Regression Discontinuity (RDD)

Estimate a Sharp Regression Discontinuity (RDD): the jump in dependent_ticker exactly where running_variable_ticker crosses cutoff.

Also known as: RDD, sharp RD.

For more information about the method, see causal_inference_model.get_regression_discontinuity.

Treats running_variable_ticker’s value as the running variable that (hypothetically) triggers some discrete change once it crosses cutoff – e.g. testing whether dependent_ticker’s return behaves discontinuously around a round-number/threshold level of another asset or indicator (a psychological price level, an index-inclusion market-cap threshold, a macro indicator’s policy-relevant threshold) fed in as running_variable_ticker. Fits separate local linear regressions of dependent_ticker’s return on the (cutoff-centered) running_variable_ticker value, one on each side of cutoff, and reports the gap between the two fitted lines exactly at the cutoff.

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Calculate the Regression Discontinuity (RDD) in Python

The Regression Discontinuity (RDD) is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_regression_discontinuity as shown below.

from financetoolkit import Toolkit

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

toolkit.econometrics.get_regression_discontinuity(
    "AAPL", "MSFT", cutoff=0.0, period="weekly"
)

Which returns:

Parameters

get_regression_discontinuity accepts the following parameters:

• dependent_ticker (str): The outcome asset.
• running_variable_ticker (str): The asset (or column) whose value determines which side of cutoff an observation falls on.
• cutoff (float): The threshold value of running_variable_ticker at which the discontinuity is estimated.
• period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
• column (str, optional): The historical data column to use for both series. Defaults to “Return”.
• bandwidth (float | None, optional): The maximum distance from cutoff an observation may be to be included in either local regression. Defaults to None, which uses half of the running variable’s observed range – see causal_inference_model.get_regression_discontinuity for why this is a deliberately naive default.
• kernel (str, optional): One of “uniform” or “triangular”. Defaults to “uniform”.
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Causal Inference

The Econometrics module page introduces the module, and the sidebar lists all of its functions.