class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, coefs_exog, sigma_u, names=None, _params_info=None)
[source]
Class represents a known VAR(p) process
Parameters: |
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Returns: | |
Return type: |
**Attributes** |
acf ([nlags]) | Compute theoretical autocovariance function |
acorr ([nlags]) | Compute theoretical autocorrelation function |
forecast (y, steps[, exog_future]) | Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y |
forecast_cov (steps) | Compute theoretical forecast error variance matrices |
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 |
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_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 |
simulate_var ([steps, offset, seed]) | simulate the VAR(p) process for the desired number of steps |
to_vecm () |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.vector_ar.var_model.VARProcess.html