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()