After ☕️ ☕️ ☕️ ☕️ ☕️ ☕️ ☕️ ☕️
staggered complaints, `stagopts' estimand(eventstudy simple) eventTime(0/4) num_fisher(500)
staggered complaints, `stagopts' estimand(eventstudy simple) eventTime(0/4) num_fisher(500)
November 22, 2024 at 1:11 PM
After ☕️ ☕️ ☕️ ☕️ ☕️ ☕️ ☕️ ☕️
staggered complaints, `stagopts' estimand(eventstudy simple) eventTime(0/4) num_fisher(500)
staggered complaints, `stagopts' estimand(eventstudy simple) eventTime(0/4) num_fisher(500)
New paper out! We find positive effects of exposure to recent migrants on test scores of incumbent students in rural schools, but negative effects in large cities. Class size reductions seem to matter. Eventstudy of 2015 refugee crisis coroborate findings.
www.ifau.se/en/Press/Abs...
www.ifau.se/en/Press/Abs...
IFAU
www.ifau.se
November 22, 2024 at 11:17 AM
New paper out! We find positive effects of exposure to recent migrants on test scores of incumbent students in rural schools, but negative effects in large cities. Class size reductions seem to matter. Eventstudy of 2015 refugee crisis coroborate findings.
www.ifau.se/en/Press/Abs...
www.ifau.se/en/Press/Abs...
Can anyone help me with my #OLS/TWFE #eventstudy #DiD question. I am trying to estimate leads on pre-treatment data in a staggered rollout with unit linear time trends and ending up with a multi-collinearity headache. stats.stackexchange.com/questions/66...
Multicollinearity with Leads and Unit-Specific Linear Time Trends in Panel Regression
I have a panel dataset with outcome variable Y observed for units (unit_id) over time (time). I am estimating the effect of a treatment (treatment) using a Two-Way Fixed Effects (TWFE) regression w...
stats.stackexchange.com
March 26, 2025 at 9:16 AM
Can anyone help me with my #OLS/TWFE #eventstudy #DiD question. I am trying to estimate leads on pre-treatment data in a staggered rollout with unit linear time trends and ending up with a multi-collinearity headache. stats.stackexchange.com/questions/66...
📄 Paper: arxiv.org/abs/2512.06804
📦 R package: ccfang2.github.io/fdid/
Joint work with my PhD student Chencheng Fang — he did a fantastic job.
▶️ ccfang2.github.io
#CausalInference #DifferenceInDifferences #Econometrics #EventStudy #Statistics #RStats #AcademicResearch
📦 R package: ccfang2.github.io/fdid/
Joint work with my PhD student Chencheng Fang — he did a fantastic job.
▶️ ccfang2.github.io
#CausalInference #DifferenceInDifferences #Econometrics #EventStudy #Statistics #RStats #AcademicResearch
Making Event Study Plots Honest: A Functional Data Approach to Causal Inference
Event study plots are the centerpiece of Difference-in-Differences (DiD) analysis, but current plotting methods cannot provide honest causal inference when the parallel trends and/or no-anticipation a...
arxiv.org
January 21, 2026 at 9:29 AM
📄 Paper: arxiv.org/abs/2512.06804
📦 R package: ccfang2.github.io/fdid/
Joint work with my PhD student Chencheng Fang — he did a fantastic job.
▶️ ccfang2.github.io
#CausalInference #DifferenceInDifferences #Econometrics #EventStudy #Statistics #RStats #AcademicResearch
📦 R package: ccfang2.github.io/fdid/
Joint work with my PhD student Chencheng Fang — he did a fantastic job.
▶️ ccfang2.github.io
#CausalInference #DifferenceInDifferences #Econometrics #EventStudy #Statistics #RStats #AcademicResearch
We made two PyPI packages:
• pip install gjr-garch-x — Student-t GJR-GARCH with exogenous regressors in the variance equation.
github.com/studiofarzul...
• pip install robust-eventstudy — Dependence-robust inference for heavy-tailed cross-asset event studies.
github.com/studiofarzul...
• pip install gjr-garch-x — Student-t GJR-GARCH with exogenous regressors in the variance equation.
github.com/studiofarzul...
• pip install robust-eventstudy — Dependence-robust inference for heavy-tailed cross-asset event studies.
github.com/studiofarzul...
GitHub - studiofarzulla/gjr-garch-x: GJR-GARCH models with exogenous variance regressors
GJR-GARCH models with exogenous variance regressors - studiofarzulla/gjr-garch-x
github.com
August 12, 2026 at 9:15 PM
We made two PyPI packages:
• pip install gjr-garch-x — Student-t GJR-GARCH with exogenous regressors in the variance equation.
github.com/studiofarzul...
• pip install robust-eventstudy — Dependence-robust inference for heavy-tailed cross-asset event studies.
github.com/studiofarzul...
• pip install gjr-garch-x — Student-t GJR-GARCH with exogenous regressors in the variance equation.
github.com/studiofarzul...
• pip install robust-eventstudy — Dependence-robust inference for heavy-tailed cross-asset event studies.
github.com/studiofarzul...
CRAN removals: ALSM BCE BHTSpack Biocomb BlackCarbon Blaunet CensMFM Clustering CopulaDTA CSTE CVEK DMLLZU DOBAD EventStudy FAOSTAT FSMUMI GraphPCA HMMcopula HMVD HRM #rstats
April 20, 2024 at 10:02 AM
CRAN removals: ALSM BCE BHTSpack Biocomb BlackCarbon Blaunet CensMFM Clustering CopulaDTA CSTE CVEK DMLLZU DOBAD EventStudy FAOSTAT FSMUMI GraphPCA HMMcopula HMVD HRM #rstats
What's new in version 1.10.0 compared to the previous CRAN release: first of all, the package now provides convenience functions for event study estimates and plots.
May 24, 2026 at 8:00 PM
What's new in version 1.10.0 compared to the previous CRAN release: first of all, the package now provides convenience functions for event study estimates and plots.
One way to benefit adolescents - Marginal REVOLUTION
One way to benefit adolescents - Marginal REVOLUTION
Have school start later: We examine the impact of California’s Senate Bill 328 (SB 328), the first statewide mandate requiring later school start times for middle and high schools, on adolescent sleep, mental health, and academic outcomes. Using difference-in-differences and eventstudy designs across five data sources, we find that SB 328 increased the share of […]
marginalrevolution.com
May 15, 2026 at 4:35 AM
One way to benefit adolescents - Marginal REVOLUTION
Removed from CRAN: CSTE (2.0.0), CVEK (0.1-2), dampack (1.0.1), DAP (1.0), discreteRV (1.2.2), DMLLZU (0.1.1), DOBAD (1.0.6), dual.spls (0.1.4), elaborator (1.1), errint (1.0), EventStudy (0.39.2), FAOSTAT (2.3.0), ffstream (0.1.7.2), FSMUMI (1.0)
April 20, 2024 at 8:15 PM
Removed from CRAN: CSTE (2.0.0), CVEK (0.1-2), dampack (1.0.1), DAP (1.0), discreteRV (1.2.2), DMLLZU (0.1.1), DOBAD (1.0.6), dual.spls (0.1.4), elaborator (1.1), errint (1.0), EventStudy (0.39.2), FAOSTAT (2.3.0), ffstream (0.1.7.2), FSMUMI (1.0)