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
- endogarray-like
2-d endogenous response variable. The independent variable.
- exogarray-like
2-d exogenous variable.
- datesarray-like
must match number of rows of endog
References
Lütkepohl (2005) New Introduction to Multiple Time Series Analysis
- Attributes
endog_namesNames of endogenous variables
- exog_names
- 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 (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