This presentation introduces the new Stata commands for 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.

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.

Starting from the regression control method, we propose the quantile control method using quantile 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.

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.

A semiparametric Cox proportional hazards regression model is used routinely to analyze uncensored and right-censored event-time data. It is also appealing for interval-censored data because it does not require any parametric assumptions about the baseline hazard function. Also, under the proportional-hazards assumption, the hazard ratios are constant over time.

Semiparametric estimation of interval-censored event-time data is challenging because none of the event times are observed exactly. Thus, "semiparametric" modeling of these data often resorted to using spline methods or piecewise-exponential models for the baseline hazard function. Genuine semiparametric modeling of interval-censored event-time data was not available until recent methodological advances, which are implemented in the stintcox command.

Join Xiao Yang, Principal Statistician and Software Developer, as she describes basic types of interval-censored data and demonstrates how to fit the semiparametric Cox proportional hazards model to these data using Stata's new stintcox command. She will also discuss how to interpret and plot results and how to graphically assess proportional-hazards assumptions.

Our team has 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.

I would like to 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?

The first part will start with the classic practical cases of 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).

This presentation draws on the method of mixing regression (midasreg) and introduces 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.