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
endog_names
Names of endogenous variables. exog_names
The names of the exogenous variables. 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
()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
()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
endog_names
Names of endogenous variables. exog_names
The names of the exogenous variables. y