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