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Modify the callings of LinearStateSpace.stationary_distributions (#123)
* update QuantEcon.py QuantEcon/QuantEcon.py#569 * catch execution errors for CI * TEST: test against new release of QuantEcon * add .git for install from quantecon.py@new-release * use correct branch name * update environment
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.github/workflows/ci.yml

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@@ -31,7 +31,7 @@ jobs:
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- name: Build HTML
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shell: bash -l {0}
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run: |
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jb build lectures --path-output ./
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jb build -W --keep-going lectures --path-output ./
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- name: Save Build as Artifact
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uses: actions/upload-artifact@v1
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with:

environment.yml

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@@ -8,7 +8,7 @@ dependencies:
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- pip:
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- jupyter-book
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- sphinx-multitoc-numbering
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- git+https://github.com/quantecon/quantecon-book-theme
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- quantecon-book-theme
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- sphinx-tojupyter
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- sphinxext-rediraffe
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- sphinx-exercise

lectures/linear_models.md

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@@ -734,7 +734,7 @@ sample_size = 500_000
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ar = LinearStateSpace(A_2, C_2, G_2, mu_0=np.ones(4))
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fig, ax = plt.subplots()
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x, y = ar.simulate(sample_size)
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mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()
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mu_x, mu_y, Sigma_x, Sigma_y, Sigma_yx = ar.stationary_distributions()
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f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y)))
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y = y.flatten()
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ygrid = np.linspace(ymin, ymax, 150)
@@ -920,7 +920,7 @@ def cross_plot(A,
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ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
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if steady_state == 'True':
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μ_x, μ_y, Σ_x, Σ_y = ar.stationary_distributions()
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μ_x, μ_y, Σ_x, Σ_y, Σ_yx = ar.stationary_distributions()
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ar_state = LinearStateSpace(A, C, G, mu_0=μ_x, Sigma_0=Σ_x)
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ymin, ymax = -0.6, 0.6

lectures/perm_income_cons.md

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@@ -345,7 +345,7 @@ G = np.array([[0., 1., 0.]])
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μ_z0 = np.array([[1.0], [0.0], [0.0]])
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Σ_z0 = np.zeros((3, 3))
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Lz = qe.LinearStateSpace(A, C, G, mu_0=μ_z0, Sigma_0=Σ_z0)
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μ_z, μ_y, Σ_z, Σ_y = Lz.stationary_distributions()
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μ_z, μ_y, Σ_z, Σ_y, Σ_yx = Lz.stationary_distributions()
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# Mean vector of state for the savings problem
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mxo = np.vstack([μ_z, 0.0])

lectures/samuelson.md

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@@ -1316,7 +1316,7 @@ class SamuelsonLSS(LinearStateSpace):
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# values for simulation
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if stationary == True:
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try:
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self.μ_x, self.μ_y, self.σ_x, self.σ_y = \
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self.μ_x, self.μ_y, self.σ_x, self.σ_y, self.σ_yx = \
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self.stationary_distributions()
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self.μ_0 = self.μ_y
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self.Σ_0 = self.σ_y

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