statsmodels.tsa.vector_ar.var_model.VAR

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

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

Attributes

endog_names	Names of endogenous variables.
exog_names	The names of the exogenous variables.
y

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

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

Properties

endog_names	Names of endogenous variables.
exog_names	The names of the exogenous variables.
y