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
Attributes
Names of endogenous variables.
The names of the exogenous variables.
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 (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 (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
Names of endogenous variables.
The names of the exogenous variables.