statsmodels.multivariate.multivariate_ols._MultivariateOLS

class statsmodels.multivariate.multivariate_ols._MultivariateOLS(endog, exog, missing='none', hasconst=None, **kwargs) [source]

Multivariate linear model via least squares

Parameters:

endog (array_like) – Dependent variables. A nobs x k_endog array where nobs is the number of observations and k_endog is the number of dependent variables exog (array_like) – Independent variables. A nobs x k_exog array where nobs is the number of observations and k_exog is the number of independent variables. An intercept is not included by default and should be added by the user (models specified using a formula include an intercept by default)

endog

array – See Parameters.

exog

array – See Parameters.

Methods

fit([method])	Fit a model to data.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
predict(params[, exog])	After a model has been fit predict returns the fitted values.

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables