The spot price of an asset depends primarily on

* the anticipated income stream

* the anticipated income stream

* attitudes about risk

* rates of time preference

* $g_t = \exp(X_t)$, so that $\ln g_t = X_t$ is the growth rate.

```{code-cell} ipython

mc = qe.tauchen(0.96, 0.25, n=25)

n = 7

mc = qe.tauchen(n, 0.96, 0.25)

sim_length = 80

x_series = mc.simulate(sim_length, init=np.median(mc.state_values))

```{code-cell} python3

n = 25 # Size of state space

β = 0.9

mc = qe.tauchen(0.96, 0.02, n=n)

mc = qe.tauchen(n, 0.96, 0.02)

K = mc.P * np.exp(mc.state_values)

if mc is None:

self.ρ = 0.9

self.σ = 0.02

self.mc = qe.tauchen(self.ρ, self.σ, n=25)

self.mc = qe.tauchen(n, self.ρ, self.σ)

else:

self.mc = mc

Consider the following primitives

```{code-cell} python3

n = 5

n = 5 # Size of State Space

P = np.full((n, n), 0.0125)

P[range(n), range(n)] += 1 - P.sum(1)

# State values of the Markov chain