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.

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.

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.

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.

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.

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.

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.

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.