statsmodels.tsa.vector_ar.svar_model.SVARProcess¶

class statsmodels.tsa.vector_ar.svar_model.SVARProcess(coefs, intercept, sigma_u, A_solve, B_solve, names=None)[source]¶

Class represents a known SVAR(p) process

Parameters:¶

coefsndarray (p x k x k)

interceptndarray (length k)

sigma_undarray (k x k)

namessequence (length k)

Aneqs x neqs np.ndarray with unknown parameters marked with ‘E’

A_maskneqs x neqs mask array with known parameters masked

Bneqs x neqs np.ndarry with unknown parameters marked with ‘E’

B_maskneqs x neqs mask array with known parameters masked

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])	Unavailable for SVAR
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
svar_ma_rep([maxn, P])	Compute Structural MA coefficient matrices using MLE of A, B
to_vecm()