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 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, T, signif, …]) | Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions |
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(\(\infty\)) 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 theoretical 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() | |
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 |
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_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() | |