mojolearn.VAR#

class mojolearn.VAR(endog, exog=None, numeric_mode=None)[source]#

Bases: object

Vector autoregression estimated by OLS.

Fits one least-squares regression per equation on the shared lagged design [1, y[t-1], ..., y[t-p]], following statsmodels’ VAR. The normal equations are solved in float32 by Cholesky, where statsmodels uses a float64 least-squares solve.

Parameters:

  • endog (array-like of shape (n_obs, n_variables)) – The multivariate series, float32. float64 raises TypeError.

  • exog (None) – Exogenous variables are not supported; anything other than None raises NotImplementedError.

  • numeric_mode (str or None, default None) – Numeric mode for this model ('identical', 'deterministic' or 'fast'); None uses the process default (see mojolearn.set_numeric_mode()).

Examples

>>> import numpy as np, mojolearn
>>> y = np.random.default_rng(0).standard_normal((200, 3)).cumsum(axis=0).astype(np.float32)
>>> res = mojolearn.VAR(y).fit(maxlags=2)
>>> res.forecast(y, steps=5).shape
(5, 3)
__init__(endog, exog=None, numeric_mode=None)[source]#

Methods

fit([maxlags, method, ic, trend])

Estimate the VAR with a fixed lag order.
fit(maxlags=1, method='ols', ic=None, trend='c')[source]#

Estimate the VAR with a fixed lag order.

Parameters:

  • maxlags (int, default 1) – The lag order p (used as is; there is no order selection).

  • method ({“ols”}, default “ols”) – Estimation method.

  • ic (None) – Information-criterion order selection is not supported; anything other than None raises NotImplementedError.

  • trend ({“c”, “n”}, default “c”) – "c" adds an intercept, "n" none. "ct" and "ctt" raise NotImplementedError.

Returns:

VARResults

Raises:

numpy.linalg.LinAlgError – If the lagged design is rank deficient.