Agent skill · AI & Agents

jqte-econometric-methods

Use when the empirical core of a 《数量经济技术经济研究》 (JQTE) manuscript is an econometric model — time series, cointegration, mixed-frequency, VAR/SVAR, state-space, or panel / macro-econometrics. Enforces correct model setup, stationarity / unit-root and cointegration diagnostics, and lag/specification justification. Use when the method itself is the contribution rather than a causal identification claim.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jqte-econometric-methods --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 4 KB
Bundled scripts: none
Path: Journal-of-Quantitative-and-Technological-Economics-Skills/skills/jqte-econometric-methods/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 计量方法(jqte-econometric-methods) ## 触发时机 - 实证主体是时间序列 / VAR / 协整 / 混频 / 状态空间 / 宏观面板 - 模型设定、平稳性、滞后阶交代不清,或被质疑"伪回归" - 方法本身是贡献(把某模型用到中国数据),而非干净因果 ## 设定前必查:数据性质决定模型 | 数据性质 | 必做诊断 | 建模含义 | |----------|----------|----------| | 单一时间序列 | 单位根(ADF/PP/KPSS)、结构突变(Zivot-Andrews/Bai-Perron) | 非平稳需差分或协整建模 | | 多变量时间序列 | 单位根 + 协整(Johansen / EG / ARDL 边界) | 协整存在用 VECM,否则差分 VAR | | 混频数据 | 频率对齐方式 | MIDAS / 桥接模型 / 状态空间 | | 宏观/动态面板 | 截面相关、面板单位根、面板协整 | 跨截面相关需 CCE/CSDL;动态面板用 GMM 并查工具有效性 | ## 各类模型的规范要点 ### 时间序列 / VAR / SVAR - 平稳性先于建模:单位根检验 + 必要的结构突变检验 - 滞后阶用信息准则(AIC/BIC/HQ)选并报告,不凭经验拍 - SVAR 的识别约束(递归/长期/符号约束)显式列出并论证 - 报告脉冲响应、方差分解,必要时给稳定性(伴随矩阵特征根 < 1) ### 协整 / VECM / ARDL - 报告协整秩检验(迹/最大特征根)或 ARDL 边界检验 - 误差修正项符号与显著性、调整速度解读 - 长期与短期关系分开汇报 ### 动态面板 / GMM - 差分/系统 GMM 选择有依据;工具变量个数受控(避免工具过多) - 报告 AR(1)/AR(2) 序列相关检验与 Hansen/Sargan 过度识别检验 - 内生变量、外生变量、工具集划分清楚 ## 执行桥(StatsPAI / Stata MCP) 把设计**跑出来并审计**,而不是只做描述。完整映射见 [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md)。《数量经济技术经济研究》偏计量方法与应用;估计量有效性 + 诊断,必要时附模拟证据。 - `detect_design` → `recommend` → 用 `as_handle=true` 拟合 → `audit_result` 列出尚欠的检查。 - **观察性因果:**交错 DID(`callaway_santanna` / `sun_abraham` + `bacon_decomposition` + `honest_did_fr

What's inside
Steps it walks through
  1. 触发时机
  2. 设定前必查:数据性质决定模型
  3. 各类模型的规范要点
  4. 时间序列 / VAR / SVAR
  5. 协整 / VECM / ARDL
  6. 动态面板 / GMM
  7. 执行桥(StatsPAI / Stata MCP)
  8. 自检清单
  9. 反模式
  10. 本刊审稿期待:方法创新 + 中国应用落地
  11. 微型走查:混频数据预测工业增加值(示意稿件)
  12. 审稿人追问模式 + 本刊语境修法
  13. 校准锚点
  14. 输出格式
More from Awesome-Journal-Skills
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About this skill
What does the jqte-econometric-methods skill do?

Use when the empirical core of a 《数量经济技术经济研究》 (JQTE) manuscript is an econometric model — time series, cointegration, mixed-frequency, VAR/SVAR, state-space, or panel / macro-econometrics. Enforces correct model setup, stationarity / unit-root and cointegration diagnostics, and lag/specification justification. Use when the method itself is the contribution rather than a causal identification claim.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jqte-econometric-methods --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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