# statsmodels.tsa.vector_ar.var_model.VARProcess¶

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

Class represents a known VAR(p) process

Parameters:
coefsndarray (p x k x k)

coefficients for lags of endog, part or params reshaped

coefs_exogndarray

parameters for trend and user provided exog

sigma_undarray (k x k)

residual covariance

namessequence (length k)
_params_infodict

internal dict to provide information about the composition of params, specifically k_trend (trend order) and k_exog_user (the number of exog variables provided by the user). If it is None, then coefs_exog are assumed to be for the intercept and trend.

Methods

 acf([nlags]) Compute theoretical autocovariance function acorr([nlags]) Autocorrelation function forecast(y, steps[, exog_future]) Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y forecast_cov(steps) Compute theoretical forecast error variance matrices forecast_interval(y, steps[, alpha, exog_future]) Construct forecast interval estimates assuming the y are Gaussian get_eq_index(name) Return integer position of requested equation name Long run intercept of stable VAR process is_stable([verbose]) Determine stability based on model coefficients Compute long-run effect of unit impulse ma_rep([maxn]) Compute MA($$\infty$$) coefficient matrices Long run intercept of stable VAR 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, offset, seed]) Plot a simulation from the VAR(p) process for the desired number of steps simulate_var([steps, offset, seed, ...]) simulate the VAR(p) process for the desired number of steps