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statsmodels.tsa.vector_ar.var_model.VAR

class statsmodels.tsa.vector_ar.var_model.VAR(endog, 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.

dates : array-like

must match number of rows of endog

References

Lutkepohl (2005) New Introduction to Multiple Time Series Analysis

Methods

fit([maxlags, method, ic, trend, verbose]) Fit the VAR model
from_formula(formula, data[, subset]) 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, verbose]) Compute lag order selections based on each of the available information

Attributes

endog_names
exog_names

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