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The Chinese Stata Conference takes place on 19–20 August 2021. There will also be a Stata Summer Training Camp offered 16–18 and 21–23 August.

The conference will provide Stata users from across China and the world the opportunity to exchange ideas, experiences, and information on new applications of the software. Everybody who is interested in using Stata is welcome. The conference language will be English because of the international nature and the participation of non-Chinese guest speakers.

Preliminary program

Day 1: Wednesday, 19 August

9:00–10:00 Introduction to customizable tables in Stata 17 Abstract: This presentation will introduce the new customizable tables in Stata 17. I will discuss how to use the new table and collect commands to collect, organize, manipulate, and display the results from Stata commands to create customizable tables.
Hua Peng
StataCorp
10:20–11:20 Global VAR and Bayesian VAR in Stata Abstract: When applying the VAR model to panel data from multiple countries or regions, the cross-sectional correlation and excessive parameters should be considered. Global VAR and Bayesian VAR are commonly used methods for fitting this large-scale VAR model. This article introduces the new Stata commands for
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these two VAR models, gvar and bvar. The program has various built-in prior distributions such as Litterman, Normal–Wishart, and Giannone–Lenza–Primeceri, as well as Gibbs and MH sampling.
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Wang Qunyong
Nankai University
11:20–2:00 Break
2:00–3:00 Quantile control method and Stata application Abstract: The causal inference of panel data is becoming increasingly popular, including synthetic control methods, regression control methods, etc., but it is difficult to obtain standard errors, p-values, or confidence intervals. Starting from the regression control method, we propose the quantile control method using quantile
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regression forests (that is, through random forests for quantile regression) to construct the treatment effect of each period confidence interval. Monte Carlo simulation shows that the confidence interval constructed using the quantile control method performs well in a limited sample. Finally, through the community-contributed command, the specific operation of the quantile control method is introduced in detail with a classic case.
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Chen Qiang
Shandong University
3:20–4:20 Regression control method and Stata application Abstract: The regression control method (Hsiao, Ching, and Wan 2012) has become an important method for evaluating policy effects using panel data. This presentation will introduce the basic principles of the regression control method, including the use of information criteria or lasso to select cross-sectional units, and the addition
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of covariates to the regression control method. Then, through the community-contributed command, the specific operation of the regression control method is introduced in detail with classic cases, including the perfect drawing function and placebo test.
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Yan Guanpeng
Shandong University
4:40–5:10 Application of Stata in causal inference Abstract: Causal inference is a simple question often raised spontaneously by human instinct: Why? In fact, as the core and purpose of scientific research, causal inference is serious thinking, scientific verification, or causal analysis on this issue. At present, causal inference models and methods have been valued and favored in
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the fields of economics, finance, sociology, management, demography, and public health, and have become the core weapon of empirical research. But causal inference is not easy. As John Dewey said, "Scientific principles and laws do not lie on the surface of nature. They are hidden, and must be wrested from nature by an active and elaborate technique of inquiry." There is no doubt that causal inference models and methods are the core weapon for modern social science researchers to publish high-level empirical papers using quantitative models. My presentation focuses on how to use Stata software for causal inference. In the seminars and demonstrations, the causal inference model is organically combined with the application and operation of statistical software, and I try to share the basic ideas, principles, models, methods, and scope of application of causal inference with you in a relatively short period of time and use real data as the basis. I also demonstrate cases and share the ability of causal inference, model construction, software application, and result interpretation, laying a foundation for publishing high-level empirical research papers.
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Wang Cuntong
Central University of Finance and Economics

Day 2: Thursday, 20 August

9:00–10:00 Some thoughts of a Stata user Abstract: I started using Stata 8.0 in 2003. Now Stata has been upgraded to 17, and I am deeply emotional. I have taught thousands of students and have collaborated with more than 200 students from various universities to write tweets and have some understanding of the puzzles that everyone faces in study and research. I would like to
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share some thoughts I have had over the years, hoping to be helpful to everyone's study and research work. This talk mainly includes the following issues:
  • The integration of Stata and Python, R, and other software/languages.
  • The tradeoff between measurement theory and Stata practical operation.
  • How to integrate resources and reduce the learning cost to Stata users (lianxh and songbl commands).
  • What should I learn?
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Lian Yujun
Sun Yat-sen University
10:20–11:20 Stata application and cutting-edge research of synthetic control method Abstract: The synthetic control method for causal inference has become one of the mainstream methods in empirical research in the past 10 years, but there are still many controversies about this method in statistical inference. This presentation will be divided into two parts. The first part will start with the classic practical cases of
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synthetic control method combined with the relevant Stata commands, introduce how to use this method for specific economic empirical scenarios, and test the relevant hypotheses of the method. The second part will systematically sort out the current controversies in the synthetic control method and briefly introduce how the current econometrics community solves the shortcomings of the method, such as the use of machine learning algorithms to construct counterfactuals (Viviano and Bradic 2019) and the use of ranking test methods realizing the construction of confidence intervals (Chernozhukov et al. 2020).
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Lu Jiaxuan
University of Chicago
11:20–2:00 Break
2:00–3:00 Stata application of bilateral stochastic boundary model Abstract: The bilateral stochastic boundary model has been widely used in welfare analysis, efficiency estimation, and corporate finance. In recent years, the existing literature has made a series of expansions in model setting, including different distribution assumptions and methods to reduce the influence of distribution assumptions. Our team has
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implemented an estimation method based on the seminormal distribution and scaling property and has developed a new Stata command. This presentation will introduce the specific operation of the command by reproducing the classic literature and comparing the estimation results set by different models.
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Liu Chang
Sun Yat-sen University
3:20–3:30 Giveaway: One-year Stata/SE 17 license
3:40–4:40 Mixed regression with macrodata and microdata in Stata Abstract: In a type of model, the dependent variable is a macro variable and the independent variable is a micro variable. How to use data-driven methods to weight independent variables is always the core issue of this type of model. This presentation draws on the method of mixing regression (midasreg) and introduces
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Stata's new program mixedreg, which is called mixed regression. This presentation also introduces how to use the microdata of listed companies to predict macroeconomic indicators through mixedreg.
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Wang Qunyong
Nankai University
4:50–5:20
Open panel discussion with Stata developers
StataCorp
5:20–5:30
Closing
Xu Qingqing
Youwan Technology

Stata Summer Training Camp

Course name

Bayesian Analysis and Stata Application

Presenter

Wang Qunyong

Date & Time

16–18 August 2021 from 9:00 a.m. to 5:00 p.m.

Description

This training covers Monte Carlo simulation, Markov chain Monte Carlo simulation, Bayesian analysis of regression models, Bayesian VAR models, Bayesian analysis of microeconometric models, Bayesian structural equation models, and writing your own likelihood functions and prior distribution.

Course name

Panel-Data Metrological Analysis and Stata Application

Presenter

Wang Qunyong

Date & Time

21–23 August 2021 from 9:00 a.m. to 5:00 p.m.

Description

This training covers classic models of panel data, dynamic panel, nonlinear panel model, using panel data for causal inference, panel-data restricted dependent variable models, Bayesian panel data analysis, and panel space measurement tools.

Registration

Conference fees
(VAT not incl.)
Conference only (early bird) 600元
Bayesian analysis Summer Training Camp 3,600元
Panel data Summer Training Camp 3,600元

Register online

Visit the official conference page for more information.

Logistics organizer

The 2021 Chinese Stata Conference is organized by Beijing Uone Info & Tech Co., Ltd. (Uone-Tech), an official reseller of Stata in China.

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


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