statsmodels.tsa.vector_ar.var_model.VAR¶

class statsmodels.tsa.vector_ar.var_model.VAR(endog, exog=None, dates=None, freq=None, missing='none')[source]¶

Fit VAR(p) process and do lag order selection

\[y_t = A_1 y_{t-1} + \ldots + A_p y_{t-p} + u_t\]

Parameters:	endog (array-like) – 2-d endogenous response variable. The independent variable. exog (array-like) – 2-d exogenous variable. dates (array-like) – must match number of rows of endog

References

Lütkepohl (2005) New Introduction to Multiple Time Series Analysis

Methods

`fit`([maxlags, method, ic, trend, verbose])	Fit the VAR model
`from_formula`(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
`hessian`(params)	The Hessian matrix of the model
`information`(params)	Fisher information matrix of model
`initialize`()	Initialize (possibly re-initialize) a Model instance.
`loglike`(params)	Log-likelihood of model.
`predict`(params[, start, end, lags, trend])	Returns in-sample predictions or forecasts
`score`(params)	Score vector of model.
`select_order`([maxlags, trend])	Compute lag order selections based on each of the available information criteria

Attributes

`endog_names`	Names of endogenous variables
`exog_names`