@@ -1216,7 +1216,7 @@ G = [[γ + g, ρ1, ρ2], # this is Y_{t+1}
12161216 [γ, α, 0], # this is C_{t+1}
12171217 [0, β, -β]] # this is I_{t+1}
12181218
1219- μ_0 = [1, 100, 100 ]
1219+ μ_0 = [1, 100, 50 ]
12201220C = np.zeros((3,1))
12211221C[1] = σ # stochastic
12221222
@@ -1273,7 +1273,7 @@ class SamuelsonLSS(LinearStateSpace):
12731273 """
12741274 def __init__(self,
12751275 y_0=100,
1276- y_1=100 ,
1276+ y_1=50 ,
12771277 α=0.8,
12781278 β=0.9,
12791279 γ=10,
@@ -1309,17 +1309,17 @@ class SamuelsonLSS(LinearStateSpace):
13091309 def plot_simulation(self, ts_length=100, stationary=True):
13101310
13111311 # Temporarily store original parameters
1312- temp_μ = self.μ_0
1313- temp_Σ = self.Sigma_0
1312+ temp_mu = self.mu_0
1313+ temp_Sigma = self.Sigma_0
13141314
13151315 # Set distribution parameters equal to their stationary
13161316 # values for simulation
13171317 if stationary == True:
13181318 try:
1319- self.μ_x , self.μ_y , self.σ_x , self.σ_y , self.σ_yx = \
1319+ self.mu_x , self.mu_y , self.Sigma_x , self.Sigma_y , self.Sigma_yx = \
13201320 self.stationary_distributions()
1321- self.μ_0 = self.μ_y
1322- self.Σ_0 = self.σ_y
1321+ self.mu_0 = self.mu_x
1322+ self.Sigma_0 = self.Sigma_x
13231323 # Exception where no convergence achieved when
13241324 #calculating stationary distributions
13251325 except ValueError:
@@ -1338,8 +1338,8 @@ class SamuelsonLSS(LinearStateSpace):
13381338 axes[-1].set_xlabel('Iteration')
13391339
13401340 # Reset distribution parameters to their initial values
1341- self.μ_0 = temp_μ
1342- self.Sigma_0 = temp_Σ
1341+ self.mu_0 = temp_mu
1342+ self.Sigma_0 = temp_Sigma
13431343
13441344 return fig
13451345
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