[QuantEcon]MATLAB混编FORTRAN语言

tulipsliu

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收藏 2020-12-09

- DisplayTables.m reads the Tables data from the results subfolder and prints them on screen using the same format and ordering as in the paper (without row names).

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source files

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tulipsliu

2020-12-9 18:07:44

18：07 开始运行，看看最后的运行时间多少；
MATLAB 混编 FORTRAN 语言；

pool =

'local'

Running Value Function Iteration...
Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).
Distance after 20 iterations: 0.88956
Distance after 40 iterations: 0.23221

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tulipsliu

2020-12-9 18:14:45

pool =

'local'

Running Value Function Iteration...
Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).
Distance after 20 iterations: 0.88956
Distance after 40 iterations: 0.23221
Distance after 60 iterations: 0.084232
Distance after 80 iterations: 0.036514
Distance after 100 iterations: 0.017516
Distance after 120 iterations: 0.0088954
Distance after 140 iterations: 0.0046623
Distance after 160 iterations: 0.0024859
Distance after 180 iterations: 0.0013377
Value Function Iteration in iteration 1 completed
Running time : 610.1224 seconds.

Running Simulation...

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tulipsliu

2020-12-9 18:45:46

Running Simulation...
Simulation in iteration 1 completed
Running time : 1739.3256 seconds.

================================================================
Plant-level investment distribution
CP-dist: 0.87518 0.10234 0.020394 0.10382 0.020994
Skewness : 2.776
Kurtosis : 12.2762
================================================================

================================================================
Iteration 1 completed
Minimum P-value : 0.98544
Maximum absolute change in LOMs : 0.00018834
================================================================

ans =

1.6723

ans =

1.4505

ans =

57.2594

ans =

0.0175

Running Value Function Iteration...

18:45 开始运行 Value Function Iteration ， VFI 值函数迭代。

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tulipsliu

2020-12-9 19:08:32

19：09

Running Value Function Iteration...
Value Function Iteration in iteration 1 completed
Running time : 48.5786 seconds.

Running Simulation...
Simulation in iteration 1 completed
Running time : 14.5523 seconds.

================================================================
Phi =0
Iteration 1 completed
Minimum P-value : 0.94621
Maximum absolute change in LOMs : 0.00036588
================================================================

ans =

1.6083

ans =

1.3989

ans =

2.6391

ans =

1.1545

--------------------------------------
Table 2
--------------------------------------
GDP STD
--------------------------------------
1.9600 1.4960 1.4694 1.4720

--------------------------------------
rel Var
--------------------------------------
2.8200 3.5592 3.2460 3.2486
1.0300 0.6071 0.6203 0.6201
0.7200 0.4761 0.4540 0.4558
2.2500 1.0299 1.0079 1.0109
3.0200 1.0317 1.0102 1.0123
0.5000 0.2438 0.2012 0.1889
2.0800 0.8083 0.8186 0.8186

--------------------------------------
Correlation
--------------------------------------
0.8500 0.8569 0.8838 0.8849
0.8200 0.9403 0.9499 0.9487
0.6100 0.9011 0.9044 0.9030
0.3800 0.8372 0.8953 0.8996
0.7900 0.6404 0.6294 0.6279
-0.4300 -0.2074 -0.1733 -0.1619
-0.0600 0.4596 0.4616 0.4592

--------------------------------------
Auto Correlation
--------------------------------------
0.6500 0.2848 0.3241 0.3217
0.6800 0.5817 0.5655 0.5715
0.5700 0.3594 0.3800 0.3770
0.4900 0.5573 0.5606 0.5600
0.5000 0.5591 0.5630 0.5620
0.5700 0.3235 0.4126 0.4187
0.4500 0.6028 0.5704 0.5683

--------------------------------------

par =

  struct with fields:

         theta: 0.2500
            nu: 0.5000
         gamma: 1.0140
         delta: 0.0940
            b: 0
      disttype: 'uniform'
         xibar: 0.2000
            p: 1
      rho_eps: 0.9675
      sigma_eps: 0.0905
         sige1: 0.0586
         sige2: 0.1224
         weps: 0.5882
update_weight: 1
         beta: 0.9700
            A: 1.8604
         sigma: 1.5000
         omega: 0.7000
      rho_zdiff: 0.7975
   sigma_zdiff: 6.6767e-04
      rho_zsum: 0.9801
   sigma_zsum: 0.0234
            kss: 1.0479

Running Value Function Iteration...
Distance after 20 iterations: 0.86682
Distance after 40 iterations: 0.22853
Distance after 60 iterations: 0.08331
Distance after 80 iterations: 0.036195
Distance after 100 iterations: 0.017381

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tulipsliu

2020-12-9 19:09:59

已经设定为只收论坛币 50 ；

是为了向我的论坛好友 epoh 大神致敬；
我和 epoh 认识快十年了；在我的心里他一直是神一样的存在，论坛第一。
不多说了；本来刚才修改帖子，这段话是放在帖子主页里的。结果修改收论坛币 50 ，那段话消失了。

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tulipsliu

2020-12-9 19:16:06

19：16

Running Value Function Iteration...
Distance after 20 iterations: 0.86682
Distance after 40 iterations: 0.22853
Distance after 60 iterations: 0.08331
Distance after 80 iterations: 0.036195
Distance after 100 iterations: 0.017381
Distance after 120 iterations: 0.0088322
Distance after 140 iterations: 0.0046307
Distance after 160 iterations: 0.0024695
Distance after 180 iterations: 0.0013291
Distance after 200 iterations: 0.00071879
Distance after 220 iterations: 0.00038974
Distance after 240 iterations: 0.00021162
Value Function Iteration in iteration 1 completed
Running time : 778.6659 seconds.

Running Simulation...

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tulipsliu

2020-12-9 19:46:30

19:46

运行时间很长。哈哈

Running Simulation...
Simulation in iteration 1 completed
Running time : 1440.2181 seconds.

================================================================
Plant-level investment distribution
CP-dist: 0.87577 0.10208 0.020195 0.10342 0.020808
Skewness : 2.7878
Kurtosis : 12.3222
================================================================

================================================================
Iteration 1 completed
Minimum P-value : 0.042698
Maximum absolute change in LOMs : 0.0025818
================================================================

ans =

1.6119

ans =

1.4204

ans =

43.3949

ans =

0.0204

Running Value Function Iteration...
Distance after 20 iterations: 6.7428e-05
Value Function Iteration in iteration 2 completed
Running time : 95.1846 seconds.

Running Simulation...

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tulipsliu

2020-12-9 20:42:10

20：42

程序还没运行结束， MATLAB 混编 FORTRAN 语言；

今晚不关机，明早起来看结果；哈哈

Running Value Function Iteration...
Value Function Iteration in iteration 3 completed
Running time : 20.2216 seconds.

Running Simulation...
Simulation in iteration 3 completed
Running time : 10.4571 seconds.

================================================================
Phi =0.48
Iteration 3 completed
Minimum P-value : 0.78002
Maximum absolute change in LOMs : 0.00040181
================================================================

ans =

1.5657

ans =

1.3832

ans =

2.0848

ans =

1.0271

Running Value Function Iteration...
Value Function Iteration in iteration 1 completed
Running time : 21.422 seconds.

Running Simulation...

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tulipsliu

2020-12-10 06:29:25

starting worker pid=13184 on localhost:11000 at 22:29:14.841

SAMPLING FOR MODEL 'correlation_ll' NOW (CHAIN 1).
Chain 1: Rejecting initial value:
Chain 1: Error evaluating the log probability at the initial value.
Chain 1: Exception: Exception: Exception: out[9]=0; scale=1.5 alpha=4.24786 pa1=0.24476 pa2=-0.17658 th= 1.8019
4.27661
6.53225
8.16557  (in '/functions/pairwise.stan' at line 28; included from 'model_correlation_ll' at line 3)
  (in '/functions/pairwise.stan' at line 46; included from 'model_correlation_ll' at line 3)
  (in 'model_correlation_ll' at line 76)

Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 400 [  0%]  (Warmup)

SAMPLING FOR MODEL 'correlation_ll' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0.001 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 10 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 400 [  0%]  (Warmup)
Chain 1: Iteration:  40 / 400 [ 10%]  (Warmup)
Chain 2: Iteration:  40 / 400 [ 10%]  (Warmup)
Chain 1: Iteration:  80 / 400 [ 20%]  (Warmup)
Chain 2: Iteration:  80 / 400 [ 20%]  (Warmup)
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Chain 2: Iteration: 120 / 400 [ 30%]  (Warmup)
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Chain 1: Iteration: 201 / 400 [ 50%]  (Sampling)
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Chain 2: Iteration: 201 / 400 [ 50%]  (Sampling)
Chain 1: Iteration: 240 / 400 [ 60%]  (Sampling)
Chain 2: Iteration: 240 / 400 [ 60%]  (Sampling)

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tulipsliu

2020-12-10 06:47:35

$$
y_{i} = X_i \beta_i + u_i, \quad i = 1, 2, \ldots, G ,
$$
where $y_i$ is a vector of the dependent variable,
$X_i$ is a matrix of the exogenous variables,
$\beta_i$ is the coefficient vector and
$u_i$ is a vector of the disturbance terms of the $i$th equation.

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tulipsliu

2020-12-10 06:48:04

$$
\left[ \begin{array}{c}
   y_1 \\ y_2\\ \vdots\\ y_G
\end{array} \right] =
\left[ \begin{array}{cccc}
   X_1 & 0 & \cdots & 0\\
   0 & X_2 & \cdots & 0\\
   \vdots & \vdots & \ddots & \vdots\\
   0 & 0 & \cdots & X_G
\end{array}\right]
\left[ \begin{array}{c}
   \beta_1 \\ \beta_2 \\ \vdots\\ \beta_G
\end{array} \right] +
\left[ \begin{array}{c}
   u_1 \\ u_2 \\ \vdots\\ u_G
\end{array} \right]
$$

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tulipsliu

2020-12-10 06:48:39

$$
\E \left[ u_{it} \, u_{js} \right] = 0
\; \forall \; t \neq s ,
$$

where $i$ and $j$ indicate the equation number
and $t$ and $s$ denote the observation number,
where the number of observations is the same for all equations.

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tulipsliu

2020-12-10 06:50:35

However, we explicitly allow for contemporaneous correlation, i.e.,
$$
\E \left[ u_{it} \, u_{jt} \right] = \sigma_{ij} .
$$
thus, the covariance matrix of all disturbances is
$$
\E \left[ u \, u^\top \right] = \Omega = \Sigma \otimes I_T ,
$$

where $\Sigma = \left[ \sigma_{ij} \right]$ is the (contemporaneous)
disturbance covariance matrix,
$\otimes$ is the Kronecker product,
$I_T$ is an identity matrix of dimension $T$,
and $T$ is the number of observations in each equation.

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tulipsliu

2020-12-10 06:50:53

If the whole system is treated as one single equation,
$\OHat$ in Equation~\ref{eq:cov-ols-wls-sur} is $\sHat^2 I_{G \cdot T}$,
where $\sHat^2$ is an estimator for the variance of all disturbances
$(\sigma^2 = \E [ u_{it}^2 ])$.
If the disturbance terms of the individual equations
are allowed to have different variances,
$\OHat$ in Equation~\ref{eq:cov-ols-wls-sur} is $\SHat \otimes I_T$,
where $\sHat_{ij} = 0 \; \forall \; i \neq j$ and
$\sHat_{ii} = \sHat_i^2$ is the estimated variance
of the disturbance term in the $i$th equation.

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tulipsliu

2020-12-10 09:11:10

$$
\mathbb{E} \beta = \{ \alpha*\gamma \}
$$

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tulipsliu

2020-12-10 09:17:42

$$
\ln m_t = \ln e \left( p_t, U_t \right)
= \alpha_0 + \sum_i \alpha_i \ln p_{it}
+ \frac{1}{2} \sum_i \sum_j \gamma_{ij}^* \ln p_{it} \ln p_{jt}
+ U_t \beta_0 \prod_i p_{it}^{\beta_i},
$$
where
$m_t$ is total expenditure at time $t$,
$p_{it}$ is the price of good $i$ at time $t$,
$U_t$ is the utility level at time $t$,
and $\alpha$, $\beta$ and $\gamma^*$ are coefficients.

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tulipsliu

2020-12-10 09:18:22

$$
x_{it} ( p_t , m_t )
= \frac{m_t}{p_{it}}
\left( \alpha_i + \sum_j \gamma_{ij} \ln p_{jt}
+ \beta_i \ln \left( m_t / P_t \right) \right)
$$
where $x_{it}$ is the consumed quantity of good $i$,
$\gamma_{ij} = \frac{1}{2} ( \gamma_{ij}^{*} + \gamma_{ji}^{*} )$,
and $P_t$ is a translog price index:

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tulipsliu

2020-12-10 09:19:23

$$
\ln P_{t} = \alpha_0 + \sum_i \alpha_i \ln p_{it}
+ \frac{1}{2} \sum_i \sum_j \gamma_{ij} \ln p_{it} \ln p_{jt}
$$

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tulipsliu

2020-12-10 09:34:34

$$
s_{it} ( p_t , m_t )
= \alpha_i + \sum_j \gamma_{ij} \ln p_{jt}
+ \beta_i \ln \left( m_t / P_t \right)
$$

where $s_{it} = x_{it} \, p_{it} / m_t$ is the expenditure share
of good $i$.

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tulipsliu

2020-12-10 09:36:09

$$
w_{it} = \alpha_i + \sum_j \gamma_{ij} \ln p_{jt}
+ \beta_i \ln \left( m_t / P_t \right) + u_{it}
$$
Given the fact that the observed budget shares always sum-up to one
$(\sum_i w_{it} = 1 \; \forall \, t)$,

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tulipsliu

2020-12-13 19:17:10

$$
\begin{array}{l}
{x_{1,t}} = {x_{1,t - 1}} + \sigma_1{u_{1,t}}\\
{x_{2,t}} = \phi {x_{2,t - 1}} + \sigma_2{u_{2,t}}\\
{y_t} = {x_{1,t}} + {x_{2,t}}
\end{array}
$$

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tulipsliu

2020-12-13 19:23:05

$$
\begin{array}{l}
{\phi_{z,t}} = {\phi_{z,t - 1}}\\
{x_{2,z,t}} = {x_{2,z,t - 1}} \phi_{z,t} + 0.8{u_{2,t}}\\
\end{array}
$$

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tulipsliu

2020-12-13 19:33:31

$$
f(x) = \frac{sin(x)}{x}
$$

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tulipsliu

2020-12-13 19:34:46

$$
\sum(x-a)^n \frac{f^{(n )}(a)}{n!}
$$

$$
\left(\begin{array}{c}
-\frac{b+\sqrt{b^2 -4\,a\,c}}{2\,a}\\
-\frac{b-\sqrt{b^2 -4\,a\,c}}{2\,a}
\end{array}\right)
$$

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tulipsliu

2020-12-13 19:39:37

$$
m\,\frac{\partial^2 }{\partial t^2 }\;x\left(t\right)+c\,\frac{\partial }{\partial t}\;x\left(t\right)+k\,x\left(t\right)=F\left(t\right)
$$

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tulipsliu

2020-12-13 19:40:24

$$
m\,\frac{\partial^2 }{\partial t^2 }\;x\left(t\right)+\gamma \,m\,\frac{\partial }{\partial t}\;x\left(t\right)+m\,x\left(t\right)\,{\omega_0 }^2 =F\left(t\right)
$$

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tulipsliu

2020-12-13 19:41:02

$$
\begin{array}{l}
{\left(\frac{{\mathrm{e}}^{-\sigma_1 } }{2}+\frac{{\mathrm{e}}^{\sigma_1 } }{2}-\frac{\gamma \,{\mathrm{e}}^{-\sigma_1 } }{2\,\sqrt{\gamma^2 -4\,{\omega_0 }^2 }}+\frac{\gamma \,{\mathrm{e}}^{\sigma_1 } }{2\,\sqrt{\gamma^2 -4\,{\omega_0 }^2 }}\right)}\,{\mathrm{e}}^{-\frac{\gamma \,t}{2}} \\
\mathrm{}\\
\textrm{where}\\
\mathrm{}\\
\;\;\sigma_1 =\frac{t\,\sqrt{\gamma^2 -4\,{\omega_0 }^2 }}{2}
\end{array}
$$

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tulipsliu

2020-12-13 19:44:52

$$
\left(\begin{array}{c}
\frac{\partial }{\partial t}\;\theta \left(t\right)=\theta_t \left(t\right)\\
\frac{\partial }{\partial t}\;\theta_t \left(t\right)=-{\omega_0 }^2 \,\mathrm{sin}\left(\theta \left(t\right)\right)
\end{array}\right)
$$

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tulipsliu

2020-12-13 19:47:17

$$
\begin{array}{l}
\left(\begin{array}{c}
-\frac{6\,{\left(3\,{x_1 }^2 -1\right)}\,{\left(-{x_1 }^3 +x_1 +x_2 \right)}-2\,x_1 +\frac{8}{3}}{\sigma_1 }\\
\frac{-6\,{x_1 }^3 +6\,x_1 +6\,x_2 }{\sigma_1 }
\end{array}\right)\\
\mathrm{}\\
\textrm{where}\\
\mathrm{}\\
\;\;\sigma_1 ={{\left(x_1 -\frac{4}{3}\right)}}^2 +3\,{{\left(-{x_1 }^3 +x_1 +x_2 \right)}}^2 +1
\end{array}
$$

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扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群