statsmodels.tsa.vector_ar.var_model.VARProcess
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class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None)
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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:** :
Methods
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() 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 . plot_acorr
([nlags, linewidth])Plot theoretical autocorrelation function plotsim
([steps])Plot a simulation from the VAR(p) process for the desired number of
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