6.11.8.1.1. statsmodels.sandbox.tsa.varma.VAR¶

statsmodels.sandbox.tsa.varma.VAR(x, B, const=0)[source]¶

multivariate linear filter

Parameters:

x: (TxK) array

columns are variables, rows are observations for time period

B: (PxKxK) array

b_t-1 is bottom “row”, b_t-P is top “row” when printing B(:,:,0) is lag polynomial matrix for variable 1 B(:,:,k) is lag polynomial matrix for variable k B(p,:,k) is pth lag for variable k B[p,:,:].T corresponds to A_p in Wikipedia

const: float or array (not tested)

constant added to autoregression

Returns:

xhat: (TxK) array

filtered, predicted values of x array

Notes

xhat(t,i) = sum{_p}sum{_k} { x(t-P:t,:) .* B(:,:,i) } for all i = 0,K-1, for all t=p..T

xhat does not include the forecasting observation, xhat(T+1), xhat is 1 row shorter than signal.correlate

References

http://en.wikipedia.org/wiki/Vector_Autoregression http://en.wikipedia.org/wiki/General_matrix_notation_of_a_VAR(p)