import matplotlib.pyplot as plt

import numpy as np

from quantecon import Kalman, LinearStateSpace

from collections import namedtuple

from typing import NamedTuple

from scipy.stats import multivariate_normal

import matplotlib as mpl

mpl.rcParams['text.usetex'] = True

0 & \sigma_{u,0}^2 \end{bmatrix}

```

To compute the firm's wage setting policy, we first create a `namedtuple` to store the parameters of the model

To compute the firm's wage setting policy, we first create a `NamedTuple` to store the parameters of the model

```{code-cell} ipython3

WorkerModel = namedtuple("WorkerModel",

('A', 'C', 'G', 'R', 'xhat_0', 'Σ_0'))

class WorkerModel(NamedTuple):

A: np.ndarray

C: np.ndarray

G: np.ndarray

R: float

xhat_0: np.ndarray

Σ_0: np.ndarray

def create_worker(α=.8, β=.2, c=.2,

R=.5, g=1.0, hhat_0=4, uhat_0=4,

return WorkerModel(A=A, C=C, G=G, R=R, xhat_0=xhat_0, Σ_0=Σ_0)

```

Please note how the `WorkerModel` namedtuple creates all of the objects required to compute an associated

Please note how the `WorkerModel` NamedTuple creates all of the objects required to compute an associated

state-space representation {eq}`ssrepresent`.

This is handy, because in order to simulate a history $\{y_t, h_t\}$ for a worker, we'll want to form

```

More generally, we can change some or all of the parameters defining a worker in our `create_worker`

namedtuple.

factory function.

Here is an example.