statsmodels.tsa.vector_ar.svar_model.SVARProcess.simulate_var¶

SVARProcess.simulate_var(steps=None, offset=None, seed=None, initial_values=None, nsimulations=None)¶

simulate the VAR(p) process for the desired number of steps

Parameters:¶

stepsNone or int

number of observations to simulate, this includes the initial observations to start the autoregressive process. If offset is not None, then exog of the model are used if they were provided in the model

offsetNone or ndarray (steps, neqs)

If not None, then offset is added as an observation specific intercept to the autoregression. If it is None and either trend (including intercept) or exog were used in the VAR model, then the linear predictor of those components will be used as offset. This should have the same number of rows as steps, and the same number of columns as endogenous variables (neqs).

seed{None, int}

If seed is not None, then it will be used with for the random variables generated by numpy.random.

initial_valuesarray_like, optional

Initial values for use in the simulation. Shape should be (nlags, neqs) or (neqs,). Values should be ordered from less to most recent. Note that this values will be returned by the simulation as the first values of endog_simulated and they will count for the total number of steps.

nsimulations{None, int}

Number of simulations to perform. If nsimulations is None it will perform one simulation and return value will have shape (steps, neqs).

Returns:¶

endog_simulatednd_array

Endog of the simulated VAR process. Shape will be (nsimulations, steps, neqs) or (steps, neqs) if nsimulations is None.