scikits.statsmodels.tsa.vector_ar.var_model.VARResults

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

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 :

Returns :

**Attributes** :

aic :

bic :

bse :

coefs : ndarray (p x K x K)

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

coef_names :

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 :

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
cov_ybar()	Asymptotically consistent estimate of covariance of the sample mean ..
fevd([periods, var_decomp])	Compute forecast error variance decomposition (“fevd”)
forecast(y, steps)	Produce linear minimum MSE forecasts for desired number of steps
forecast_cov([steps])	Compute forecast covariance matrices for desired number of steps
forecast_interval(y, steps[, alpha])	Construct forecast interval estimates assuming the y are Gaussian
get_eq_index(name)	Return integer position of requested equation name
irf([periods, var_decomp, var_order])	Analyze impulse responses to shocks in system
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()	Mean of stable process
mse(steps)	Compute theoretical forecast error variance matrices
orth_ma_rep([maxn, P])	Compute Orthogonalized MA coefficient matrices using P matrix such
plot()	Plot input time series
plot_acorr([nlags, linewidth])	Plot theoretical autocorrelation function
plot_forecast(steps[, alpha, plot_stderr])	Plot forecast
plot_sample_acorr([nlags, linewidth])	Plot theoretical autocorrelation function
plotsim([steps])	Plot a simulation from the VAR(p) process for the desired number of
resid_acorr([nlags])	Compute sample autocorrelation (including lag 0)
resid_acov([nlags])	Compute centered sample autocovariance (including lag 0)
sample_acorr([nlags])
sample_acov([nlags])
summary()	Compute console output summary of estimates
test_causality(equation, variables[, kind, ...])	Compute test statistic for null hypothesis of Granger-noncausality,
test_normality([signif, verbose])	Test assumption of normal-distributed errors using Jarque-Bera-style
test_whiteness([nlags, plot, linewidth])	Test white noise assumption.

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