Chow Test

Calculate the Chow test for a structural break at break_date in a regression of dependent_ticker on independent_tickers.

Also known as: Chow breakpoint test.

For more information about the method, see specification_tests_model.get_chow_test.

Chow Test in Python

get_chow_test is part of the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_chow_test as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

# AAPL (the first ticker) is dependent; MSFT and Benchmark are independent
toolkit.econometrics.get_chow_test(
    break_date="2021-06-30",
    independent_tickers=["MSFT", "Benchmark"],
    period="weekly",
)

Which returns:

Parameters

get_chow_test accepts the following parameters:

• break_date (str): The date (e.g. “2021-06-30”) at which to split the sample – all periods starting on or after this date form the “after” sub-sample, everything before it forms the “before” sub-sample.
• dependent_ticker (str | None, optional): The dependent (predicted) asset. Defaults to None, meaning the Toolkit instance’s first ticker.
• independent_tickers (str | list[str] | None, optional): The independent (predictor) asset(s). Defaults to None, meaning every other ticker in the Toolkit instance besides dependent_ticker.
• include_benchmark (bool, optional): Whether to include “Benchmark” in the default independent ticker(s) (has no effect when independent_tickers is given explicitly). Defaults to False.
• period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
• column (str, optional): The historical data column to regress on. Defaults to “Return”.
• add_constant (bool, optional): Whether to include an intercept in the underlying regression(s). Defaults to True.
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

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