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 () | |