许文立 | Wenli Xu

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机构：澳门城市大学金融学院

我专注于中国宏观经济政策、DSGE建模与AI经济学研究，也对因果推断（尤其是DID及其经验应用）感兴趣，欢迎交流合作。

Wenli Xu

Welcome to my homepage

Affiliation: Faculty of Finance, City University of Macau

I focus on China's macroeconomic policy, DSGE modeling, and AI economics, and I am also interested in causal inference, especially DID and empirical applications. Collaboration is welcome.

研究方向

• 中国宏观经济政策
• DSGE建模
• AI经济学
• 因果推断（尤其是DID及其经验应用）

Research Interests

• China's Macroeconomic Policy
• DSGE Modeling
• AI Economics
• Causal Inference (especially DID and empirical applications)

代表成果（节选）

以下论文聚焦中国宏观经济、因果推断与政策评估，涵盖DSGE建模、DID及相关经验研究方法。

• Patronage and pollution (JEEM, 2025)

  摘要：研究“关系网络”在不同晋升激励下对污染排放的影响，发现将环保纳入考核后，关系网络由促污转为减污，但伴随一定增长权衡。

• Television exposure in early childhood and subsequent cognitive outcomes (JEBO, 2025)

  摘要：基于中国农村数据识别早期电视接触对后续认知的影响，结果显示0-5岁阶段的完整电视暴露显著提升10-15岁认知得分。

• Impact of ICT development on China's carbon emissions (Journal of Innovation & Knowledge, 2025)

  摘要：ICT发展在空间上表现为“本地增排、周边减排、总体减排”，并可通过技术创新与产业升级间接促进减排。

• Baby Boom (SSRN, 2024)

  摘要：利用县志手工数据和三重差分方法评估家庭联产承包责任制对生育率的影响，发现改革显著提高生育，尤其体现在二孩及以上。

• Environmental regulation and energy efficiency (Journal of Regulatory Economics)

  摘要：以“按日连续处罚”政策为准自然实验，发现该政策显著提升企业能源效率，并同时带来环境与产出双重改善。

• Do VAT cuts help stabilize employment? (Economic Analysis and Policy)

  摘要：围绕增值税税率改革进行DID分析，发现减税并不必然稳就业；非劳动要素相对价格下降会强化“资本替代劳动”机制。

• A Baseline Medium-Scale NK DSGE Model for Policy Analysis

  摘要：构建中等规模新凯恩斯DSGE基准框架，用于联合分析货币政策与财政政策冲击及其传导机制。

• A Baseline DSGE Model of Climate Change for Climate Policy Analysis

  摘要：在NK-DSGE中引入碳循环与气候模块，比对税、总量控制等多类气候政策对宏观经济与环境变量的动态影响。

Selected Publications

These papers focus on China's macroeconomy, causal inference, and policy evaluation, including DSGE modeling, DID, and applied empirical methods.

• Patronage and pollution (JEEM, 2025)

  Abstract: This paper studies how patronage affects emissions under different promotion incentives, showing that environmental KPIs can turn patronage from pollution-increasing to pollution-reducing.

• Television exposure in early childhood and subsequent cognitive outcomes (JEBO, 2025)

  Abstract: Using rural China data, the study finds that full TV exposure during ages 0-5 significantly improves cognitive outcomes at ages 10-15.

• Impact of ICT development on China's carbon emissions (Journal of Innovation & Knowledge, 2025)

  Abstract: ICT development has heterogeneous spatial effects and can reduce aggregate emissions through technological innovation and industrial upgrading channels.

• Baby Boom (SSRN, 2024)

  Abstract: With manually collected county-level records and a triple-difference design, the paper shows that the HRS reform raised fertility, especially for higher-order births.

• Environmental regulation and energy efficiency (Journal of Regulatory Economics)

  Abstract: The Daily Penalty Policy is shown to improve firm energy efficiency and deliver both environmental and economic gains.

• Do VAT cuts help stabilize employment? (Economic Analysis and Policy)

  Abstract: DID evidence suggests VAT rate cuts may reduce employment when cheaper non-labor inputs increase substitution away from labor.

• A Baseline Medium-Scale NK DSGE Model for Policy Analysis

  Abstract: A medium-scale NK DSGE baseline is developed for joint monetary-fiscal policy analysis and impulse-response-based interpretation.

• A Baseline DSGE Model of Climate Change for Climate Policy Analysis

  Abstract: The model extends NK DSGE with geophysical blocks to compare macro and environmental dynamics under multiple climate policy regimes.

Packages

这些工具包面向政策评估与因果推断实证工作，覆盖Python与Stata环境。

• Python package: lwdid-py

  概要：用于DID估计与应用流程实现，便于在Python中开展政策处理效应评估与结果复现。

• Stata package: pretest

  概要：用于处理前趋势等关键前提检验，帮助在DID应用中快速完成可视化与统计诊断。

• Stata package: equitrends

  概要：用于趋势可比性与均衡趋势检验，支持更系统地评估平行趋势相关假设。

• Stata package: diddesign

  概要：用于DID研究设计构建与诊断，支持处理分配机制、识别假设与实证流程规范化。

Packages

These packages support applied causal inference and policy evaluation in both Python and Stata.

• Python package: lwdid-py

  Summary: A Python toolkit for DID estimation workflows, useful for treatment-effect evaluation and reproducible empirical analysis.

• Stata package: pretest

  Summary: Provides pre-treatment diagnostics (including trend checks) for DID applications with quick statistical and visual assessment.

• Stata package: equitrends

  Summary: Supports balanced-trend and comparability checks to strengthen parallel-trend diagnostics.

• Stata package: diddesign

  Summary: Supports DID design setup and diagnostics, including identification assumptions and implementation workflow checks.

书籍

专著

• 推动绿色发展：中国环境宏观经济学

  简介：围绕中国绿色转型与宏观政策协同，系统讨论环境约束下的增长机制与政策框架。

• The Effect Book

  简介：聚焦因果效应识别与实证策略，面向政策评估与应用研究提供方法与案例。

译著

• 量化宏观经济学导论及Julia应用

  简介：介绍量化宏观经济学核心思想，并结合Julia展示模型求解与模拟的实践路径。

• Python因果推断手册（中文版）

  简介：从实证流程出发讲解Python中的因果推断工具，覆盖DID等常见识别方法。

Book

Monographs

• Promoting Green Development: China's Environmental Macroeconomics

  Summary: This book discusses China's green transition and macro policy coordination under environmental constraints.

• The Effect Book

  Summary: A practical reference on causal effect identification and empirical strategies for policy evaluation.

Translations

• Introduction to Quantitative Macroeconomics and Julia Applications

  Summary: Introduces core quantitative macroeconomics methods with practical Julia-based modeling workflows.

• Python Causality Handbook (Chinese Translation)

  Summary: A hands-on guide to causal inference in Python, covering common designs including DID.

链接与联系

• GitHub
• 澳门城市大学金融学院个人主页
• wlxu@cityu.edu.mo
• 澳门城市大学个人主页（英文）

Links & Contact

• GitHub
• My FOF Profile (CN)
• wlxu@cityu.edu.mo
• My City University of Macau Profile (EN)