Template-Type: ReDIF-Paper 1.0 Title: Heterogeneous difference in differences in Stata File-URL: http://repec.org/chin2023/China23_Liu.pdf Author-Name: Di Liu Author-Workplace-Name: StataCorp LLC Abstract: We are interested in obtaining causal answers to our research questions. We want the effect of a treatment on an outcome. When studying causal questions with repeated cross-sections or panel data, it is common for treatment timing to differ across groups. When this occurs, treatment effects may be heterogeneous across groups and time. Failing to account for effect heterogeneity will lead to inconsistent estimates. We show how to use heterogeneous difference in differences to estimate, visualize, infer, and aggregate heterogeneous treatment effects. Creation-Date: 20241002 Handle: RePEc:boc:chin23:01 Template-Type: ReDIF-Paper 1.0 Title: Control variables in causal inference: The good and the bad File-URL: http://repec.org/chin2023/China23_Yujun.pdf Author-Name: Lian Yujun Author-Workplace-Name: Sun Yat-sen University Abstract: The traditional concept of "the more control variables, the higher the accuracy of model identication" has misled many people. This presentation is based on the theory of causal identication, uses causal diagrams to explain "good control variables" and "bad control variables" and denes the conditions for "good control variables", and then uses the classic application examples in the top issue to illustrate the control variables. This presentation discusses issues of selection, measurement, robustness testing, and sensitivity analysis. Creation-Date: 20241002 Handle: RePEc:boc:chin23:02 Template-Type: ReDIF-Paper 1.0 Title: Double machine learning and Stata application File-URL: http://repec.org/chin2023/China23_Qiang.pdf Author-Name: Chen Qiang Author-Workplace-Name: Shandong University Abstract: Traditional methods for estimating treatment effects generally assume strong functional forms and are only applicable when the covariates are low-dimensional data. However, using machine learning methods directly often leads to "regularization bias". The recently emerging "double/debiased machine learning" provides an effective estimation method without assuming a functional form and is suitable for high-dimensional data. This presentation will introduce the principles of dual machine learning in a simple way and demonstrate the corresponding Stata operations with classic cases. Creation-Date: 20241002 Handle: RePEc:boc:chin23:03 Template-Type: ReDIF-Paper 1.0 Title: DID placebo test and Stata application File-URL: http://repec.org/chin2023/China23_Guanpeng.zip Author-Name: Yan Guanpeng Author-Workplace-Name: Shandong University Abstract: he parallel trends assumption on which differences in differences (DID) relies is inherently untestable. For this reason, recent empirical studies have increasingly used placebo tests to further examine the robustness of the estimated results. This presentation will comprehensively sort out various types of DID placebo tests and classic cases and introduce the new Stata command didplacebo for DID placebo tests. This command can automatically perform the time and space placebo test of DID and provide a visual display. Creation-Date: 20241002 Handle: RePEc:boc:chin23:04 Template-Type: ReDIF-Paper 1.0 Title: Create customizable tables File-URL: http://repec.org/chin2023/China23_Xu.pdf Author-Name: Zhao Xu Author-Workplace-Name: StataCorp LLC Abstract: Customizable tables allow researchers to effectively and clearly present their analysis results to others. Stata versions 17 and 18 introduced commands such as table, collect, etable, and dtable to help users create standard and customizable tables using results from Stata's estimation and postestimation commands, summary statistics, and hypothesis testing. Additionally, those tables can be easily exported to various le formats, including Microsoft Word/Excel, PDF, LaTeX, and HTML. In this presentation, I will show you how to create various customized tables conveniently using those commands. Creation-Date: 20241002 Handle: RePEc:boc:chin23:05 Template-Type: ReDIF-Paper 1.0 Title: Stata and accounting research: Capital market openness and financial report robustness File-URL: http://repec.org/chin2023/China23_Shangkun.pdf Author-Name: Liang Shangkun Author-Workplace-Name: Central University of Finance and Economics Abstract: Based on the exogenous policy changes implemented by the “Shanghai–Hong Kong Stock Connect”, this presentation explores the impact and mechanism of the opening of the capital market on the accounting conservatism of enterprises. The research found that the implementation of the Shanghai–Hong Kong Stock Connect has signicantly reduced the accounting conservatism of the target enterprise and increased the introduction of foreign investors. The impact of “communication” on the reduction of accounting conservatism is more signicant in low-governance and state-owned enterprises. It is comprehensively shown that the implementation of the Shanghai–Hong Kong Stock Connect will affect a company's decision-making function through regulatory changes and the introduction of foreign investors, which will affect a company's disclosure strategy. Creation-Date: 20241002 Handle: RePEc:boc:chin23:06 Template-Type: ReDIF-Paper 1.0 Title: Instrumental variables quantile regression File-URL: http://repec.org/chin2023/China23_Liu_IVQ.pdf Author-Name: Di Liu Author-Workplace-Name: StataCorp LLC Abstract: When we want to study the effects of covariates on the different quantiles of the outcome, we use quantile regression. However, the traditional quantile regression is inconsistent when a covariate is endogenous. We introduce the Stata command ivqregress, which models the quantiles of the outcome and, at the same time, controls for problems that arise from endogeneity. We show how to use the suite of IV quantile regresison to estimate, visualize, and infer features of the outcome distribution. Creation-Date: 20241002 Handle: RePEc:boc:chin23:07 Template-Type: ReDIF-Paper 1.0 Title: Comparative review of intervention time-series analysis and program package File-URL: http://repec.org/chin2023/China23_Wang.pdf Author-Name: Qunyong Wang Author-Workplace-Name: Nankai University Abstract: Intervention time-series analysis (ITSA) can describe the dynamic changes of policy effects, and the exibility of policy effects is the characteristic of ITSA that distinguishes it from experimental designs such as DID and RD. This presentation reviews two classes of models for intervention time-series analysis: linear regression models with deterministic trends and transfer function ARIMA models. I also introduce the functions and features of Stata's itsa command and compare it with Mathematica's itsa package. Creation-Date: 20241002 Handle: RePEc:boc:chin23:08