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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:** :

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(\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

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