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https://github.com/simonpierreboucher/mle_econometrics_notebook
This project contains a series of Python notebooks that demonstrate econometric techniques using Maximum Likelihood Estimation (MLE) and Ordinary Least Squares (OLS), with a focus on robust standard error calculations and heteroscedasticity tests.
https://github.com/simonpierreboucher/mle_econometrics_notebook
econometrics linear-models linear-regression maximum-likelihood-estimation mle-estimation python white-test
Last synced: 10 days ago
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This project contains a series of Python notebooks that demonstrate econometric techniques using Maximum Likelihood Estimation (MLE) and Ordinary Least Squares (OLS), with a focus on robust standard error calculations and heteroscedasticity tests.
- Host: GitHub
- URL: https://github.com/simonpierreboucher/mle_econometrics_notebook
- Owner: simonpierreboucher
- Created: 2024-11-15T12:01:33.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-15T12:06:01.000Z (about 1 month ago)
- Last Synced: 2024-11-15T13:18:25.718Z (about 1 month ago)
- Topics: econometrics, linear-models, linear-regression, maximum-likelihood-estimation, mle-estimation, python, white-test
- Language: Jupyter Notebook
- Homepage:
- Size: 639 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MLE Econometrics Notebook
Welcome to the **MLE Econometrics Notebook** repository! This project contains a series of Python notebooks that demonstrate econometric techniques using Maximum Likelihood Estimation (MLE) and Ordinary Least Squares (OLS), with a focus on robust standard error calculations and heteroscedasticity tests.
## Repository Overview
This repository is a collection of Jupyter notebooks designed to provide clear and practical examples of key econometric concepts, implemented in Python.
## Table of Contents
### Notebooks
1. **[MLE Linear Model in Python](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/MLE_linear_model_python.ipynb)**
Implements a linear regression model using Maximum Likelihood Estimation (MLE). Includes comprehensive model diagnostics and visualizations.
2. **[OLS Linear Model in Python](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/OLS_linear_model_Python.ipynb)**
Demonstrates a traditional Ordinary Least Squares (OLS) regression with diagnostics and visualizations.
3. **[HC0 Robust Standard Errors](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/MLE_HC0_Python.ipynb)**
Calculates robust standard errors (HC0) to account for heteroscedasticity in residuals.
4. **[HC1 Robust Standard Errors](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/MLE_HC1_Python.ipynb)**
Implements HC1 robust standard errors with finite-sample corrections for smaller datasets.
5. **[HC2 Robust Standard Errors](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/MLE_HC2_Python.ipynb)**
Includes leverage adjustments for residuals using HC2 robust standard errors.
6. **[HC3 Robust Standard Errors](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/MLE_HC3_Python.ipynb)**
Features a stricter leverage adjustment approach with HC3 robust standard errors for high-leverage observations.
7. **[White Test for Heteroscedasticity](https://github.com/simonpierreboucher/mle_econometrics_notebook/blob/main/MLE_White_test_python.ipynb)**
Conducts a White Test to detect heteroscedasticity in regression residuals.
---
## Features
- Implementation of **Maximum Likelihood Estimation (MLE)** and **Ordinary Least Squares (OLS)** techniques.
- Robust standard errors using HC0, HC1, HC2, and HC3 corrections.
- Statistical diagnostics including residual analysis, \(R^2\), \(t\)-tests, \(F\)-tests, and more.
- Heteroscedasticity detection using the **White Test**.
- Comprehensive explanations and visualizations to enhance understanding.
---
## Getting Started
### Prerequisites
- Python 3.8+
- Jupyter Notebook or Jupyter Lab
- Required Python packages:
- `numpy`
- `scipy`
- `pandas`
- `statsmodels`
- `matplotlib`
- `seaborn`
### Installation
1. Clone the repository:
```bash
git clone https://github.com/simonpierreboucher/mle_econometrics_notebook.git
```
2. Navigate to the repository directory:
```bash
cd mle_econometrics_notebook
```
3. Install the required packages:
```bash
pip install -r requirements.txt
```
4. Open the notebooks in Jupyter:
```bash
jupyter notebook
```
---
## Usage
Each notebook is self-contained and includes examples with detailed explanations and outputs. Simply open the desired notebook in Jupyter Notebook or Jupyter Lab and follow along.
---
## Author
This repository is maintained by **Simon-Pierre Boucher**. Feel free to reach out or contribute to the project if you have suggestions or improvements!
---
## Repository URL
[https://github.com/simonpierreboucher/mle_econometrics_notebook](https://github.com/simonpierreboucher/mle_econometrics_notebook)
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.