# econometrics

**Repository Path**: wait1111/econometrics

## Basic Information

- **Project Name**: econometrics
- **Description**: 陈强高级计量经济学笔记，使用python、matlab实现各模型估计
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2022-07-14
- **Last Updated**: 2022-07-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 陈强高级计量经济学笔记

<div align=center><img src="https://lei-picture.oss-cn-beijing.aliyuncs.com/img/20200423192255.png" width="350"></div>

学习计量经济学过程中总结的笔记（更新ing...），主要包括**原理讲解与实现、python第三方库估计、matlab实现**三部分。

由于本人能力有限，欢迎各位指正批评。

<a href="https://www.python.org/downloads/"><img  src="https://img.shields.io/badge/python-3.6%2B-brightgreen"></a>
<a href="https://github.com/bashtage/arch"><img src="https://img.shields.io/badge/arch-4.13-red"></a>
<a href="https://github.com/matplotlib/matplotlib"><img src="https://img.shields.io/badge/matplotlib-3.1.3-blue"></a>
<a href="https://github.com/numpy/numpy"><img src="https://img.shields.io/badge/numpy-1.18.1-blue"></a>
<a href="https://github.com/pandas-dev/pandas"><img src="https://img.shields.io/badge/pandas-1.0.1-yellow"></a>
<a href="https://github.com/scipy/scipy"><img src="https://img.shields.io/badge/scipy-1.4.1-brightgreen"></a>
<a href="https://github.com/statsmodels/statsmodels"><img src="https://img.shields.io/badge/statsmodels-0.11.0-red"></a>
<a href="https://github.com/sympy/sympy"><img src="https://img.shields.io/badge/sympy-1.5.1-lightgrey"></a>

&emsp;

## 目录

* [基础入门](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/基础入门.ipynb)

* [古典线性回归](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/古典线性回归.ipynb)

* 金融时间序列

  * [收益率概念与平稳性](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/收益率概念与平稳性.ipynb)

  * [自回归模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/自回归模型.ipynb)

  * [移动平均(MA)模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/移动平均(MA)模型.ipynb)

  * [ARMA模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/ARMA模型.ipynb)

  * [自回归分布滞后模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/自回归分布滞后模型.ipynb)

  * [VAR模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/VAR模型.ipynb)

  * [Granger因果检验](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/Granger因果检验.ipynb)

  * [ARCH模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/ARCH模型.ipynb)

  * [GARCH模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/金融时间序列/GARCH模型.ipynb)

* 分位数回归

  * [基础概念](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/分位数回归/基础概念.ipynb)

  * [分位数Granger因果检验](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/分位数回归/分位数Granger因果检验.ipynb)

  * [分位数VAR模型](https://nbviewer.jupyter.org/github/lei940324/econometrics/blob/master/分位数回归/分位数VAR模型.ipynb)

## License

[MIT](https://github.com/lei940324/econometrics/blob/master/LICENSE) © 热心市民石头