[QuantEcon]MATLAB混编FORTRAN语言

$$
\begin{Vmatrix} \lambda_1 \\ \lambda_2+9 \end{Vmatrix}
$$

$$
\left[\begin{array}{cccc}
k_{11} & k_{12} & \ldots & k_{1n}\\
k_{21} & k_{22} & \ldots & k_{2n}\\
\cdots\\
k_{n1} & k_{n2} & \ldots & k_{nn}
\end{array}\right]
%
\left\{\begin{array}{c}
x_1\\x_2\\ \cdot\\x_n
\end{array}\right\} =
%
\left\{\begin{array}{l}
f_1+a\\f_2\\ \cdots \\f_n+c
\end{array}\right\}
$$

$$
\sum_{\substack{i=1\\ i\in\Omega_{\text{old}}}}
$$
$$
\max_{\substack{i=1\\ i\in\Omega_{\text{old}}}}
$$

for(i = 1; i <= n-1; i++)
{ for(j = i+1; j <= n; j++)
{ if(a < a[j])
{ tmp = a
a = a[j]
a[j] = tmp
}
}
}

$$
\left [
{y_t} = \sum\limits_{i = 1}^s {\left[ {\left( {{{\phi ‘}_i}y_t^{\left( p \right)} + {{\xi ‘}_i}{x_t} + {{\psi ‘}_i}e_t^{\left( q \right)}} \right){F_i}\left( {{z_{t – d}};{\gamma _i},{\alpha _i},{c_i},{\beta _i}} \right)} \right]} + {\varepsilon _t}
\right ]
$$

$$
\left [
{\pi _{i,t}} = {\gamma _i}\left( {{{\alpha ‘}_i}{z_{t – d}} – {c_i}} \right) + {{\beta ‘}_i}{\pi _{i,t – 1}},\quad \gamma_i>0
\right ]
$$

$$

{y_t} = F\left( {{y_{t – 1}};\theta } \right) + {\varepsilon _t}

$$

$$
s_t = \gamma^*(y_t - \ell_{t}) + (1-\gamma^*)s_{t-m}.
$$

$$s_t = \gamma^*(1-\alpha)(y_t - \ell_{t-1}-b_{t-1}) +
\{1-\gamma^*(1-\alpha)\}s_{t-m}.
$$

Let $\mu_t = \hat{y}_t = \ell_{t-1}+b_{t-1}$ denote the one-step
forecast of $y_{t}$ assuming that we know the values of all
parameters. Also, let $\varepsilon_t = y_t - \mu_t$ denote the
one-step forecast error at time $t$. From the equations in
Table \ref{table:pegels}, we find that\vspace*{-15pt}
$$\begin{align}
\label{ss1}
y_t    &= \ell_{t-1} + \phi b_{t-1} + \varepsilon_t\\
\ell_t &= \ell_{t-1} + \phi b_{t-1} + \alpha \varepsilon_t
\label{ss2}\\
b_t    &= \phi b_{t-1} + \beta^*(\ell_t - \ell_{t-1}- \phi b_{t-1})
    = \phi b_{t-1} + \alpha\beta^*\varepsilon_t. \label{ss3}
\end{align}$$