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
Attributes
resid
()Residuals of response variable resulting from estimated coefficients tvalues
()Compute t-statistics. coefs (ndarray (p x K x K)) Estimated A_i matrices, A_i = coefs[i-1] dates endog endog_lagged k_ar (int) Order of VAR process k_trend (int) model names neqs (int) Number of variables (equations) nobs (int) n_totobs (int) params (ndarray (Kp + 1) x K) A_i matrices and intercept in stacked form [int A_1 … A_p] names (list) variables names sigma_u (ndarray (K x K)) Estimate of white noise process variance Var[u_t] y : ys_lagged Methods
acf
([nlags])Compute theoretical autocovariance function acorr
([nlags])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, 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 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 sample 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
()pvalues_endog_laggd 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
()The roots of the VAR process are the solution to (I - coefs[0]*z - coefs[1]*z**2 … sample_acorr
([nlags])Sample acorr sample_acov
([nlags])Sample acov 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_dt stderr_endog_lagged
()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
()