semvec/pypi-packages huggingface dataset:
chowtest 0.1.4
Python implementation of the Chow test (1960).
License: MIT License

# Chow Test

[![Downloads](https://pepy.tech/badge/chowtest)](https://pepy.tech/project/chowtest) [![Downloads](https://pepy.tech/badge/chowtest/month)](https://pepy.tech/project/chowtest)

This project provides an implementation of the Chow break test.

The Chow test was initially developed by Gregory Chow in 1960 to test whether one regression or two or more regressions best characterise the data. As such, the Chow test is capable of detecting "structural breaks" within time-series. Additional information can be obtained from:

[Chow, Gregory C. "Tests of equality between sets of coefficients in two linear regressions." Econometrica: Journal of the Econometric Society (1960): 591-605.][abc]

[Toyoda, Toshihisa. "Use of the Chow test under heteroscedasticity." Econometrica: Journal of the Econometric Society (1974): 601-608.][def]

This implementation supports simple linear models, and finding breaks where k = 2.

### Installation

This module requires Python 3.0+ to run. The module can can be imported by:
```python
pip install chowtest
from chow_test import chowtest
```

### Input Arguments

The required input arguments are listed below:

| Argument | Description |
| ------ | ------ |
| y | dependent variable (Pandas DataFrame Column) |
| X | independent variable(s) (Pandas Dataframe Column(s)) |
| last_index_in_model_1 | index of final point prior to assumed structural break (str) |
| first_index_in_model_2 | index of first point following structural break (str) |
| significance_level | the significance level for hypothesis testing (float)  |


   [abc]: <https://www.jstor.org/stable/1910133?casa_token=5boKBERpursAAAAA%3ABCYkFnXnHBbM0c4thWh5rySthktrt5nLlWE1nwjKbHlwmpH5fTdQoAMzgv82adNdzRzoZBe01scMcO_lDf-mjemPUsRtOmbhXkCsuoc4tUXyWrlJi59Z3Q&seq=1#metadata_info_tab_contents>
   [def]: <https://www.jstor.org/stable/1911796?casa_token=4WNFjhaMRG8AAAAA%3AKzirHep7m9iaXUTF-q90Z-ZyHVHeolvk_cNUlOuZw2bQF4z4UmAvgvejjPlC9woHSTdzBx5PVFSHP1aFhbnvWve1aMPYGO90MkbUTAgQBk-wo6HzVLjLIw&seq=1#metadata_info_tab_contents>