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
()