Chencheng Fang
@ccfang819.bsky.social
PhD Researcher in Econometrics and Statistics at Uni Bonn
Reposted by Chencheng Fang
Event-study plots in DiD are incredibly persuasive—but, honestly, not always honest.
Why?
If parallel trends or no anticipation fail, DiD estimates are biased. Testing against a zero-effect null then becomes misleading.
🚨New 📄: arxiv.org/abs/2512.06804
#CausalInference #EconSky #StatsSky #rstats
Why?
If parallel trends or no anticipation fail, DiD estimates are biased. Testing against a zero-effect null then becomes misleading.
🚨New 📄: arxiv.org/abs/2512.06804
#CausalInference #EconSky #StatsSky #rstats
January 21, 2026 at 9:29 AM
Event-study plots in DiD are incredibly persuasive—but, honestly, not always honest.
Why?
If parallel trends or no anticipation fail, DiD estimates are biased. Testing against a zero-effect null then becomes misleading.
🚨New 📄: arxiv.org/abs/2512.06804
#CausalInference #EconSky #StatsSky #rstats
Why?
If parallel trends or no anticipation fail, DiD estimates are biased. Testing against a zero-effect null then becomes misleading.
🚨New 📄: arxiv.org/abs/2512.06804
#CausalInference #EconSky #StatsSky #rstats