📊 dsh-econ-tools — Econometrics Research Assistant
English | [中文](README.zh.md)
> A DeepSeek Harness plugin providing 6 ready-to-use econometrics tools covering the full research workflow: method selection, data preparation, model specification, empirical analysis, robustness checks, and result reporting.
 !DSH Plugin 
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Feature Overview
| Tool | Function | Use Case |
|---|---|---|
🎯 econ_method_guide | Method Guide — Recommend appropriate econometric models based on research question and data type | Research design stage, unsure which model to use |
🧹 econ_data_prep | Data Preparation — Missing value handling, outlier detection, variable transformation, categorical encoding, with Python code snippets | Cleaning raw data before analysis |
⚙️ econ_model_spec | Model Specification & Variable Selection — Theory-driven, data-driven, hybrid, and ML-based (LASSO/Ridge/ElasticNet) strategies, with diagnostic checklists | Selecting core variables and controls |
🔬 econ_run_analysis | Empirical Analysis — Supports OLS, IV/2SLS, Logit, Probit, panel FE, DID, RDD; auto-generates Python/R/Stata code templates with interpretation guidance | Running regressions, interpreting results |
🛡️ econ_robustness | Robustness Checks — Omitted variables, measurement error, sample selection, model specification, outliers, parallel trends, placebo tests — 7 dimensions | Verifying whether core findings are reliable |
📝 econ_report | Result Reporting — Generate descriptive statistics tables, baseline regression tables, and robustness check summaries in Markdown / LaTeX / HTML, bilingual (CN/EN) | Writing papers, formatting result tables |
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Quick Start
Installation
#### Option 1: From GitHub (Recommended)
Install directly via the dsh CLI:
dsh plugin --profile web add github:Chaos-Hyper/dsh-econ-tools#### Option 2: Local File Installation
If you already have the source directory, install by path:
dsh plugin --profile web add /path/to/dsh-econ-toolsOr manually add it to the web profile dependencies (edit ~/.dsh/profiles/web/package.json):
"dependencies": {
"dsh-econ-tools": "link:/path/to/dsh-econ-tools"
}Then add "dsh-econ-tools" to the dsh.profile.bundles array, and run:
cd ~/.dsh/profiles/web
pnpm installRestart DSH for the changes to take effect.
Usage
The Agent will automatically call the appropriate tool based on your research needs. For example:
> "I want to study the impact of education on income using cross-sectional data. What model should I use?" > → Agent calls econ_method_guide, recommending OLS, IV methods, etc.
> "Run robustness checks for potential omitted variable bias." > → Agent calls econ_robustness, providing Oster stability test and other solutions.
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Tool Details
1. econ_method_guide
Parameters:
research_goal: Research goal (causal inference / prediction / policy evaluation)dependent_type: Dependent variable type (continuous / binary / panel)data_structure: Data structure (cross-section / time series / panel)endogeneity_concern: Whether endogeneity is a concern (optional)
Sample output:
{
"recommended_models": ["OLS", "DID"],
"methodology_notes": ["Run model diagnostics", "Use robust standard errors"],
"next_tools": ["econ_data_prep", "econ_model_spec", "econ_run_analysis"]
}2. econ_data_prep
Parameters:
missing_rate: Missing data proportion (none / low / moderate / high)outlier_concern: Whether to address outliersvariable_types: Variable types (continuous / categorical / dummy)need_transformation: Whether variable transformation is needed
Output includes Python code: KNNImputer for missing values, Winsorize for outliers.
3. econ_model_spec
Four strategies:
| Strategy | Method | Best For |
|---|---|---|
| Theory-driven | Core model based on economic theory, add controls stepwise | Replication studies |
| Data-driven | Stepwise regression + AIC/BIC | Many candidates, weak theory |
| Hybrid | Theory screening → data-driven → LASSO review | Most empirical research |
| ML-based | LASSO / Ridge / Elastic Net / Random Forest | High-dimensional data, prediction |
4. econ_run_analysis
Supported models: OLS, IV/2SLS, Logit, Probit, Panel FE, DID, RDD
Auto-generated code:
- Python:
statsmodels+ robust SE - R:
fixest+lmtest+sandwich - Stata:
reg+robust
5. econ_robustness
Seven dimensions:
| Dimension | Key Methods |
|---|---|
| Omitted variables | Oster (2019) stability test, Altonji-Elder-Taber ratio |
| Measurement error | Alternative variable estimation, IV correction |
| Sample selection | Heckman two-stage, PSM |
| Model specification | Functional form change, quantile regression, Bootstrap |
| Outliers | Winsorize 1%/5%, trim extremes, M-estimation |
| Parallel trends | Event study plot, placebo treatment time, permutation test |
| Placebo test | Random treatment assignment, fictitious treatment time |
6. econ_report
Report types:
- Descriptive statistics table (Table 1)
- Baseline regression table (Table 2, with significance stars, controls, FE, R² footnotes)
- Robustness checks summary (Table 3)
- Full research summary (all three tables)
Formats: Markdown, LaTeX, HTML
Languages: Chinese, English
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Suggested Workflow
econ_method_guide → Determine research method and model
↓
econ_data_prep → Clean and preprocess data
↓
econ_model_spec → Specify model, select variables
↓
econ_run_analysis → Run regression analysis
↓
econ_robustness → Verify result robustness
↓
econ_report → Generate result report---
License
MIT