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The Chinese Stata Conference took place on 19–20 August 2020. There was also a Stata Summer Training Camp offered 17–18 and 21–22 August.

Proceedings

9:15–10:15 Using Stata to obtain and process COVID-19 data Abstract: Rapid data acquisition and analysis are the bases for public health decisions. I will show various data acquisition toolkits in Stata and their applications in acquiring and processing COVID-19 data.

Additional information:
China20_Peng (https:)

Hua Peng
StataCorp
9:15–10:15 Call Stata from Python Abstract: Stata 16 introduces tight integration with Python, allowing users to embed and execute Python code from within Stata. In this presentation, I will demonstrate a new functionality we have been working on—calling Stata from within Python. We are working on
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providing two ways to let users interact with Stata from within Python: the IPython magic commands and a suite of API functions. With those utilities, you will be able to run Stata conveniently from Python environments, such as Jupyter Notebook/console, Jupyter Lab/console, Spyder IDE, or Python launched from a Windows Command Prompt, Unix terminal, etc.
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Additional information:
China20_Xu (http:)

Zhao Xu
StataCorp
9:15–10:15 Mixing regression methods and Stata applications Abstract: The sampling frequency of common variables is not uniform. For example, GDP is often quarterly, while inflation rate is monthly, and financial market data are often daily. Mixing data regression is a method that uses low-frequency variables to regress high-frequency
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variables and uses a data-driven method to assign weights to high-frequency variables in different periods to improve accurate prediction. It is more and more widely used in macroeconomic forecasting. This presentation introduces the mixing regression model and Stata's new command: midasreg. This command allows regression of data of a variety of different frequencies, includes multiple weighting functions such as STEP, PDL, and Beta, and is compatible with Stata's standard commands such as estat and predict.
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Additional information:
China20_Wang_Q (1).pdf

Wang Qunyong
Nankai University
9:15–10:15 Source of endogenity based on Stata simulation and its response Abstract: Although endogeneity is a common topic, it is still easily overlooked in the modeling process. Through a unified framework and simple Stata program simulation, I show how self-selection bias, simultaneous causality, missing variables, measurement errors, etc.,
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lead to endogeneity. I combine easy-to-understand cases to show the conditions under which control variables, difference estimation, instrumental variables, breakpoint regression, matching, and other methods are used. I also show how to overcome endogenity and obtain consistent estimates.
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Additional information:
China20_Chen_C.pdf

Chen Chuanbo
Renmin University of China
9:15–10:15 Smooth conversion model and Stata application Abstract: The smooth transition model describes the transition relationship between two or more states between variables, and the states are realized by a smooth transition function. This model is widely used in macroeconomics and financial markets, for example, oil prices and
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economic fluctuations, banking and economic development, etc. This presentation introduces the setting and related tests of the smooth transition model, and Stata's new command: stregress. This command is suitable for time series, cross-sectional data, and panel data, and includes multiple conversion functions such as LSTR, ESTR, NSTR, and L2STR. This presentation also includes multiple tests such as linearity of the model, serial correlation of residuals, and parameter-constant characteristics.
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Additional information:
China20_Wang_Q (2).pdf

Wang Qunyong
Nankai University
9:15–10:15 Span regression, regression, skewness, and kurtosis regression in Stata applications Abstract: Quantile regression is a powerful tool to study the effects of covariates on key quantiles of conditional distribution. Yet we often still lack a general picture about how covariates affect the overall shape of conditional distribution. Using quantile-based measures of
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spread, skewness, and kurtosis, we propose spread regression, skewness regression, and kurtosis regression as empirical tools to quantify the effects of covariates on the spread, skewness and kurtosis of conditional distribution. While spread regression can be implemented by the official Stata command iqreg, I provide the new commands skewreg and kurtosisreg for skewness regression and kurtosis regression, respectively, and illustrate them with an example of US wage data with substantive findings.
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Additional information:
China20_Chen_Q.pdf

Chen Qiang
Shandong University
9:15–10:15 Causal mediation Abstract: The estimation of the effect of intervention or exposure is a common effect evaluation and an important statistical analysis aspect of causal inference. In order to further understand the mechanism of action or the path of action to improve intervention strategies, the
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mediation effect has become an important research aspect of social development disciplines and epidemiological research, and has formed different schools. Causal mediation starts from the counterfactual framework, dealing with exposure-mediating confounding, exposure-outcome confounding, and sensitivity testing of mediating-outcome confounding.
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Additional information:
China20_Jin.pdf

Jin Chenggang
Beijing Normal University
9:15–10:15 On improvement of placebo test of synthetic control method—statistical inference based on standardized treatment effect and nonrefusal domain Abstract: The statistical inference of the synthetic control method mainly relies on the "placebo test" with the permutation test as the basic idea, but this method has serious problems of excessive rejection and significance pursuit. This presentation uses "quasi-standardized"
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conversion to punish the noise component in the placebo test process to avoid the inconsistency of the policy effect distribution after the intervention to ensure that we can implement the placebo test without deleting the observations. The above improvements can overcome the excessive rejection and saliency chasing problems faced by traditional statistics. The study found that (1) the standardization of placebo test can effectively reduce the heteroskedasticity of random shocks and the excessive rejection caused by estimation bias; (2) the standardized treatment of the placebo test can make the treatment group and the distribution of policy effects of potential control groups meet consistency and avoids the problem of chasing significant results; (3) based on the standardized processing results, we can construct the "nonrejection domain" of policy effects by using the bootstrap method under relatively clean data conditions. This ensures that the statistical inference framework of the synthetic control method is consistent with traditional statistical inference.
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Additional information:
China20_Lian.pdf

Lian Yujun
Sun Yat-sen University
9:15–10:15 Measuring technical efficiency and total factor productivity change with undesirable outputs in Stata Abstract: In recent years, considering undesirable output in efficiency and productivity analysis has become an important research direction. Corresponding to this, in the field of efficiency and productivity analysis, many new models that can consider both desirable and
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undesirable output have gradually developed. This presentation shares the nonparametric frontier estimation methods and applications of efficiency and productivity, as well as the new Stata commands teddf and gtfpch, cowritten with Du Kerui from Xiamen University. These commands are suitable for estimating the efficiency and productivity of decision-making units with undesirable output and cover a variety of nonparametric frontier models based on directional distance functions.
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Additional information:
China20_Wang_D.pdf

Daoping Wang
Shanghai University of Finance and Economics

Stata Summer Training Camp

Course name

Nonlinear models tell extraordinary stories

Presenter

Wang Qunyong

Date & Time

17–18 August 2020 from 9:00 a.m. to 5:00 p.m.

Description

This training mainly introduces the basic ideas and algorithms of nonlinear models, combined with specific cases, and introduces practical operation of Stata. Stata 16 is used throughout the demonstration.

Course name

Natural experiment and causal inference

Presenter

Wang Qunyong

Date & Time

21–22 August 2020 from 9:00 a.m. to 5:00 p.m.

Description

Causal inference is one of the important characteristics of micro-metric analysis. Counterfactuals are the most basic framework for causal inference. Policy endogeneity caused by self-selection and other issues has brought many obstacles to policy evaluation. This course introduces several tools to use observational data to infer causality: how to use natural experiments (or quasi-experiments) to infer causality, how to overcome policy endogenous problems, how breakpoint design weakens policy endogenous problems and model error settings problems, and how to use double check and synthetic control to construct counterfactual strategies in the panel data.

Registration

Registration is closed.

Logistics organizer

The 2020 Chinese Stata Conference is jointly organized by Beijing Uone Info & Tech Co., Ltd. (Uone-Tech), an official reseller
of Stata in China, and the Institute of Quantitative Economics, Nankai University.

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