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The Chinese Stata Conference took place on 16 August 2020 in cooperation with the Wuhan University School of Economics and Management.


8:45–9:45 Fusion application of Stata and Python Abstract: Fusion application with Python is a new feature of Stata 16. It allows Stata to run Python programs freely. I will demonstrate the possibilities of expanding Stata and Python through this series of examples.

Additional information:
China20_Peng (https:)

Hua Peng
StataCorp LLC
9:45–10:30 Link Stata to Chinese maps Abstract: I will introduce the principal methodology with which Stata reads the geographic information from Baidu Maps and Gaode Maps, as well as some map commands we developed.
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cngcode converts Chinese addresses to latitude and longitude. cnaddress converts latitude and longitude to Chinese iconic geographical locations. cntraveltime can calculate the traffic distance between two geographical locations (you can even choose different traffic modes). cnmapsearch can search for various geographical keywords such as subway stations, hospitals, cafes, barbecue stalls, etc., within a few kilometers of a given location. These commands are convenient for empirical research on geography and transportation and have broad application prospects in finance, economics, and sociology.
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Li Chuntao
Zhongnan University of Economics and Law
10:40–12:00 Using Stata 16's lasso features for prediction and inference Abstract: Lasso and elastic net are two popular machine-learning methods. In this presentation, I discuss Stata 16's new lasso features for prediction and inference.
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I will demonstrate how lasso-type techniques can be used for prediction with linear, binary, and count outcomes. I will then show why these methods are effective and how they work. The increasing availability of high-dimensional data and increasing interest in more realistic functional forms have sparked a renewed interest in automated methods for selecting the covariates to include in a model. I discuss the promises and perils of model selection and pay special attention to some new estimators that provide reliable inference after model selection.
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Di Liu
12:00–12:40 Visualization and modeling method of epidemic data of COVID-19 Abstract: Since COVID-19 was found in 2019, the global spread has attracted extensive attention. I will summarize basic methods for visualization of epidemic data and explore the temporal and spatial characteristics of the global spread of COVID-19.
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I will then introduce a modeling method for forecasting the epidemic trend of COVID-19 so as to better adjust the policy of the epidemic prevention and control of the situation. Finally, I will summarize the matters needing attention in the epidemic data analysis of the situation.
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Additional information:

Xiao Guangen
Wuhan University


Registration is closed.

Logistics organizer

The 2020 Chinese Stata Conference is jointly organized by Beijing Tianyan Rongzhi Software Co., Ltd. (TurnTech), an official reseller of Stata in China, and the Wuhan University School of Economics and Management.

View the proceedings of previous Stata Conferences and Users Group meetings.