A. [2022]
  • The impact of benefits coverage expansion of social health insurance: Evidence from Korea
    Lee, H. M. [2022]
  • The effect of cost-sharing in private health insurance on the utilization of health care services between private insurance purchasers and non-purchasers: a study of the Korean health panel survey (2008–2012)
    Choi, Y. [2015]
  • Technology and big data are changing economics: Miningtext to track methods
    Currie, J. [2020]
  • Synthetic difference-in-differences
  • Should we combine difference in differences with conditioning on pre-treatment outcomes?
  • Semiparametric difference-in-differences estimators
    Abadie A. [2005]
  • Revisiting event study designs: Robust and efficient estimation
  • Progress on catastrophic health spending in 133 countries: a retrospective observational study
  • On the use of two-way fixed effects regression models for causal inference with panel data
    Imai, K. [2021]
  • On the linear in probability model for binary data
  • Now trending: Coping with non-parallel trends in difference-in-differences analysis
    Ryan, A. M. [2019]
  • Noncollapsibility, confounding, and sparse-data bias. Part 1: The oddities of odds
  • Interaction terms in nonlinear models
  • Incidence and correlates of ‘catastrophic’maternal health care expenditure in India
    Bonu, S. [2009]
  • How much should we trust staggered difference-in-differences estimates?
  • Generalized difference in differences with panel data and least squares estimator
    Lee, M. [2016]
  • Estimating dynamic treatment effects in event studies with heterogeneous treatment effects
    Sun, L. [2021]
  • Estimating and contextualizing the attenuation of odds ratios due to non collapsibility
    Burgess, S. [2017]
  • Doubly robust difference-in-differences estimators
  • Difference-in-differences with variation in treatment timing
  • Difference-in-differences with multiple time periods
  • Difference-in-differences estimators of intertemporal treatment effects
  • Designing difference in difference studies: Best practices for public health policy research
    Wing, C. [2018]
  • Design-based analysis in difference-in-differences settings with staggered adoption
    Athey, S. [2022]
  • Comparing approaches to causal inference for longitudinal data: inverse probability weighting versus propensity scores
  • Causal inference using potential outcomes: Design, modeling, decisions
  • An advantage of the linear probability model over probit or logit
  • 4대 중증질환 보장성 강화 정책이 의료비에 미친 영향: 본인부담금을 중심으로
    김관옥 [2017]