"""

self.θ = self.ρ * self.θ + self.σ_θ * w

def gen_aggregates(self):

def gen_aggregates(self, rng):

"""

Generate aggregates based on current beliefs (μ, γ). This

is a simulation step that depends on the draws for F.

"""

F_vals = self.σ_F * np.random.randn(self.num_firms)

F_vals = self.σ_F * rng.standard_normal(self.num_firms)

M = np.sum(self.ψ(F_vals) > 0) # Counts number of active firms

if M > 0:

x_vals = self.θ + self.σ_x * np.random.randn(M)

x_vals = self.θ + self.σ_x * rng.standard_normal(M)

X = x_vals.mean()

else:

X = 0

γ_vec[0] = econ.γ

θ_vec[0] = 0

w_shocks = np.random.randn(sim_length)

rng = np.random.default_rng()

w_shocks = rng.standard_normal(sim_length)

for t in range(sim_length-1):

X, M = econ.gen_aggregates()

X, M = econ.gen_aggregates(rng)

X_vec[t] = X

M_vec[t] = M

θ_vec[t+1] = econ.θ

# Record final values of aggregates

X, M = econ.gen_aggregates()

X, M = econ.gen_aggregates(rng)

X_vec[-1] = X

M_vec[-1] = M

```