Fit a Random Effects estimator explaining a panel of dependent_tickers (the entities) by a regressor x, via Swamy-Arora feasible Generalized Least Squares.

Also known as: RE, GLS panel estimator, Swamy-Arora estimator.

See get_fixed_effects for how dependent_tickers/independent_tickers/ independent_column are shaped into a panel, and panel_data_model.get_random_effects for the full estimator formula, references, and how it compares to Fixed Effects. Unlike Fixed Effects, Random Effects retains and estimates a single, population-average intercept rather than one intercept per entity – more efficient than Fixed Effects if entity effects are indeed uncorrelated with the regressor(s) (see get_hausman_test to check that assumption).

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Calculate the Random Effects in Python

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

pip install financetoolkit -U

Then call get_random_effects as shown below.

from financetoolkit import Toolkit

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

toolkit.econometrics.get_random_effects(
    independent_column="Volume", period="weekly"
)

Which returns:

  Coefficient Std. Error t-Statistic P-Value
Intercept 0.0094 0.0012 7.7341 0.0000
Volume -0.0000 0.0000 -5.1258 0.0000

Parameters

get_random_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. Note: since this Toolkit instance’s historical data is aligned onto a common calendar across tickers, a broadcast factor’s entity mean is identical for every entity, leaving no between-entity variation to identify Random Effects’ between-regression step – prefer independent_column here unless dependent_tickers genuinely differ in their date coverage (e.g. different listing histories).
  • 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”.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.
Fixed Effects Hausman Test

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

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