📊 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.

![MIT License](LICENSE) !DSH Plugin ![Changelog](CHANGELOG.md)

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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

Source facts

Install

Check the source files