Fit a Fixed Effects (“within”) estimator explaining a panel of dependent_tickers (the entities) by a regressor x, controlling for entity-specific (and/or time-specific) fixed effects.

Also known as: within estimator, FE, least squares dummy variable (LSDV) estimator.

Unlike this module’s other regression methods (get_ols, get_wls, …), which each compare a handful of individual ticker return series on equal footing, this treats dependent_tickers as a genuine panel of entities – e.g. every stock in the Toolkit instance – observed over time. The regressor x is built in exactly one of two ways (provide exactly one of the two arguments below):

  • independent_tickers: a COMMON factor (or factors) applied identically to every entity at each date (e.g. a market benchmark, mirroring a Fama-French-style factor regression).
  • independent_column: a PER-ENTITY regressor – each entity’s own value of a different historical data column (e.g. does "Volume" explain "Return", across the panel).

Fixed Effects removes any purely entity-specific, time-invariant characteristic (e.g. a stock’s typical risk premium) before estimating the regressor’s coefficient(s), by demeaning every variable by its entity’s mean. See panel_data_model.get_fixed_effects for the full formula and references.

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Calculate the Fixed Effects in Python

The Fixed Effects is available in the Econometrics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_fixed_effects as shown below.

from financetoolkit import Toolkit

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

toolkit.econometrics.get_fixed_effects(
    independent_tickers="Benchmark", period="weekly"
)

Which returns:

  Coefficient Std. Error t-Statistic P-Value
Benchmark 1.0772 0.0331 32.5314 0.0000
Entity Effect: AAPL 0.0022 nan nan nan
Entity Effect: MSFT 0.0017 nan nan nan

Parameters

get_fixed_effects accepts the following parameters:

  • independent_tickers (str | list[str] | None, optional): The factor ticker(s), whose column values are broadcast identically to every entity at each date. Mutually exclusive with independent_column.
  • independent_column (str | None, optional): A different historical data column, taken per-entity from each of dependent_tickers’ own data, to use as the regressor. Mutually exclusive with independent_tickers.
  • dependent_tickers (str | list[str] | None, optional): The panel of entity tickers to explain. Defaults to None, meaning every ticker in the Toolkit instance (other than independent_tickers, if given).
  • period (str, optional): The data frequency (daily, weekly, monthly, quarterly, or yearly). Defaults to “daily”.
  • column (str, optional): The dependent variable’s historical data column. Defaults to “Return”.
  • entity_effects (bool, optional): Whether to control for time-invariant entity-specific characteristics. Defaults to True.
  • time_effects (bool, optional): Whether to control for entity-invariant, time-specific shocks. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Random Effects Hausman Test

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

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