pyfan.stats.interpolate.interpolate2d.inter_states_bp

pyfan.stats.interpolate.interpolate2d.inter_states_bp(prod_inst, util_opti, b_ssv_sd, k_ssv_sd, epsilon_ssv_sd, b_ssv, k_ssv, epsilon_ssv, b_ssv_zr, k_ssv_zr, epsilon_ssv_zr, states_vfi_dim, shocks_vfi_dim)

interpolate value function and expected value function.

Need three matrix here: 1. state matrix x shock matrix where optimal choices were solved at

• previously, shock for this = 0, but now shock vector might not be zero

2. state matrix x shock matrix where shocks are drawn monte carlo way to allow

   for averaging, integrating over shocks for each x row

3. state matrix alone, shock = 0, each of the x row in matrix x