gs_quant.timeseries.statistics.LinearRegression

class LinearRegression(X, y, fit_intercept=True)[source]

Fit an Ordinary least squares (OLS) linear regression model.

Parameters:

  • X (Union[Series, List[Series]]) – observations of the explanatory variable(s)

  • y (Series) – observations of the dependent variable

  • fit_intercept (bool) – whether to calculate intercept in the model

Usage

Fit OLS Model based on observations of the explanatory variables(s) X and the dependent variable y. If X and y are not aligned, only use the intersection of dates/times.

Examples

Run a linear regression on y vs. x1 and x2 and compute the R squared:

>>> x1 = generate_series(100)
>>> x2 = generate_series(100)
>>> y = generate_series(100)
>>> r = LinearRegression([x1, x2], y, True)
>>> r.r_squared()

Methods

coefficient(i)

Estimated coefficient.

fitted_values()

Fitted values computed by evaluating the regression model on the original input X.

predict(X_predict)

Use the model for prediction.

r_squared()

Coefficient of determination (R Squared)

standard_deviation_of_errors()

Standard deviation of the error term.