Template-Type: ReDIF-Paper 1.0
Title: Linear models and related using Stata: More speed and more inference
File-URL: http://repec.org/chin2025/China25_Pinzon.pdf
Author-Name: Enrique Pinzón
Author-Workplace-Name: StataCorp LLC
Abstract: In the last three releases, we have upgraded the most commonly used linear models in Stata, namely, areg, regress, and xtreg, fe. Some of the improvements are for speed and convenience. Some have added new options to compute valid standard errors and confidence intervals for cases in which traditional computations will underperform. And some have added estimators that leverage this technology. In this talk, I will go through these upgrades and present the theoretical results that motivate them.
Creation-Date: 20250711
Handle: RePEc:boc:chin25:01

Template-Type: ReDIF-Paper 1.0
Title: Mobile share instrumental variables and Stata application
File-URL: http://repec.org/chin2025/China25_Chen.html
Author-Name: Chen Qiang
Author-Workplace-Name: Shandong University
Abstract: The shift share instrument, also known as the “Bartik IV”, has become increasingly popular in empirical research in recent years and has made breakthroughs in theoretical research. This talk will combine classic papers to introduce the historical development and latest results of the shift share instrument, as well as the corresponding Stata practical examples.
Creation-Date: 20250711
Handle: RePEc:boc:chin25:02

Template-Type: ReDIF-Paper 1.0
Title: Conditional average treatment effects estimation using Stata
File-URL: http://repec.org/chin2025/China25_Liu.pdf
Author-Name: Di Liu
Author-Workplace-Name: StataCorp LLC
Abstract: Treatment effects estimate the causal effects of a treatment on an outcome. The effect may be heterogeneous. Average treatment effects conditional on a set of variables (CATEs) help us understand heterogeneous treatment effects, and, by construction, are useful to evaluate how different treatment-assignment policies affect different groups in the population. In this talk, I will show how to use Stata's new cate command to answer questions such as the following:
Are the treatment effects heterogeneous?
How do the treatment effects vary with some variables?
Do the treatment effects vary across prespecified groups?
Are there unknown groups in the data for which treatment effects differ?
Which is best among possible treatment-assignment rules?
Creation-Date: 20250711
Handle: RePEc:boc:chin25:03

Template-Type: ReDIF-Paper 1.0
Title: Causal inference in machine learning and Stata application
File-URL: http://repec.org/chin2025/China25_Wang.pdf
Author-Name: Wang Qunyong
Author-Workplace-Name: Nankai University
Abstract: This presentation introduces the application of machine learning causal inference in economics, including dual machine learning estimation, inference and evaluation of conditional (heterogeneous) treatment-effect models (local linear models and interaction models), dual machine learning causal inference (DML instrumental variables, DML breakpoint design, etc.), and policy learning methods.
Creation-Date: 20250711
Handle: RePEc:boc:chin25:04