📊 dsh-econ-tools — Econometrics Research Assistant

English | 中文

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.

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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-tools

Or 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 install

Restart 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 outliers
• variable_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

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License

MIT