# 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