# pysteps.timeseries.autoregression.estimate_var_params_yw#

pysteps.timeseries.autoregression.estimate_var_params_yw(gamma, d=0, check_stationarity=True)#

Estimate the parameters of a VAR(p) model

$$\mathbf{x}_{k+1}=\mathbf{\Phi}_1\mathbf{x}_k+ \mathbf{\Phi}_2\mathbf{x}_{k-1}+\dots+\mathbf{\Phi}_p\mathbf{x}_{k-p}+ \mathbf{\Phi}_{p+1}\mathbf{\epsilon}$$

from the Yule-Walker equations using the given correlation matrices $$\mathbf{\Gamma}_0,\mathbf{\Gamma}_1,\dots,\mathbf{\Gamma}_n$$, where n=p.

Parameters
• gamma (list) – List of correlation matrices $$\mathbf{\Gamma}_0,\mathbf{\Gamma}_1,\dots,\mathbf{\Gamma}_n$$. To obtain these matrices, use pysteps.timeseries.correlation.temporal_autocorrelation_multivariate() with window_radius=np.inf.

• d ({0,1}) – The order of differencing. If d=1, the correlation coefficients gamma are assumed to be computed from the differenced time series, which is also done for the resulting parameter estimates.

• check_stationarity (bool) – If True, the stationarity of the resulting VAR(p) process is tested. An exception is thrown if the process is not stationary.

Returns

out – List of VAR(p) coefficient matrices $$\mathbf{\Phi}_1, \mathbf{\Phi}_2,\dots\mathbf{\Phi}_{p+1}$$, where the last matrix corresponds to the innovation term.

Return type

list

Notes

To estimate the parameters of an integrated VARI(p,d) model, compute the correlation coefficients gamma by calling pysteps.timeseries.correlation.temporal_autocorrelation_multivariate() with d>0. Estimation of the innovation parameter $$\mathbf{\Phi}_{p+1}$$ is not currently implemented, and it is set to a zero matrix.