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