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)

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]

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
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)	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
info_criteria()	information criteria for lagorder selection
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
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() 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 that .
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
pvalues()	Two-sided p-values for model coefficients from Student t-distribution
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
stderr()	Standard errors of coefficients, reshaped to match in size
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.
tvalues()	Compute t-statistics.

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