4.8.1.2.7. statsmodels.tsa.api.VAR¶
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class
statsmodels.tsa.api.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
4.8.1.2.7.1. Methods¶
__init__(endog[, dates, freq, missing]) |
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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 |
4.8.1.2.7.2. Attributes¶
endog_names |
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exog_names |