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UID:20250814T164604EDT-8957GsbNhr@132.216.98.100
DTSTAMP:20250814T204604Z
DESCRIPTION:Harold Chiang (Vanderbilt)\n	Date: January 13\, 2020\n	Time and L
 ocation: 10:00 a.m.\, Leacock 429\n\nAbstract:\n	We study estimation\, poin
 twise and simultaneous inference\, and confidence intervals for many avera
 ge partial effects of lasso Logit. Focusing on high-dimensional cluster-sa
 mpling environments\, we propose a new average partial effect estimator an
 d explore its asymptotic properties. Practical penalty choices compatible 
 with our asymptotic theory are also provided. The proposed estimator allow
  for valid inference without requiring oracle property. We provide easy-to
 -implement algorithms for cluster-robust high-dimensional hypothesis testi
 ng and construction of simultaneously valid confidence intervals using a m
 ultiplier cluster bootstrap. We apply the proposed algorithms to the text 
 regression model of Wu (2018) to examine the presence of gendered language
  on the internet\n
DTSTART:20200113T150000Z
DTEND:20200113T163000Z
LOCATION:Leacock 429\, Leacock Building\, CA\, QC\, Montreal\, H3A 2T7\, 85
 5 rue Sherbrooke Ouest
SUMMARY:Harold Chiang (Vanderbilt)\, “Many Average Partial Effects: with an
  Application to Text Regression”  
URL:/economics/channels/event/harold-chiang-vanderbilt
 -many-average-partial-effects-application-text-regression-305545
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