GitHub

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Let's start with some imports:

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```{code-cell} ipython

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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import quantecon as qe

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import matplotlib.pyplot as plt

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%matplotlib inline

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from quantecon.markov import DiscreteDP

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from numba import jit

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```

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```{code-cell} ipython

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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```

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## The AR(1) Model

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We will use the following imports:

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```{code-cell} ipython

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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from interpolation import interp

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from scipy.optimize import minimize_scalar, bisect

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```

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In what follows, we require the following imports:

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```{code-cell} ipython

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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```

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## The Model

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We begin with some imports:

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```{code-cell} ipython

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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import quantecon as qe

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import matplotlib.pyplot as plt

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%matplotlib inline

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from numba import njit, prange

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from quantecon.distributions import BetaBinomial

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from scipy.special import binom, beta

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Let's start with some standard imports:

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```{code-cell} ipython

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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from numba import njit, float64

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from numba.experimental import jitclass

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

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```

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## The Model

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Let's start with some standard imports:

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```{code-cell} ipython

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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from numba import njit, float64

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from numba.experimental import jitclass

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

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```

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## Review of Cass-Koopmans Model

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Let's start with some imports:

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```{code-cell} ipython

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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import quantecon as qe

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import matplotlib.pyplot as plt

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%matplotlib inline

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from interpolation import interp

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from quantecon.optimize import brentq

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from numba import njit, float64

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We'll need the following imports:

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```{code-cell} ipython

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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from sympy import *

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```

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Let's start with some standard imports:

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```{code-cell} ipython

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%matplotlib inline

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import matplotlib.pyplot as plt

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plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

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import numpy as np

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import quantecon as qe

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from interpolation import interp

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from numba import njit, float64

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from numba.experimental import jitclass

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from quantecon.optimize import brentq

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import matplotlib.pyplot as plt

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%matplotlib inline

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```

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## Key Idea

Read the original on github.com ↗