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8:45–9:45 Creating custom estimation tables Abstract: After fitting several regression models, researchers are often interested in creating a regression table for the regression results to examine the variables or statistics of interest.
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This presentation introduces how to create an estimation table using Stata’s new command etable and how to customize the look of the table using collect.
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Mia Lv
9:45–10:05 cnevent: Event study is so simple Abstract: cnevent is an event research command newly developed by the Stata and Python club.
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Using this command, users only need to provide event lists. cnevent will obtain the transaction data and index data required for event research through the network and automatically calculate the cumulative excess return according to the market model set by users.
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Chuntao Li
Henan University
10:05–10:30 Reporting empirical results to .DOCX Abstract: Reporting empirical results automatically to generate structured tables is an important but time-consuming task for empirical researchers.
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Because of the lack of commands that could effectively create and edit Office Open XML documents (a .docx document), neither official modules nor community-contributed commands could tabulate results to this commonly used type of document until the launching of putdocx in Stata 15. This presentation introduces four new commands based on putdocx. They are sum2docx, corr2docx, t2docx, and reg2docx. With these commands, we can report summary statistics, correlation coefficient matrices, split-sample t-tests, and regression results automatically to a single .docx file. These commands are simple and flexible to use, and they provide researchers with new choices when reporting empirical results.
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Yuan Xue
Huazhong University of Science and Technology
10:40–11:30 Markov chain Monte Carlo simulation (MCMC) and Stata application Abstract: Markov chain Monte Carlo simulation (MCMC) is the core sampling method of the Bayesian statistical and econometric model that is used to approximate the distribution of unknown forms through random samples.
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I will introduce the concept of MCMC, Metroplis–Hasting and Gibbs algorithms, the statistics of sampling results, and the diagnosis of sampling quality. The application methods of Metroplis–Hasting sampling and Gibbs sampling in Stata are introduced through microeconometric model and macro time-series model.
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Qunyong Wang
Nankai University
11:30–12:20 Panel ARDL model and its application Abstract: The panel autoregressive distribution lag model (Panel ARDL model) is mainly used to estimate the long-term relationship between variables.
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In time-series analysis, this model is mainly used to analyze the long-term and short-term relationships between the nonstationary series with cointegration relationships. In the panel data, we can better control various fixed effects and consider spatial correlation and heterogeneity to analyze the impact of a policy or a variable with slow change (such as climate and p) on economic growth, innovation, trade, and other outcome variables.
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Yujun Lian
Sun Yat-sen University
12:20–12:40 Open panel discussion with Stata developers
Contribute to the Stata community by sharing your feedback with StataCorp's developers. From feature improvements to bug fixes and new ways to analyze data, we want to hear how Stata can be made better for our users.

Logistics organizer

The 2022 Chinese Stata Conference is jointly organized by Beijing Tianyan Rongzhi Software Co., Ltd. (TurnTech), an official reseller of Stata in China, and the Henan University School of Economics.

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





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