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 : ndarray
endog_lagged : ndarray
params : ndarray
sigma_u : ndarray
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 : ndarray
Attributes
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 resid sigma_u (ndarray (K x K)) Estimate of white noise process variance Var[u_t] tvalues y : ys_lagged Methods
acf
([nlags])Compute theoretical autocovariance function acorr
([nlags])Autocorrelation function cov_params
()Estimated variance-covariance of model coefficients cov_ybar
()Asymptotically consistent estimate of covariance of the sample mean fevd
([periods, var_decomp])Compute forecast error variance decomposition (“fevd”) 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 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 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 reorder
(order)Reorder variables for structural specification resid_acorr
([nlags])Compute sample autocorrelation (including lag 0) resid_acov
([nlags])Compute centered sample autocovariance (including lag 0) sample_acorr
([nlags])Sample acorr sample_acov
([nlags])Sample acov simulate_var
([steps, offset, seed])simulate the VAR(p) process for the desired number of steps 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
()Methods
acf
([nlags])Compute theoretical autocovariance function acorr
([nlags])Autocorrelation function cov_params
()Estimated variance-covariance of model coefficients cov_ybar
()Asymptotically consistent estimate of covariance of the sample mean fevd
([periods, var_decomp])Compute forecast error variance decomposition (“fevd”) 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 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 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 reorder
(order)Reorder variables for structural specification resid_acorr
([nlags])Compute sample autocorrelation (including lag 0) resid_acov
([nlags])Compute centered sample autocovariance (including lag 0) sample_acorr
([nlags])Sample acorr sample_acov
([nlags])Sample acov simulate_var
([steps, offset, seed])simulate the VAR(p) process for the desired number of steps 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
()Properties
aic
Akaike information criterion bic
Bayesian a.k.a. bse
Standard errors of coefficients, reshaped to match in size detomega
Return determinant of white noise covariance with degrees of freedom correction: df_model
Number of estimated parameters, including the intercept / trends df_resid
Number of observations minus number of estimated parameters fittedvalues
The predicted insample values of the response variables of the model. fpe
Final Prediction Error (FPE) hqic
Hannan-Quinn criterion info_criteria
information criteria for lagorder selection llf
Compute VAR(p) loglikelihood pvalues
Two-sided p-values for model coefficients from Student t-distribution pvalues_dt
pvalues_endog_lagged
pvalues_endog_laggd resid
Residuals of response variable resulting from estimated coefficients 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 … sigma_u_mle
(Biased) maximum likelihood estimate of noise process covariance stderr
Standard errors of coefficients, reshaped to match in size stderr_dt
Stderr_dt stderr_endog_lagged
Stderr_endog_lagged tvalues
Compute t-statistics. tvalues_dt
tvalues_endog_lagged
y
ys_lagged