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