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:

Parameters:	endog : array-like 2-d endogenous response variable. The independent variable. dates : array-like must match number of rows of endog

endog : array-like

2-d endogenous response variable. The independent variable.

dates : array-like

must match number of rows of endog

Lutkepohl (2005) New Introduction to Multiple Time Series Analysis

`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

`endog_names`
`exog_names`

Links:

doc_statsmodels

2017-01-18 16:21:16

Comments