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