CompEcon

https://randall-romero.github.io/CompEcon/
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CompEcon is a Python toolbox for computational economics that provides numerical methods and...
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### Import scipy distributions

Source: https://randall-romero.github.io/CompEcon/notebooks/math/03%20Major%20Distribution%20CDFs%20and%20PDFs.html

Initial imports and setup for the distribution examples.

```python
from scipy.stats import norm, lognorm, beta, gamma, expon, chi2, f, t, logistic
n = 400
```

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

### Install and Import Required Libraries

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/dp/01%20Timber%20Harvesting%20Model%20-%20Cubic%20Spline%20Approximation.ipynb

This snippet shows the recommended installation command for the CompEcon module and the necessary imports for the example. It uses numpy, matplotlib, and specific classes from compecon.

```bash
!pip install compecon --upgrade
```

```python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from compecon import DPmodel, BasisSpline, NLP
```

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

### Set up DPmodel

Source: https://randall-romero.github.io/CompEcon/notebooks/dp/10%20Water%20Resource%20Management%20Model.html

Create the DPmodel instance with the basis, reward, transition, bounds, shocks, and discount factor.

```python
water_model = DPmodel(basis, reward, transition, bounds,e=e,w=w,
                       x=['Irrigation'],
                       discount=δ )

```

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

### Output of print statements for Rosencrantz root finding

Source: https://randall-romero.github.io/CompEcon/notebooks/slv/02%20Compute%20root%20of%20Rosencrantz%20function.html

Example output from the print statements, showing the starting point used in the run. The actual values depend on the random starting point.

```text
Hundreds of seconds required to compute root of Rosencrantz function
f(x1,x2)= [200*x1*(x2-x1^2)+1-x1;100*(x1^2-x2)] via Newton and Broyden
methods, starting at x1 = -1.66 x2 = -0.22

```

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

### Import CompEcon and set up environment

Source: https://randall-romero.github.io/CompEcon/notebooks/ode/01%20Stability%20of%20Linear%20Homogeneous%20ODEs.html

Import the ODE class from compecon and other required libraries. Set ANIMATED to 0 for static images or a positive number of seconds for videos, which requires ffmpeg.

```python
from compecon import ODE

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

ANIMATED = 3 # static images if 0, videos of ANIMATED seconds if positive. Requires ffmpeg

```

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

### Import CompEcon and plotting libraries

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/qua/50%20Change%20in%20consumer%20surplus.ipynb

Imports the required modules: qnwlege from compecon, numpy, and matplotlib.pyplot. This is the initial setup for the example.

```python
from compecon import qnwlege
import numpy as np
import matplotlib.pyplot as plt
```

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

### Import libraries and set up problem

Source: https://randall-romero.github.io/CompEcon/notebooks/slv/03%20Compute%20fixedpoint%20of%20f%28x%29%20%3D%20sqrt%28x%29.html

Import numpy, pandas, and CompEcon modules. Generate a random starting point and define the fixed-point problem g(x) = sqrt(x) using NLP.

```python
import numpy as np
import pandas as pd
from compecon import tic, toc, NLP

```

```python
xinit = np.random.rand(1) + 0.5

```

```python
def g(x):
    return np.sqrt(x)

problem_as_fixpoint = NLP(g, xinit)

```

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

### Import qnwequi and matplotlib

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/qua/01%20Equidistributed%20sequences%20on%20unit%20square%20in%20R2.ipynb

Imports the `qnwequi` function from the `compecon` package and `matplotlib.pyplot` for plotting. This is the initial setup required to run the examples on this page.

```python
from compecon import qnwequi
import matplotlib.pyplot as plt
```

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

### Install CompEcon library

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/dif/05%20Finite-Difference%20Jacobians%20and%20Hessians.ipynb

Command to install or upgrade the CompEcon library via pip.

```bash
!pip install compecon --upgrade
```

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

### Import CompEcon and NumPy

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/dif/05%20Finite-Difference%20Jacobians%20and%20Hessians.ipynb

Initial setup to import necessary functions and configure NumPy print options.

```python
from compecon import jacobian, hessian
import numpy as np

np.set_printoptions(precision=15)
```

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

### Create DPmodel instance

Source: https://randall-romero.github.io/CompEcon/notebooks/dp/07%20Stochastic%20Optimal%20Economic%20Growth%20Model.html

Instantiates the CompEcon DPmodel with the basis, reward, transition, bounds, shock distribution, and discount factor δ.

```python
growth_model = DPmodel(basis, reward, transition, bounds,e=e,w=w,
                       x=['Investment'],
                       discount=δ )
```

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

### Generate random starting point

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/slv/04%20Compute%20fixedpoint%20of%20f%28x1%2Cx2%29%3D%20%5Bx1%202%2Bx2%203; x1 x2 - 0.5].ipynb

Generates a random starting point for the fixed-point algorithms.

```python
xinit = np.random.randn(2)
```

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

### Set random starting point for root finding

Source: https://randall-romero.github.io/CompEcon/notebooks/slv/02%20Compute%20root%20of%20Rosencrantz%20function.html

Randomly generate a starting point for the root-finding algorithms. The starting point is stored in the problem's x0 attribute and is used by both Newton and Broyden methods.

```python
problem.x0 = np.random.randn(2)

```

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

### Initialize DPmodel

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/dp/03%20Industry%20Entry-Exit%20Model.ipynb

Configures the DPmodel object with the defined basis, profit and transition functions, discrete states, actions, discount factor, and transition matrix.

```python
model = DPmodel(basis,
                profit, transition,
                i=dstates,
                j=dactions,
                discount=0.9, e=e, w=w,
                h=[[0, 0], [1, 1]])
```

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

### Create LCP instance

Source: https://randall-romero.github.io/CompEcon/notebooks/slv/07%20Linear%20complementarity%20problem%20methods.html

Instantiates the LCP problem with matrices M, q and bounds a, b.

```python
L = LCP(M, q, a, b)

```

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

### Install compecon package

Source: https://randall-romero.github.io/CompEcon/_sources/notebooks/interacting.ipynb

Run this command in a notebook cell before running the notebook to install the compecon package.

```bash
!pip install compecon
```
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