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X-WR-CALDESC:Events for IHSS
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20221117T090000
DTEND;TZID=Asia/Hong_Kong:20221117T090000
DTSTAMP:20261010T081708
CREATED:20240613T195003Z
LAST-MODIFIED:20260416T103253Z
UID:83905-1668675600-1668675600@ihss.hku.hk
SUMMARY:Qing Officialdom: Insights from Historical Big Data
DESCRIPTION:Language:  English\n									\n								\n											\n													\n										Via Zoom\n									\n								\n											\n													\n										November 12\, 2024 (Tue) 12:30 – 13:30 HKT (Nov 11 | 20:30 – 21:30 PST)\n									\n								\n											\n\n												\n													\n										ihss@hku.hk\n											\n									\n								\n											\n\n												\n													\n										Register Now\n											\n									\n						\n						\n				\n				\n				\n							\n			\n						\n		\n						\n				\n					\n				\n		\n					\n				\n				\n					ABSTRACT				\n				\n							\n						\n				\n									Economists are increasingly interested in the ‘very long run\,’ yet research on the distant past has been hampered by the scarcity of economic data available for many centuries ago. Meredith Paker of Grinnell College and her co-authors introduce a new framework to peer into the past despite the scarcity of data in earlier periods. They use state-of-the-art machine learning predictive modeling methods and best-practice forecasting techniques to develop robust predictions of historical economic time series\, treating the past as the prediction window. To demonstrate the usefulness of this new method\, they apply this framework to the seven centuries of English wage data at the heart of debates on long-run living standards. Their new approach generates predictions with improved accuracy and generalizability compared to existing regression-based interpolations. This new long-run wage series deviates from established estimates with the largest variation at key points of economic and social change. In this Quantitative History Webinar\, Meredith Paker will discuss the implications for our understanding of long-run economic development. Meredith Paker’s co-authors:  Judy Stephenson (University College London) and Patrick Wallis (The London School of Economics and Political Science)  Discussant: Yuqi Chen\, PhD candidate\, Department of History\, Peking University (will join the Centre of Quantitative History as a Postdoctoral Fellow in December 2024)  								\n				\n				\n				\n					ABOUT THE SPEAKER				\n				\n				\n				\n									Gary Chi-hung Luk is an Assistant Professor in the Department of History\, Chinese University of Hong Kong. He is a historian of Hong Kong\, late imperial and modern China\, and the Asian maritime world\, with particular interests in empires and colonialism\, frontiers and borderlands\, and race and ethnicity. 								\n				\n				\n				\n					ABOUT THE SERIES				\n				\n				\n				\n									The Quantitative History (QH) Webinar Series aims to provide researchers\, teachers\, and students with an online intellectual platform to keep up to date with the latest research in the field\, promoting the dissemination of research findings and interdisciplinary use of quantitative methods in historical research. The QH Webinar Series\, now entering its fifth year\, is co-organized by the Centre for Quantitative History at the HKU Business School and the International Society for Quantitative History in partnership with the Hong Kong Institute for the Humanities and Social Sciences. The Series is now substantially supported by the Areas of Excellence (AoE) Scheme from the Research Grants Council of the Hong Kong Special Administrative Region\, China (Project No. [AoE/B-704/22-R]). 量化歷史網上講座系列由香港大學陳志武和馬馳騁教授聯合發起，旨在介紹前沿量化歷史研究成果、促進同仁交流，推廣量化方法在歷史研究中的應用。本系列講座由香港大學經管學院量化歷史研究中心和國際量化歷史學會承辦，及香港人文社會研究所全力支持。從2023年開始，系列得到中國香港特別行政區研究資助局卓越學科領域計劃的重要資助 (項目編號[AoE/B-704/22-R])。 Conveners: Professors Zhiwu Chen & Chicheng Ma (HKU Business School)
URL:https://ihss.hku.hk/events/qing-officialdom-insights-from-historical-big-data/
CATEGORIES:Quantitative History,Quantitative History Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20221027T090000
DTEND;TZID=Asia/Hong_Kong:20221027T090000
DTSTAMP:20261010T081708
CREATED:20240613T220154Z
LAST-MODIFIED:20260416T103254Z
UID:83928-1666861200-1666861200@ihss.hku.hk
SUMMARY:The Rise and Fall of Imperial China
DESCRIPTION:Language:  English\n									\n								\n											\n													\n										Via Zoom\n									\n								\n											\n													\n										November 12\, 2024 (Tue) 12:30 – 13:30 HKT (Nov 11 | 20:30 – 21:30 PST)\n									\n								\n											\n\n												\n													\n										ihss@hku.hk\n											\n									\n								\n											\n\n												\n													\n										Register Now\n											\n									\n						\n						\n				\n				\n				\n							\n			\n						\n		\n						\n				\n					\n				\n		\n					\n				\n				\n					ABSTRACT				\n				\n				\n				\n									Economists are increasingly interested in the ‘very long run\,’ yet research on the distant past has been hampered by the scarcity of economic data available for many centuries ago. Meredith Paker of Grinnell College and her co-authors introduce a new framework to peer into the past despite the scarcity of data in earlier periods. They use state-of-the-art machine learning predictive modeling methods and best-practice forecasting techniques to develop robust predictions of historical economic time series\, treating the past as the prediction window. To demonstrate the usefulness of this new method\, they apply this framework to the seven centuries of English wage data at the heart of debates on long-run living standards. Their new approach generates predictions with improved accuracy and generalizability compared to existing regression-based interpolations. This new long-run wage series deviates from established estimates with the largest variation at key points of economic and social change. In this Quantitative History Webinar\, Meredith Paker will discuss the implications for our understanding of long-run economic development. Meredith Paker’s co-authors:  Judy Stephenson (University College London) and Patrick Wallis (The London School of Economics and Political Science)  Discussant: Yuqi Chen\, PhD candidate\, Department of History\, Peking University (will join the Centre of Quantitative History as a Postdoctoral Fellow in December 2024)  								\n				\n				\n				\n					ABOUT THE SPEAKER				\n				\n				\n				\n									Gary Chi-hung Luk is an Assistant Professor in the Department of History\, Chinese University of Hong Kong. He is a historian of Hong Kong\, late imperial and modern China\, and the Asian maritime world\, with particular interests in empires and colonialism\, frontiers and borderlands\, and race and ethnicity. 								\n				\n				\n				\n					ABOUT THE SERIES				\n				\n				\n				\n									The Quantitative History (QH) Webinar Series aims to provide researchers\, teachers\, and students with an online intellectual platform to keep up to date with the latest research in the field\, promoting the dissemination of research findings and interdisciplinary use of quantitative methods in historical research. The QH Webinar Series\, now entering its fifth year\, is co-organized by the Centre for Quantitative History at the HKU Business School and the International Society for Quantitative History in partnership with the Hong Kong Institute for the Humanities and Social Sciences. The Series is now substantially supported by the Areas of Excellence (AoE) Scheme from the Research Grants Council of the Hong Kong Special Administrative Region\, China (Project No. [AoE/B-704/22-R]). 量化歷史網上講座系列由香港大學陳志武和馬馳騁教授聯合發起，旨在介紹前沿量化歷史研究成果、促進同仁交流，推廣量化方法在歷史研究中的應用。本系列講座由香港大學經管學院量化歷史研究中心和國際量化歷史學會承辦，及香港人文社會研究所全力支持。從2023年開始，系列得到中國香港特別行政區研究資助局卓越學科領域計劃的重要資助 (項目編號[AoE/B-704/22-R])。 Conveners: Professors Zhiwu Chen & Chicheng Ma (HKU Business School)
URL:https://ihss.hku.hk/events/the-rise-and-fall-of-imperial-china/
CATEGORIES:Quantitative History,Quantitative History Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20221020T090000
DTEND;TZID=Asia/Hong_Kong:20221020T090000
DTSTAMP:20261010T081708
CREATED:20240613T224642Z
LAST-MODIFIED:20260416T103304Z
UID:83952-1666256400-1666256400@ihss.hku.hk
SUMMARY:The Journey of Humanity: The Origin of Wealth and Inequality
DESCRIPTION:Language:  English\n									\n								\n											\n													\n										Via Zoom\n									\n								\n											\n													\n										November 12\, 2024 (Tue) 12:30 – 13:30 HKT (Nov 11 | 20:30 – 21:30 PST)\n									\n								\n											\n\n												\n													\n										ihss@hku.hk\n											\n									\n								\n											\n\n												\n													\n										Register Now\n											\n									\n						\n						\n				\n				\n				\n							\n			\n						\n		\n						\n				\n					\n				\n		\n					\n				\n				\n					ABSTRACT				\n				\n				\n				\n									Economists are increasingly interested in the ‘very long run\,’ yet research on the distant past has been hampered by the scarcity of economic data available for many centuries ago. Meredith Paker of Grinnell College and her co-authors introduce a new framework to peer into the past despite the scarcity of data in earlier periods. They use state-of-the-art machine learning predictive modeling methods and best-practice forecasting techniques to develop robust predictions of historical economic time series\, treating the past as the prediction window. To demonstrate the usefulness of this new method\, they apply this framework to the seven centuries of English wage data at the heart of debates on long-run living standards. Their new approach generates predictions with improved accuracy and generalizability compared to existing regression-based interpolations. This new long-run wage series deviates from established estimates with the largest variation at key points of economic and social change. In this Quantitative History Webinar\, Meredith Paker will discuss the implications for our understanding of long-run economic development. Meredith Paker’s co-authors:  Judy Stephenson (University College London) and Patrick Wallis (The London School of Economics and Political Science)  Discussant: Yuqi Chen\, PhD candidate\, Department of History\, Peking University (will join the Centre of Quantitative History as a Postdoctoral Fellow in December 2024)  								\n				\n				\n				\n					ABOUT THE SPEAKER				\n				\n				\n				\n									Gary Chi-hung Luk is an Assistant Professor in the Department of History\, Chinese University of Hong Kong. He is a historian of Hong Kong\, late imperial and modern China\, and the Asian maritime world\, with particular interests in empires and colonialism\, frontiers and borderlands\, and race and ethnicity. 								\n				\n				\n				\n					ABOUT THE SERIES				\n				\n				\n				\n									The Quantitative History (QH) Webinar Series aims to provide researchers\, teachers\, and students with an online intellectual platform to keep up to date with the latest research in the field\, promoting the dissemination of research findings and interdisciplinary use of quantitative methods in historical research. The QH Webinar Series\, now entering its fifth year\, is co-organized by the Centre for Quantitative History at the HKU Business School and the International Society for Quantitative History in partnership with the Hong Kong Institute for the Humanities and Social Sciences. The Series is now substantially supported by the Areas of Excellence (AoE) Scheme from the Research Grants Council of the Hong Kong Special Administrative Region\, China (Project No. [AoE/B-704/22-R]). 量化歷史網上講座系列由香港大學陳志武和馬馳騁教授聯合發起，旨在介紹前沿量化歷史研究成果、促進同仁交流，推廣量化方法在歷史研究中的應用。本系列講座由香港大學經管學院量化歷史研究中心和國際量化歷史學會承辦，及香港人文社會研究所全力支持。從2023年開始，系列得到中國香港特別行政區研究資助局卓越學科領域計劃的重要資助 (項目編號[AoE/B-704/22-R])。 Conveners: Professors Zhiwu Chen & Chicheng Ma (HKU Business School)
URL:https://ihss.hku.hk/events/the-journey-of-humanity-the-origin-of-wealth-and-inequality/
CATEGORIES:Quantitative History,Quantitative History Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20221006T160000
DTEND;TZID=Asia/Hong_Kong:20221006T160000
DTSTAMP:20261010T081708
CREATED:20240613T225615Z
LAST-MODIFIED:20260416T103305Z
UID:83975-1665072000-1665072000@ihss.hku.hk
SUMMARY:唐代中國是否出現過牡丹泡沫? (“Peony Bubble” in the Tang Dynasty)
DESCRIPTION:Language:  English\n									\n								\n											\n													\n										Via Zoom\n									\n								\n											\n													\n										November 12\, 2024 (Tue) 12:30 – 13:30 HKT (Nov 11 | 20:30 – 21:30 PST)\n									\n								\n											\n\n												\n													\n										ihss@hku.hk\n											\n									\n								\n											\n\n												\n													\n										Register Now\n											\n									\n						\n						\n				\n				\n				\n							\n			\n						\n		\n						\n				\n					\n				\n		\n					\n				\n				\n					ABSTRACT				\n				\n				\n				\n									Economists are increasingly interested in the ‘very long run\,’ yet research on the distant past has been hampered by the scarcity of economic data available for many centuries ago. Meredith Paker of Grinnell College and her co-authors introduce a new framework to peer into the past despite the scarcity of data in earlier periods. They use state-of-the-art machine learning predictive modeling methods and best-practice forecasting techniques to develop robust predictions of historical economic time series\, treating the past as the prediction window. To demonstrate the usefulness of this new method\, they apply this framework to the seven centuries of English wage data at the heart of debates on long-run living standards. Their new approach generates predictions with improved accuracy and generalizability compared to existing regression-based interpolations. This new long-run wage series deviates from established estimates with the largest variation at key points of economic and social change. In this Quantitative History Webinar\, Meredith Paker will discuss the implications for our understanding of long-run economic development. Meredith Paker’s co-authors:  Judy Stephenson (University College London) and Patrick Wallis (The London School of Economics and Political Science)  Discussant: Yuqi Chen\, PhD candidate\, Department of History\, Peking University (will join the Centre of Quantitative History as a Postdoctoral Fellow in December 2024)  								\n				\n				\n				\n					ABOUT THE SPEAKER				\n				\n				\n				\n									Gary Chi-hung Luk is an Assistant Professor in the Department of History\, Chinese University of Hong Kong. He is a historian of Hong Kong\, late imperial and modern China\, and the Asian maritime world\, with particular interests in empires and colonialism\, frontiers and borderlands\, and race and ethnicity. 								\n				\n				\n				\n					ABOUT THE SERIES				\n				\n				\n				\n									The Quantitative History (QH) Webinar Series aims to provide researchers\, teachers\, and students with an online intellectual platform to keep up to date with the latest research in the field\, promoting the dissemination of research findings and interdisciplinary use of quantitative methods in historical research. The QH Webinar Series\, now entering its fifth year\, is co-organized by the Centre for Quantitative History at the HKU Business School and the International Society for Quantitative History in partnership with the Hong Kong Institute for the Humanities and Social Sciences. The Series is now substantially supported by the Areas of Excellence (AoE) Scheme from the Research Grants Council of the Hong Kong Special Administrative Region\, China (Project No. [AoE/B-704/22-R]). 量化歷史網上講座系列由香港大學陳志武和馬馳騁教授聯合發起，旨在介紹前沿量化歷史研究成果、促進同仁交流，推廣量化方法在歷史研究中的應用。本系列講座由香港大學經管學院量化歷史研究中心和國際量化歷史學會承辦，及香港人文社會研究所全力支持。從2023年開始，系列得到中國香港特別行政區研究資助局卓越學科領域計劃的重要資助 (項目編號[AoE/B-704/22-R])。 Conveners: Professors Zhiwu Chen & Chicheng Ma (HKU Business School)
URL:https://ihss.hku.hk/events/%e5%94%90%e4%bb%a3%e4%b8%ad%e5%9c%8b%e6%98%af%e5%90%a6%e5%87%ba%e7%8f%be%e9%81%8e%e7%89%a1%e4%b8%b9%e6%b3%a1%e6%b2%ab-peony-bubble-in-the-tang-dynasty/
CATEGORIES:Quantitative History,Quantitative History Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20220929T100000
DTEND;TZID=Asia/Hong_Kong:20220929T100000
DTSTAMP:20261010T081708
CREATED:20240613T230228Z
LAST-MODIFIED:20260416T103305Z
UID:83982-1664445600-1664445600@ihss.hku.hk
SUMMARY:Strengthening State Capacity: Postal Reform and Innovation during the Gilded Age
DESCRIPTION:Language:  English\n									\n								\n											\n													\n										Via Zoom\n									\n								\n											\n													\n										November 12\, 2024 (Tue) 12:30 – 13:30 HKT (Nov 11 | 20:30 – 21:30 PST)\n									\n								\n											\n\n												\n													\n										ihss@hku.hk\n											\n									\n								\n											\n\n												\n													\n										Register Now\n											\n									\n						\n						\n				\n				\n				\n							\n			\n						\n		\n						\n				\n					\n				\n		\n					\n				\n				\n					ABSTRACT				\n				\n				\n				\n									Economists are increasingly interested in the ‘very long run\,’ yet research on the distant past has been hampered by the scarcity of economic data available for many centuries ago. Meredith Paker of Grinnell College and her co-authors introduce a new framework to peer into the past despite the scarcity of data in earlier periods. They use state-of-the-art machine learning predictive modeling methods and best-practice forecasting techniques to develop robust predictions of historical economic time series\, treating the past as the prediction window. To demonstrate the usefulness of this new method\, they apply this framework to the seven centuries of English wage data at the heart of debates on long-run living standards. Their new approach generates predictions with improved accuracy and generalizability compared to existing regression-based interpolations. This new long-run wage series deviates from established estimates with the largest variation at key points of economic and social change. In this Quantitative History Webinar\, Meredith Paker will discuss the implications for our understanding of long-run economic development. Meredith Paker’s co-authors:  Judy Stephenson (University College London) and Patrick Wallis (The London School of Economics and Political Science)  Discussant: Yuqi Chen\, PhD candidate\, Department of History\, Peking University (will join the Centre of Quantitative History as a Postdoctoral Fellow in December 2024)  								\n				\n				\n				\n					ABOUT THE SPEAKER				\n				\n				\n				\n									Gary Chi-hung Luk is an Assistant Professor in the Department of History\, Chinese University of Hong Kong. He is a historian of Hong Kong\, late imperial and modern China\, and the Asian maritime world\, with particular interests in empires and colonialism\, frontiers and borderlands\, and race and ethnicity. 								\n				\n				\n				\n					ABOUT THE SERIES				\n				\n				\n				\n									The Quantitative History (QH) Webinar Series aims to provide researchers\, teachers\, and students with an online intellectual platform to keep up to date with the latest research in the field\, promoting the dissemination of research findings and interdisciplinary use of quantitative methods in historical research. The QH Webinar Series\, now entering its fifth year\, is co-organized by the Centre for Quantitative History at the HKU Business School and the International Society for Quantitative History in partnership with the Hong Kong Institute for the Humanities and Social Sciences. The Series is now substantially supported by the Areas of Excellence (AoE) Scheme from the Research Grants Council of the Hong Kong Special Administrative Region\, China (Project No. [AoE/B-704/22-R]). 量化歷史網上講座系列由香港大學陳志武和馬馳騁教授聯合發起，旨在介紹前沿量化歷史研究成果、促進同仁交流，推廣量化方法在歷史研究中的應用。本系列講座由香港大學經管學院量化歷史研究中心和國際量化歷史學會承辦，及香港人文社會研究所全力支持。從2023年開始，系列得到中國香港特別行政區研究資助局卓越學科領域計劃的重要資助 (項目編號[AoE/B-704/22-R])。 Conveners: Professors Zhiwu Chen & Chicheng Ma (HKU Business School)
URL:https://ihss.hku.hk/events/strengthening-state-capacity-postal-reform-and-innovation-during-the-gilded-age/
CATEGORIES:Quantitative History,Quantitative History Webinar Series
END:VEVENT
END:VCALENDAR