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)

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
intercept_longrun()	Long run intercept of stable VAR process
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()	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
to_vecm()