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)[source]

Estimate VAR(p) process with fixed number of lags

Parameters
endogndarray
endog_laggedndarray
paramsndarray
sigma_undarray
lag_orderint
modelVAR model instance
trendstr {‘n’, ‘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

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