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:

coefs : ndarray (p x k x k)

intercept : ndarray (length k)

sigma_u : ndarray (k x k)

names : sequence (length k)

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

A_mask : neqs x neqs mask array with known parameters masked

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

B_mask : neqs 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()

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()