估计Hessian和以下错误：ValueError：使用序列设置数组元素

Question

我想估计对数似然函数的Hessian。我估计了有马模型和garch模型的参数。然后我使用粗麻布功能找到粗麻布。但是现在我得到了错误：ValueError：用序列设置数组元素。 eps出错了。

def main():
    R_bel = stationary(data_bel)
    vP0 = (0.01,0.6,0.02,0.01,0.01, 0.06, 0.90)
    b = minimize(garch_loglike, vP0, R_bel, bounds = ((None,None),(None,None),(None,None),(None,None),(0.0001, None), (0.0001, None), (0.0001, None)), options={'disp':True})
    print(b.x)
    value = garch_loglike(b.x, R_bel)
    print("Log likelihood value:", -value)
    hessian_ = hessian(garch_loglike)
    print(hessian_(b.x, R_bel))
def garch_filter(omega, alpha, beta, eps, R):
    iT = len(R)-8
    sigma_2 = np.zeros(iT)

    for i in range(iT):
        if i==0:
            sigma_2[i] = omega + alpha*eps[i-1]**2 + beta*np.var(eps)
        else:
            sigma_2[i] = omega + alpha*eps[i-1]**2 + beta*sigma_2[i-1]
    return sigma_2
def eps1(a, b, c, d, R):
    eps = np.zeros(len(R)-8)
    for t in range(len(R)-8):
        if t ==0:
            eps[t] = R[1]-R[0]
        else:
            eps[t] = R[t+8]-a - b*R[t+2] - c*R[t] -d*eps[t-1]
    return eps
def garch_loglike(vP, R):
    iT = len(R)
    a = vP[0]
    b=vP[1]
    c=vP[2]
    d=vP[3]
    omega = vP[4]
    alpha = vP[5]
    beta = vP[6]


    eps = eps1(a,b,c,d,R)
    sigma_2 = garch_filter(omega, alpha, beta, eps, R)
    logL = -(-len(eps)/2 * np.log(2*math.pi) -0.5 *np.sum(np.log(sigma_2) + eps**2/sigma_2))

    return logL
def stationary(data):
    S=data
    R = np.zeros(len(S))
    for i in range(len(S)-1):
        R[i+1] = np.log(S[i+1] / S[i]) * 100
    R = np.delete(R, [0])
    return R

如何解决此错误？ 谢谢！