What is MultivariateOLS model in the StatsModels library?

This recipe describes what is MultivariateOLS model in the StatsModels library

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Recipe Objective - What is MultivariateOLS model in the StatsModels library?

Multivariate regression is a technique for estimating a single regression model using multiple result variables. It is used to build a Multivariate linear model via least squares. It belongs to a class statsmodels.multivariate.multivariate_ols._MultivariateOLS(endog, exog, missing='none', hasconst=None, **kwargs)

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Table of Contents

Parameters:

endog
Dependent variables. A nobs x k_endog array where nobs are the number of observations and k_endog is the number of dependent variables

exog
Independent variables. A nobs x k_exog array where nobs are 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)

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.

MultivariateOLSResults:

It is a result class of MultivariateOLS.

Methods:

mv_test([hypotheses])
Linear hypotheses testing

summary()
Summary of the model

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