https://github.com/amineouerfellii/econometron

A Python package for time series forecasting and economic analysis, providing tools for simulation, estimation, and model evaluation with a focus on scalability and research applications.
https://github.com/amineouerfellii/econometron

data-science deep-learning dsge-models econometrics forecasting localprojections macroeconometrics prediction projection-methods time-series

Last synced: 3 months ago
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A Python package for time series forecasting and economic analysis, providing tools for simulation, estimation, and model evaluation with a focus on scalability and research applications.

• Host: GitHub
• URL: https://github.com/amineouerfellii/econometron
• Owner: AmineOuerfellii
• License: mit
• Created: 2025-05-29T14:31:12.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-08-31T16:26:15.000Z (10 months ago)
• Last Synced: 2026-03-28T00:36:57.370Z (3 months ago)
• Topics: data-science, deep-learning, dsge-models, econometrics, forecasting, localprojections, macroeconometrics, prediction, projection-methods, time-series
• Language: Python
• Homepage: http://econometron.netlify.app
• Size: 20.7 MB
• Stars: 4
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

          

![Econometron Logo](/econometron.png)

# Econometron: A Python package for Econometric and Time Series Analysis

[![PyPI Version](https://img.shields.io/pypi/v/econometron?color=blue&label=PyPI)](https://pypi.org/project/econometron/)

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/)

## Introduction

Econometron is a Python library designed for econometric modeling, time series analysis, and dynamic stochastic general equilibrium (DSGE) model solving and estimation. It provides a wide range of tools for researchers, economists, and data scientists to build, estimate, and analyze complex multivariate time series models and non-linear DSGE models. With a focus on flexibility and performance, Econometron supports both classical and modern approaches, including state-of-the-art neural network-based forecasting and robust statistical methods.

Whether you're modeling economic time series, performing impulse response function (IRF) analysis, or solving non-linear DSGE models, Econometron offers a unified and efficient framework to streamline your workflow.

## Key Features

### Multivariate Statistical Time Series Models

- **VAR (Vector Autoregression):** Model the dynamic relationships between multiple time series.  

- **SVAR (Structural Vector Autoregression):** Incorporate structural restrictions for causal inference and policy analysis.  

- **VARMA (Vector Autoregressive Moving Average):** Combine autoregressive and moving average components for enhanced flexibility.  

- **VARIMA (Vector Autoregressive Integrated Moving Average):** Handle non-stationary time series with differencing.  

### VARMA Identification

- **Echelon Form Identification:** Implements the echelon form approach for identifying VARMA models, ensuring robust and unique parameter estimation in Python.

### Neural Network-Based Forecasting

- **N-BEATS (Neural Basis Expansion Analysis for Time Series):** A state-of-the-art deep learning model for univariate and multivariate time series forecasting.  

- **N-BEATS + RevIN:** Enhances N-BEATS with Reversible Instance Normalization (RevIN) for improved generalization and robustness.

### State Space Models

- Flexible framework for modeling complex dynamic systems using state space representations, suitable for both linear and non-linear systems.

### Estimation Methods

- **Bayesian Estimation:** Leverage Bayesian techniques for parameter estimation, incorporating prior knowledge and uncertainty quantification.  

- **Maximum Likelihood Estimation (MLE):** Optimize model parameters using likelihood-based methods for precise inference.

### Impulse Response Functions (IRF)

- **Local Projection IRF:** Compute impulse response functions using local projection methods, ideal for non-linear and robust analysis.

### Non-Linear DSGE Model Solving

- **Projection Methods:** Solve non-linear DSGE models using advanced numerical techniques:  

  - Galerkin Method: Project solutions onto a basis of functions for accurate approximation.  

  - Collocation Method: Solve at specific points to approximate the policy function.  

  - Least Squares Method: Minimize residuals to find optimal solutions.

## Getting Started

To use Econometron, install it via pip:

```bash

pip install econometron

````

### Example: Fitting a VAR Model

```python

from econometron.Models.VectorAutoReg import VAR

# Load your time series data

data = ...  # Your multivariate time series data

model = VAR(data=data,max_p=2,check_stationnarity=True)

results = model.fit()

```

For detailed documentation, tutorials, and examples, visit the [Econometron Documentation](https://econometron.netlify.app).

## Why Econometron?

* **Comprehensive:** Covers a wide range of econometric models, from classical VAR to cutting-edge neural network approaches.

* **Flexible:** Supports both statistical and machine learning-based methods for time series analysis.

* **Robust:** Implements state-of-the-art estimation and identification techniques for reliable results.

* **User-Friendly:** Designed with Python's ecosystem in mind, integrating seamlessly with libraries like NumPy, Pandas, and PyTorch.

## Code of Conduct

We are committed to fostering a welcoming and inclusive community. All participants are expected to:

* Be respectful and considerate in interactions.

* Avoid harassment or discriminatory behavior.

* Use constructive feedback and maintain professionalism.

* Respect the community and project’s guidelines.

Violations may result in removal from the project or community channels. Please read the full [Code of Conduct](/CODE_OF_CONDUCT.md) for details.

## Contributing

Contributions are highly valued. To contribute:

1. Fork the repository.

2. Create a new branch for your feature or bug fix.

3. Implement your changes with clear, well-documented code.

4. Run all tests to ensure stability.

5. Submit a pull request describing your changes in detail.

For more information, see the full [Contributing Guide](/CONTRIBUTING.md).

## License

Econometron is licensed under the MIT License. See the LICENSE file for more information.