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
dates : |
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Returns : | **Attributes** : aic : bic : bse : coefs : ndarray (p x K x K)
coef_names : cov_params : dates : detomega : df_model : int df_resid : int endog : endog_lagged : fittedvalues : fpe : intercept : info_criteria : k_ar : int k_trend : int llf : model : names : neqs : int
nobs : int n_totobs : int params : k_ar : int
params : ndarray (Kp + 1) x K
pvalues : names : list
resid : sigma_u : ndarray (K x K)
sigma_u_mle : stderr : trenorder : tvalues : y : : ys_lagged : |
Methods
acf([nlags]) | Compute theoretical autocovariance function |
acorr([nlags]) | Compute theoretical autocorrelation function |
cov_ybar() | Asymptotically consistent estimate of covariance of the sample mean .. |
fevd([periods, var_decomp]) | Compute forecast error variance decomposition (“fevd”) |
forecast(y, steps) | Produce linear minimum MSE forecasts for desired number of steps |
forecast_cov([steps]) | Compute forecast covariance matrices for desired number of steps |
forecast_interval(y, steps[, alpha]) | Construct forecast interval estimates assuming the y are Gaussian |
get_eq_index(name) | Return integer position of requested equation name |
irf([periods, var_decomp, var_order]) | Analyze impulse responses to shocks in system |
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() | Mean of stable process |
mse(steps) | Compute theoretical forecast error variance matrices |
orth_ma_rep([maxn, P]) | Compute Orthogonalized MA coefficient matrices using P matrix such |
plot() | Plot input time series |
plot_acorr([nlags, linewidth]) | Plot theoretical autocorrelation function |
plot_forecast(steps[, alpha, plot_stderr]) | Plot forecast |
plot_sample_acorr([nlags, linewidth]) | Plot theoretical autocorrelation function |
plotsim([steps]) | Plot a simulation from the VAR(p) process for the desired number of |
resid_acorr([nlags]) | Compute sample autocorrelation (including lag 0) |
resid_acov([nlags]) | Compute centered sample autocovariance (including lag 0) |
sample_acorr([nlags]) | |
sample_acov([nlags]) | |
summary() | Compute console output summary of estimates |
test_causality(equation, variables[, kind, ...]) | Compute test statistic for null hypothesis of Granger-noncausality, |
test_normality([signif, verbose]) | Test assumption of normal-distributed errors using Jarque-Bera-style |
test_whiteness([nlags, plot, linewidth]) | Test white noise assumption. |
Attributes
aic | Akaike information criterion |
bic | Bayesian a.k.a. |
df_model | Number of estimated parameters, including the intercept / trends |
df_resid | Number of observations minus number of estimated parameters |
fpe | Final Prediction Error (FPE) |
hqic | Hannan-Quinn criterion |