Update about.md

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@@ -18,13 +18,14 @@ WORKING PAPERS
 
   The common practice to include firm fixed effects in empirical research may eliminate the explanatory power of important economic factors that are persistent. We use the intuitive R&Dpatent relation to illustrate this point. Our review of recent studies suggests a surprising pattern that R&D input only positively explains patent output in half of prior regression estimations. This "missing link" can be attributed to the persistence of R&D and patents that causes the between-firm variation to be absorbed by firm fixed effects. We consider adjusted Hausman-Taylor estimates and advanced machine learning methods, and find that both methods lead to a clear positive R&Dpatent relation. In particular, advanced machine learning methods suggest that only 10-20% of firm dummies are informative for the R&D-patent relation and that including other non-informative firm dummies may bias the identification. This paper thus offers two ready-to-use econometric methods to serve as a "second opinion" for empirical researchers working with explanatory variables that strongly correlate with between-individual unobservables.
 
-<!-- - The SFS Cavalcade Asia-Pacific 2024; The 2024 UC Davis-FMA Napa Finance Conference; The 23rd Taiwan Symposium on Innovation Economics and Entrepreneurship; NTU; Max Planck I&E Seminar\*; The 16th NYCU Finance Conference Keynote Speech\*; Academia Sinica\*\*; The 2024 FMA Asia Pacific Conference\*\*. (Presented by \*Po-Hsuan Hsu).-->
+ - The SFS Cavalcade Asia-Pacific 2024; The 2024 UC Davis-FMA Napa Finance Conference; The 23rd Taiwan Symposium on Innovation Economics and Entrepreneurship; NTU; Max Planck I&E Seminar\*; The 16th NYCU Finance Conference Keynote Speech\*; Academia Sinica\*\*; The 2024 FMA Asia Pacific Conference\*\*. (Presented by \*Po-Hsuan Hsu).
+
 _What Share of Patents Is Commercialized?_ (with Po-Hsuan Hsu, You-Na Lee, and John. P Walsh)
 
 This paper applies machine learning and advanced natural language processing techniques to estimate the probabilities of commercial use of patents, over time at scale. We combine three surveys of inventors reporting on US patents as independently labeled training data, and use a combination of contextual embedding codings of the patent text and bibliometric indicators from the patent documents to develop machine learning models to predict the probabilities of commercial use for patented inventions.
 
+  -  TPRI Brownbag Seminar\*; NBER Productivity Seminar\*; Max Planck I&E Seminar\*; TES 2023; Academia Sinica; NTU; NTPU; YZU (\*Presented by John. P Walsh)
 
- <!--   -  TPRI Brownbag Seminar\*; NBER Productivity Seminar\*; Max Planck I&E Seminar\*; TES 2023; Academia Sinica; NTU; NTPU; YZU (\*Presented by John. P Walsh)
  _Machine Learning in Hedge Fund Classification: Systematic vs. Discretionary Strategies and Their Performance Implications_ (with Chung-Ming Kuan) [\[ssrn\]](<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3912348>){:target="_blank"}
 
 This paper applies machine learning to classify hedge funds into systematic and discretionary