Forecast Error Variance Share

Problem

Compute the share of forecast error variance at a horizon attributable to one variable, given its MA coefficient path own_psi and the coefficients of the other shocks other_psi: sum(own^2) / (sum(own^2) + sum(other^2)).

Return 0.0 when all coefficients are zero.

Hint

Squared MA coefficients measure each shock contribution to the forecast error.

Test cases

case 1

in: [[1,0.5],[0,0.5]]

expected: 0.8333333333333334

case 2

in: [[1],[1]]

expected: 0.5

case 3

in: [[],[]]

expected: 0

case 4

in: [[0.3,0.4],[0.5,0.12]]

expected: 0.48600311041990674

Starter codePython

def forecast_error_variance_share(own_psi, other_psi):
    # Your code here
    pass

4 test cases · run your code against them in the editor