Introduction

Econometric research usually involves more than one step: first, determining which model fits the research question, then handling missing values, outliers, and variable encoding, followed by selecting control variables, estimating models, performing robustness checks, and finally organizing descriptive statistics and regression tables. If these steps are scattered across manual scripts, code snippets, and report templates, it is difficult for an agent to form a continuous workflow.

dsh-econ-tools is a DSH plugin maintained by Chaos-Hyper, licensed under MIT. Its positioning is as an “Econometrics Research Assistant,” providing a full suite of tools for model design, data cleaning, variable selection, empirical analysis, and robustness analysis. Once installed, the Agent can automatically invoke corresponding capabilities based on research needs.

What is it

One-sentence positioning: dsh-econ-tools packages common steps in econometrics research into DSH plugin capabilities, covering method guidance, data preparation, model specification and variable selection, empirical analysis, robustness checks, and result reporting.

It is suitable for the following scenarios:

  • Developers using the DSH web profile
  • Researchers needing the Agent to participate in econometric model selection, code template generation, and result table organization
  • Teams wishing to connect “from research question to result report”

It should be noted that the community directory in the DSH plugin ecosystem is an independent site with no official affiliation to DeepSeek / Hyperbolic; do not interpret this plugin as an official component in the official application store.

Core Features

Method Guidance

Recommends appropriate econometric models based on the research question and data type. This step usually occurs during the research design phase, used to determine which estimation methods are suitable for the current data structure and causal problem.

Data Preparation

Supports missing value handling, outlier detection, variable transformation, and categorical encoding, and provides Python code snippets.

Model Specification and Variable Selection

Supports theory-driven, data-driven, and hybrid strategies, as well as machine learning-based LASSO / Ridge / ElasticNet methods.

Empirical Analysis

Supports OLS, IV/2SLS, Logit, Probit, Panel fixed effects, DID, RDD, and automatically generates Python / R / Stata code templates.

Robustness Checks

Covers seven dimensions: omitted variables, measurement error, sample selection, model specification, outliers, parallel trends, and placebo tests.

Result Reporting

Generates descriptive statistics tables, baseline regression tables, and robustness check summaries. Supports Markdown, LaTeX, and HTML, and supports both Chinese and English output.

Installation and Usage

Before installation, confirm that the current DSH environment satisfies the dependency scope declared by the plugin:

{
  "react": "^18.2.0",
  "@deepseek-ai/dsh-tools": ">=0.0.1-rc.1 <0.1.0 || >=0.1.0-rc.1 <0.2.0-0"
}

Then use the official installation command to add the plugin to the DSH web profile:

dsh plugin --profile web add github:Chaos-Hyper/dsh-econ-tools

In addition to GitHub installation, local path installation is also supported, or manual editing of the web profile dependencies. If installed via local path or manual dependency addition, you need to complete dependency installation according to the project instructions and restart DSH for the plugin to take effect.

Typical Usage

The Agent will automatically invoke tools based on research needs. The following two examples are from the project documentation.

Example 1: Researching the impact of education on income using cross-sectional data.

Research Question: What model should be used to study the impact of education on income using cross-sectional data?

The Agent will call econ_method_guide to recommend methods such as OLS and IV based on the research question and data type.

Example 2: Checking for potential omitted variable bias.

Research Question: Please check if the core results have potential omitted variable bias.

The Agent will call econ_robustness and provide solutions such as the Oster test.

Applicable Scenarios and Notes

Suitable for:

  • Developers who need to integrate econometric research workflows into DSH
  • Researchers who need to automatically generate method suggestions, code templates, and result reports
  • Teams wishing to connect model selection, data preparation, empirical estimation, and robustness checks

Notes:

  • The plugin runs with the permissions of the current dsh process. You should check the source code, dependencies, and license before installation.
  • The license is MIT.
  • The corresponding version ranges of react ^18.2.0 and @deepseek-ai/dsh-tools need to be satisfied.
  • The plugin needs to be restarted to take effect after installation.
  • Model suggestions, code templates, and reports output by the plugin should still be subject to manual review based on specific research questions, data constraints, and software environments.

Conclusion

The value of dsh-econ-tools lies in incorporating common steps in econometrics research into DSH plugin capabilities, reducing the cost of repeatedly switching between model selection, data preparation, estimation, robustness checks, and result reporting.

Project Address:

  • GitHub: https://github.com/Chaos-Hyper/dsh-econ-tools