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The 2019 Chinese Stata Conference was held on 20–21 August in Shanghai at the Shanghai University of Finance and Economics.

Using Python within Stata
Abstract: Users may extend Stata's features using other programming languages such as Java and C. New in Stata 16, Stata has tight integration with Python, which allows users to embed and execute Python code from within Stata. I will discuss how users can easily call Python from Stata, output Python results within Stata, and exchange data and results between Python and Stata, both interactively and as sub-routines within do-files and ado-files. I will also show examples of the Stata Function Interface (sfi); a Python module provided with Stata which provides extensive facilities for accessing Stata objects from within Python.

Additional information:
china19_Peng (https:)

Hua Peng
Stata's application in corporate investment and financing research
Abstract: Investment and financing behavior is important for a company. What are the research topics of the company's investment and financing research so far? What empirical techniques or problems are involved in mainstream investment and financing research topics? How does Stata solve these technologies or problems? What are the future trends?

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Yan Jiaqi
Nankai University
Quantile regression: Cross section, panel, and instrumental variables methods
Abstract: Quantile regression has an increasingly widespread use in economics, finance, and social sciences. This presentation begins with the overall quantile and sample quantile. It then introduces the basic cross-sectional quantile regression, the most advanced panel quantile and quantile instrumental-variable methods, and the corresponding Stata operations and cases.

Additional information:

Qiang Chen
Shandong University
Using lasso and related estimators for prediction
Abstract: Lasso and elastic net are two popular machine-learning methods. In this presentation, I discuss Stata 16's new features for lasso and elastic net. I will demonstrate how they can be used for prediction with linear, binary, and count outcomes. I will then show why these methods are effective and how they work.

Additional information:

Di Liu
Inference after lasso model selection
Abstract: 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
Nonparametric economic approach (kernel regression, local linear regression)
Abstract: Model error setting is a common problem in econometric analysis, and nonparametric and semiparametric methods are estimated to have robust and elastic features. This presentation introduces the estimation and setting test of nonparametric and semiparametric econometric models such as kernel regression and local linear regression, including local estimation and global estimation, and the application of these methods in Stata.

Additional information:

Wang Qunyong
Nankai University
Roundtable: Discussion of user needs
Fixed-effect panel threshold model for unbalanced panel
Abstract: The current fixed-effect panel-data threshold model is applicable only to the balance panel, which may cause large-sample selection bias when converting the unbalanced panel to the balance panel. Based on the current xthreg command, we propose an improved command, xthreg2, that uses the cluster wild bootstrap to estimate the fixed-effect threshold model of unbalanced panel data. In this presentation, I use the Monte Carlo simulation method to investigate the effective sample size of clustering wild bootstrap in different situations.

Additional information:

Wang Qunyong
Nankai University
Stata's application in the foreign exchange market
Abstract: In this presentation, I will discuss how to use several of Stata's key commands to change the frequency of time-series data in the Forex market. Then, I will illustrate how to obtain panel data comparing the exchange rate system of various countries, several key commands in Stata focus on how to obtain the actual system of "objects gathered together". Get the reviewers impression with the best Stata "puzzles".

Additional information:

Jianping Ding
Shanghai University of Finance and Economics
Artificial intelligence and Stata
Abstract: In the era of big data, the amount of data is getting bigger and bigger, and the types of data are becoming more and more abundant. How to deal with unstructured data such as images, sounds, and texts is a major challenge for econometric researchers. With the help of Microsoft's cloud-based artificial intelligence platform, Stata users can use powerful algorithms to complete the above tasks in just a few lines of code, transform unstructured data into structured data, and introduce it into econometric models to help users produce the results of scientific research.

Additional information:

Chen Yuping
Microsoft China
A quick tour of the new reporting features in Stata 16
Abstract: Stata's commands for report generation allow you to create complete Word, Excel, PDF, and HTML documents that include formatted text, as well as summary statistics, regression results, and graphs produced by Stata. In this talk, I will go over the new features in Stata 16 for generating reproducible reports.
Hua Peng
Developer forum: Technical discussion exchange


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Logistics organizer

The logistics organizer for the 2019 Chinese Stata Conference Shanghai is Beijing Uone Info&Tech Co., Ltd. (Uone-Tech).

View the proceedings of previous Stata Users Group meetings.