7.8.3.1. statsmodels.tsa.vector_ar.var_model.VARProcess¶

class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None)[source]¶

Class represents a known VAR(p) process

Parameters:	coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k)
Returns:	Attributes:

__init__(coefs, intercept, sigma_u, names=None)[source]¶

Methods

__init__(coefs, intercept, sigma_u[, names])
acf([nlags])	Compute theoretical autocovariance function
acorr([nlags])	Compute theoretical autocorrelation function
forecast(y, steps)	Produce linear minimum MSE forecasts for desired number of steps
forecast_cov(steps)	Compute theoretical forecast error variance matrices
forecast_interval(y, steps[, alpha])	Construct forecast interval estimates assuming the y are Gaussian
get_eq_index(name)	Return integer position of requested equation name
is_stable([verbose])	Determine stability based on model coefficients
long_run_effects()	Compute long-run effect of unit impulse
ma_rep([maxn])	Compute MA(\(\infty\)) coefficient matrices
mean()	Mean of stable process
mse(steps)	Compute theoretical forecast error variance matrices
orth_ma_rep([maxn, P])	Compute Orthogonalized MA coefficient matrices using P matrix such that \(\Sigma_u = PP^\prime\).
plot_acorr([nlags, linewidth])	Plot theoretical autocorrelation function
plotsim([steps])	Plot a simulation from the VAR(p) process for the desired number of