statsmodels.tsa.vector_ar.svar_model.SVARResults

class statsmodels.tsa.vector_ar.svar_model.SVARResults(endog, endog_lagged, params, sigma_u, lag_order, A=None, B=None, A_mask=None, B_mask=None, model=None, trend='c', names=None, dates=None)

Estimate VAR(p) process with fixed number of lags

Parameters

endogarray

endog_laggedarray

paramsarray

sigma_uarray

lag_orderint

modelVAR model instance

trendstr {‘nc’, ‘c’, ‘ct’}

namesarray_like

List of names of the endogenous variables in order of appearance in endog.

dates

Attributes

aic

Akaike information criterion

bic

Bayesian a.k.a.

bse

coefsndarray (p x K x K)

Estimated A_i matrices, A_i = coefs[i-1]

cov_params

Estimated variance-covariance of model coefficients

dates

detomega

df_modelint

Number of estimated parameters, including the intercept / trends

df_residint

Number of observations minus number of estimated parameters

endog

endog_lagged

fittedvalues

fpe

Final Prediction Error (FPE)

intercept

info_criteria

k_arint

k_trendint

llf

model

names

neqsint

Number of variables (equations)

nobsint

n_totobsint

params

k_arint

Order of VAR process

paramsndarray (Kp + 1) x K

A_i matrices and intercept in stacked form [int A_1 … A_p]

pvalue

nameslist

variables names

resid

sigma_undarray (K x K)

Estimate of white noise process variance Var[u_t]

sigma_u_mle

stderr

trenorder

tvalues

y :

ys_lagged

Methods

acf([nlags])	Compute theoretical autocovariance function
acorr([nlags])	Autocorrelation function
cov_params()	Estimated variance-covariance of model coefficients
cov_ybar()	Asymptotically consistent estimate of covariance of the sample mean
fevd([periods, var_decomp])	Compute forecast error variance decomposition (“fevd”)
forecast(y, steps[, exog_future])	Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y
forecast_cov([steps, method])	Compute forecast covariance matrices for desired number of steps
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
irf([periods, var_order])	Analyze structural impulse responses to shocks in system
irf_errband_mc([orth, repl, steps, signif, …])	Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions
irf_resim([orth, repl, steps, seed, burn, cum])	Simulates impulse response function, returning an array of simulations.
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()	Plot input time series
plot_acorr([nlags, resid, linewidth])	Plot autocorrelation of sample (endog) or residuals
plot_forecast(steps[, alpha, plot_stderr])	Plot forecast
plot_sample_acorr([nlags, linewidth])	Plot sample autocorrelation function
plotsim([steps, offset, seed])	Plot a simulation from the VAR(p) process for the desired number of steps
reorder(order)	Reorder variables for structural specification
resid_acorr([nlags])	Compute sample autocorrelation (including lag 0)
resid_acov([nlags])	Compute centered sample autocovariance (including lag 0)
sample_acorr([nlags])	Sample acorr
sample_acov([nlags])	Sample acov
simulate_var([steps, offset, seed])	simulate the VAR(p) process for the desired number of steps
sirf_errband_mc([orth, repl, steps, signif, …])	Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions
summary()	Compute console output summary of estimates
svar_ma_rep([maxn, P])	Compute Structural MA coefficient matrices using MLE of A, B
test_causality(caused[, causing, kind, signif])	Test Granger causality
test_inst_causality(causing[, signif])	Test for instantaneous causality
test_normality([signif])	Test assumption of normal-distributed errors using Jarque-Bera-style omnibus Chi^2 test.
test_whiteness([nlags, signif, adjusted])	Residual whiteness tests using Portmanteau test
to_vecm()

Properties

aic	Akaike information criterion
bic	Bayesian a.k.a.
bse	Standard errors of coefficients, reshaped to match in size
detomega	Return determinant of white noise covariance with degrees of freedom correction:
df_model	Number of estimated parameters, including the intercept / trends
df_resid	Number of observations minus number of estimated parameters
fittedvalues	The predicted insample values of the response variables of the model.
fpe	Final Prediction Error (FPE)
hqic	Hannan-Quinn criterion
info_criteria	information criteria for lagorder selection
llf	Compute VAR(p) loglikelihood
pvalues	Two-sided p-values for model coefficients from Student t-distribution
pvalues_dt
pvalues_endog_lagged	pvalues_endog_laggd
resid	Residuals of response variable resulting from estimated coefficients
resid_corr	Centered residual correlation matrix
roots	The roots of the VAR process are the solution to (I - coefs[0]*z - coefs[1]*z**2 …
sigma_u_mle	(Biased) maximum likelihood estimate of noise process covariance
stderr	Standard errors of coefficients, reshaped to match in size
stderr_dt	Stderr_dt
stderr_endog_lagged	Stderr_endog_lagged
tvalues	Compute t-statistics.
tvalues_dt
tvalues_endog_lagged
y
ys_lagged