statsmodels.tsa.vector_ar.var_model.VARResults

class statsmodels.tsa.vector_ar.var_model.VARResults(endog, endog_lagged, params, sigma_u, lag_order, model=None, trend='c', names=None, dates=None, exog=None) [source]

Estimate VAR(p) process with fixed number of lags

Parameters:

endog (array) – endog_lagged (array) – params (array) – sigma_u (array) – lag_order (int) – model (VAR model instance) – trend (str {'nc', 'c', 'ct'}) – names (array-like) – List of names of the endogenous variables in order of appearance in endog. dates – exog (array) –

Returns:

**Attributes** aic bic bse coefs (ndarray (p x K x K)) – Estimated A_i matrices, A_i = coefs[i-1] cov_params dates detomega df_model (int) df_resid (int) endog endog_lagged fittedvalues fpe intercept info_criteria k_ar (int) k_trend (int) llf model names neqs (int) – Number of variables (equations) nobs (int) n_totobs (int) params k_ar (int) – Order of VAR process params (ndarray (Kp + 1) x K) – A_i matrices and intercept in stacked form [int A_1 … A_p] pvalues names (list) – variables names resid roots (array) – The roots of the VAR process are the solution to (I - coefs[0]*z - coefs[1]*z**2 … - coefs[p-1]*z**k_ar) = 0. Note that the inverse roots are returned, and stability requires that the roots lie outside the unit circle. sigma_u (ndarray (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])	Compute theoretical autocorrelation function
bse()	Standard errors of coefficients, reshaped to match in size
cov_params()	Estimated variance-covariance of model coefficients
cov_ybar()	Asymptotically consistent estimate of covariance of the sample mean
detomega()	Return determinant of white noise covariance with degrees of freedom correction:
fevd([periods, var_decomp])	Compute forecast error variance decomposition (“fevd”)
fittedvalues()	The predicted insample values of the response variables of the model.
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
info_criteria()	information criteria for lagorder selection
intercept_longrun()	Long run intercept of stable VAR process
irf([periods, var_decomp, var_order])	Analyze impulse responses to shocks in system
irf_errband_mc([orth, repl, T, signif, …])	Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions
irf_resim([orth, repl, T, seed, burn, cum])	Simulates impulse response function, returning an array of simulations.
is_stable([verbose])	Determine stability based on model coefficients
llf()	Compute VAR(p) loglikelihood
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])	Compute orthogonalized MA coefficient matrices using P matrix such that \(\Sigma_u = PP^\prime\).
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 theoretical autocorrelation function
plotsim([steps, offset, seed])	Plot a simulation from the VAR(p) process for the desired number of steps
pvalues()	Two-sided p-values for model coefficients from Student t-distribution
pvalues_dt()
pvalues_endog_lagged()
reorder(order)	Reorder variables for structural specification
resid()	Residuals of response variable resulting from estimated coefficients
resid_acorr([nlags])	Compute sample autocorrelation (including lag 0)
resid_acov([nlags])	Compute centered sample autocovariance (including lag 0)
resid_corr()	Centered residual correlation matrix
roots()
sample_acorr([nlags])
sample_acov([nlags])
sigma_u_mle()	(Biased) maximum likelihood estimate of noise process covariance
simulate_var([steps, offset, seed])	simulate the VAR(p) process for the desired number of steps
stderr()	Standard errors of coefficients, reshaped to match in size
stderr_dt()
stderr_endog_lagged()
summary()	Compute console output summary of estimates
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()
tvalues()	Compute t-statistics.
tvalues_dt()
tvalues_endog_lagged()

Attributes

aic	Akaike information criterion
bic	Bayesian a.k.a.
df_model	Number of estimated parameters, including the intercept / trends
df_resid	Number of observations minus number of estimated parameters
fpe	Final Prediction Error (FPE)
hqic	Hannan-Quinn criterion