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lectures/_config.yml

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@@ -53,8 +53,8 @@ sphinx:
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og_logo_url: https://assets.quantecon.org/img/qe-og-logo.png
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description: This website presents introductory lectures on computational economics, designed and written by Thomas J. Sargent and John Stachurski.
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keywords: Python, QuantEcon, Quantitative Economics, Economics, Sloan, Alfred P. Sloan Foundation, Tom J. Sargent, John Stachurski
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# analytics:
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# google_analytics_id: UA-54984338-9
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analytics:
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google_analytics_id: G-QDS1YRJNGM
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launch_buttons:
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notebook_interface : classic # The interface interactive links will activate ["classic", "jupyterlab"]
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binderhub_url : https://mybinder.org # The URL of the BinderHub (e.g., https://mybinder.org)

lectures/geom_series.md

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@@ -561,7 +561,7 @@ For a reason soon to be revealed, we assume that $G < R$.
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The **present value** of the lease is
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$$
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\begin{aligned} p_0 & = x_0 + x_1/R + x_2/(R^2) + \ddots \\
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\begin{aligned} p_0 & = x_0 + x_1/R + x_2/(R^2) + \cdots \\
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& = x_0 (1 + G R^{-1} + G^2 R^{-2} + \cdots ) \\
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& = x_0 \frac{1}{1 - G R^{-1}} \end{aligned}
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$$

lectures/intro_supply_demand.md

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@@ -375,7 +375,7 @@ $$
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$$
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$$
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p = i_s(q) := s_0 + s_1 q^{1.2}
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p = i_s(q) := s_0 + s_1 q^{1.8}
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$$
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All parameters are positive, as before.
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$$
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W(q)
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= \int_0^q i_d(x) dx - \int_0^q i_q(x) dx
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= \int_0^q i_d(x) dx - \int_0^q i_s(x) dx
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$$
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Solve the integrals and write a function to compute this quantity numerically
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def excess_demand(q):
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return market.inverse_demand(q) - market.inverse_supply(q)
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equilibrium_q = newton(excess_demand, 0.1)
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equilibrium_q = newton(excess_demand, 0.99)
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print(f"{equilibrium_q: .5f}")
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```
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lectures/networks.md

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ch1_data = qbn_data.introduction()
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export_figures = False
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DG = ch1_data['aircraft_network_2019']
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pos = ch1_data['aircraft_network_2019_pos']
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DG = ch1_data['aircraft_network']
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pos = ch1_data['aircraft_network_pos']
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centrality = nx.eigenvector_centrality(DG)
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node_total_exports = qbn_io.node_total_exports(DG)
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name: financial_network
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tags: [hide-input]
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---
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Z = ch1_data["adjacency_matrix_2019"]["Z"]
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Z_visual= ch1_data["adjacency_matrix_2019"]["Z_visual"]
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countries = ch1_data["adjacency_matrix_2019"]["countries"]
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Z = ch1_data["adjacency_matrix"]["Z"]
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Z_visual= ch1_data["adjacency_matrix"]["Z_visual"]
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countries = ch1_data["adjacency_matrix"]["countries"]
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G = qbn_io.adjacency_matrix_to_graph(Z_visual, countries, tol=0.03)
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