Econometrics MCP server for regression, causal inference, time series, panel data, and statistical analysis workflows.
概览
Econometrics MCP server for regression, causal inference, time series, panel data, machine learning, and broader statistical analysis workflows. aigroup-econ-mcp is a professional econometrics-oriented MCP server designed to help AI assistants and MCP clients perform structured quantitative analysis. - parameter estimation and regression analysis - causal inference workflows - microeconometrics and panel data - time series and volatility models - machine learning for econometric tasks - spatial econometrics, decomposition, and inference tools - across core econometrics domains - including CSV, JSON, TXT, and Excel - including JSON, Markdown, and text - such as RooCode, Claude-compatible tools, and other MCP hosts - from OLS and IV to ARIMA, GARCH, GAM, and causal forests - rather than narrow single-task workflows The server currently groups its 66 tools across the following categories: - — OLS, MLE, GMM - — DID, IV, PSM, fixed/random effects, RDD, synthetic control, event study,...
README
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aigroup-econ-mcp
Econometrics MCP server for regression, causal inference, time series, panel data, machine learning, and broader statistical analysis workflows.
Overview
aigroup-econ-mcp is a professional econometrics-oriented MCP server designed to help AI assistants and MCP clients perform structured quantitative analysis.
It covers:
- parameter estimation and regression analysis
- causal inference workflows
- microeconometrics and panel data
- time series and volatility models
- machine learning for econometric tasks
- spatial econometrics, decomposition, and inference tools
Highlights
- 66 professional tools across core econometrics domains
- Multiple input formats including CSV, JSON, TXT, and Excel
- Multiple output formats including JSON, Markdown, and text
- Support for MCP clients such as RooCode, Claude-compatible tools, and other MCP hosts
- Broad method coverage from OLS and IV to ARIMA, GARCH, GAM, and causal forests
- Designed for research and applied analysis rather than narrow single-task workflows
Tool Groups
The server currently groups its 66 tools across the following categories:
- Basic parametric estimation — OLS, MLE, GMM
- Causal inference — DID, IV, PSM, fixed/random effects, RDD, synthetic control, event study, and more
- Decomposition analysis — Oaxaca-Blinder, ANOVA, time-series decomposition
- Machine learning — random forest, gradient boosting, SVM, neural networks, clustering, DML, causal forest
- Microeconometrics — logit, probit, multinomial logit, Poisson, negative binomial, Tobit, Heckman
- Missing data handling — simple imputation and MICE
- Model diagnostics and robust inference — specification tests, GLS, WLS, robust errors, regularization, simultaneous equations
- Nonparametric methods — kernel regression, quantile regression, spline regression, GAM
- Spatial econometrics — weights matrices, Moran’s I, Geary’s C, LISA, spatial regression, GWR
- Statistical inference — bootstrap and permutation tests
- Time series and panel data — ARIMA, exponential smoothing, GARCH, unit-root tests, VAR/SVAR, cointegration, dynamic panel, panel VAR, structural breaks, time-varying parameter models
Quick Start
Requirements
- Python >= 3.10
uvxrecommended for easiest usage, orpip
Run with uvx
uvx aigroup-econ-mcp
If uvx keeps using an older cached build:
uvx --no-cache aigroup-econ-mcp
Install with pip
pip install aigroup-econ-mcp
aigroup-econ-mcp
安装
uvx aigroup-econ-mcp配置
{
"mcpServers": {
"aigroup-econ-mcp": {
"command": "uvx",
"args": ["aigroup-econ-mcp"]
}
}
}