Fit an Instrumental Variables regression via Two-Stage Least Squares (2SLS) of dependent_ticker on endogenous_ticker, instrumented by instrument_tickers.

Also known as: IV, 2SLS, IV-2SLS.

For more information about the method, and why plain OLS on an asset suspected of reverse-causality/omitted-confounder bias against another asset is unreliable, see causal_inference_model.get_iv_2sls.

A natural use case in a multi-asset return panel: suppose endogenous_ticker’s return is suspected to be simultaneously determined together with dependent_ticker’s return (e.g. two closely related assets that react to each other intraday, or one asset’s return partly reflects news about the other) – plain OLS of one on the other is then biased. An instrument_tickers asset that moves endogenous_ticker for reasons unrelated to dependent_ticker’s own error term (e.g. a supplier/peer whose moves affect endogenous_ticker but only reach dependent_ticker, if at all, THROUGH endogenous_ticker) allows recovering a cleaner estimate of the causal pass-through.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Instrumental Variables (2SLS) for you. Just ask in plain English.

Calculate the Instrumental Variables (2SLS) in Python

The Instrumental Variables (2SLS) is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_iv_2sls as shown below.

from financetoolkit import Toolkit

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

toolkit.econometrics.get_iv_2sls("AAPL", "MSFT", "Benchmark", period="weekly")

Which returns:

  Coefficient Std. Error t-Statistic P-Value
Intercept 0.0006 0.0025 0.2336 0.8156
MSFT 1.1453 0.0813 14.0938 0.0000

Parameters

get_iv_2sls accepts the following parameters:

  • dependent_ticker (str): The dependent (predicted) asset.
  • endogenous_ticker (str | list[str]): The endogenous regressor asset(s) – suspected correlated with the error term.
  • instrument_tickers (str | list[str]): The excluded instrument asset(s), correlated with endogenous_ticker but assumed uncorrelated with the error term. Must supply at least as many instruments as endogenous regressors.
  • exogenous_tickers (str | list[str] | None, optional): Other, non-instrumented control asset(s) included as-is in both stages. Defaults to None.
  • 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. Defaults to True.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
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The Econometrics module page introduces the module, and the sidebar lists all of its functions.

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