MAINT: Simplify pages and migrate to gh-pages based deployments

.github/workflows/ci.yml

@@ -0,0 +1,19 @@
+name: Build Project [using jupyter-book]
+on: [push]
+jobs:
+  preview:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      # Push website files to netlify
+      - name: Preview Deploy to Netlify
+        uses: nwtgck/actions-netlify@v2
+        with:
+          publish-dir: website
+          production-branch: main
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          deploy-message: "Preview Deploy from GitHub Actions"
+        env:
+          NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
+          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}

.github/workflows/publish.yml

@@ -0,0 +1,18 @@
+name: Assemble & Publish to GH-PAGES
+on:
+  push:
+    tags:
+      - 'publish*'
+jobs:
+  publish:
+    if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Deploy website to gh-pages
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: website
+          cname: dp.quantecon.org

.gitignore

@@ -1,2 +1,4 @@
 .DS_Store
-*./DS_Store
+*./DS_Store
+.ipynb_checkpoints
+__pycache__

LICENSE

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2022, QuantEcon
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

@@ -4,6 +4,15 @@ Public repo for the textbook **Dynamic Programming Volume 1** by [Thomas J.
 Sargent](http://www.tomsargent.com/) and [John
 Stachurski](https://johnstachurski.net/).
 
-## Website
+| Folder | Description |
+|--------|-------------|
+| website | HTML files that build the website |
+| pdf | PDF of the book |
+| code | Public source of `py` and `jl` code |
 
-The website is currently being built with `jekyll`
+## PDF
+
+The PDF is available in `pdf/dp.pdf`
+
+Comments and feedback are very welcome.
+The easiest way to provide feedback is to open an issue above.

code/jl/README.md

@@ -0,0 +1,4 @@
+The source code here is read into the private repo via a symbolic link.
+
+If you edit a jl file here and rerun to produce a PDF, it needs to be shifted to
+code_book/figures, which is also connected to the private repo via a sym link.

code/jl/american_option.jl

@@ -0,0 +1,153 @@
+"""
+Valuation for finite-horizon American call options in discrete time.
+
+"""
+
+include("s_approx.jl")
+using QuantEcon, LinearAlgebra, IterTools
+
+"Creates an instance of the option model with log S_t = Z_t + W_t."
+function create_american_option_model(;
+        n=100, μ=10.0,  # Markov state grid size and mean value
+        ρ=0.98, ν=0.2,  # persistence and volatility for Markov state
+        s=0.3,          # volatility parameter for W_t
+        r=0.01,         # interest rate 
+        K=10.0, T=200)  # strike price and expiration date
+    t_vals = collect(1:T+1)
+    mc = tauchen(n, ρ, ν)
+    z_vals, Q = mc.state_values .+ μ, mc.p
+    w_vals, φ, β = [-s, s], [0.5, 0.5], 1 / (1 + r)
+    e(t, i_w, i_z) = (t ≤ T) * (z_vals[i_z] + w_vals[i_w] - K)
+    return (; t_vals, z_vals, w_vals, Q, φ, T, β, K, e)
+end
+
+"The continuation value operator."
+function C(h, model)
+    (; t_vals, z_vals, w_vals, Q, φ, T, β, K, e) = model
+    Ch = similar(h)
+    z_idx, w_idx = eachindex(z_vals), eachindex(w_vals)
+    for (t, i_z) in product(t_vals, z_idx)
+        out = 0.0
+        for (i_w′, i_z′) in product(w_idx, z_idx)
+            t′ = min(t + 1, T + 1)
+            out += max(e(t′, i_w′, i_z′), h[t′, i_z′]) * 
+                       Q[i_z, i_z′] * φ[i_w′]
+        end
+        Ch[t, i_z] = β * out
+    end
+    return Ch
+end
+
+"Compute the continuation value function by successive approx."
+function compute_cvf(model) 
+    h_init = zeros(length(model.t_vals), length(model.z_vals))
+    h_star = successive_approx(h -> C(h, model), h_init)
+    return h_star
+end
+
+
+# Plots
+
+using PyPlot
+using LaTeXStrings
+PyPlot.matplotlib[:rc]("text", usetex=true) # allow tex rendering
+fontsize=16
+
+
+function plot_contours(; savefig=false, 
+                         figname="./figures/american_option_1.pdf")
+
+    model = create_american_option_model()
+    (; t_vals, z_vals, w_vals, Q, φ, T, β, K, e) = model
+    h_star = compute_cvf(model)
+
+    fig, axes = plt.subplots(3, 1, figsize=(7, 11))
+    z_idx, w_idx = eachindex(z_vals), eachindex(w_vals)
+    H = zeros(length(w_vals), length(z_vals))
+
+    for (ax_index, t) in zip(1:3, (1, 195, 199))
+
+        ax = axes[ax_index, 1]
+
+        for (i_w, i_z) in product(w_idx, z_idx)
+            H[i_w, i_z] = e(t, i_w, i_z) - h_star[t, i_z]
+        end
+
+        cs1 = ax.contourf(w_vals, z_vals, transpose(H), alpha=0.5)
+        ctr1 = ax.contour(w_vals, z_vals, transpose(H), levels=[0.0])
+        plt.clabel(ctr1, inline=1, fontsize=13)
+        plt.colorbar(cs1, ax=ax) #, format="%.6f")
+
+        ax.set_title(L"t=" * "$t", fontsize=fontsize)
+        ax.set_xlabel(L"w", fontsize=fontsize)
+        ax.set_ylabel(L"z", fontsize=fontsize)
+
+    end
+
+    fig.tight_layout()
+    if savefig
+        fig.savefig(figname)
+    end
+    plt.show()
+end
+
+
+function plot_strike(; savefig=false, 
+                       fontsize=12,
+                       figname="./figures/american_option_2.pdf")
+
+    model = create_american_option_model()
+    (; t_vals, z_vals, w_vals, Q, φ, T, β, K, e) = model
+    h_star = compute_cvf(model)
+
+    # Built Markov chains for simulation 
+    z_mc = MarkovChain(Q, z_vals)
+    P_φ = zeros(length(w_vals), length(w_vals))
+    for i in eachindex(w_vals)  # Build IID chain
+        P_φ[i, :] = φ
+    end
+    w_mc = MarkovChain(P_φ, w_vals)
+
+
+    y_min = minimum(z_vals) + minimum(w_vals)
+    y_max = maximum(z_vals) + maximum(w_vals)
+    fig, axes = plt.subplots(3, 1, figsize=(7, 12))
+
+    for ax in axes
+
+        # Generate price series
+        z_draws = simulate_indices(z_mc, T, init=Int(length(z_vals) / 2 - 10))
+        w_draws = simulate_indices(w_mc, T)
+        s_vals = z_vals[z_draws] + w_vals[w_draws]
+
+        # Find the exercise date, if any.
+        exercise_date = T + 1
+        for t in 1:T
+            if e(t, w_draws[t], z_draws[t]) ≥ h_star[w_draws[t], z_draws[t]]
+                exercise_date = t
+            end
+        end
+
+        @assert exercise_date ≤ T "Option not exercised."
+
+        # Plot
+        ax.set_ylim(y_min, y_max)
+        ax.set_xlim(1, T)
+        ax.fill_between(1:T, ones(T) * K, ones(T) * y_max, alpha=0.2)
+        ax.plot(1:T, s_vals, label=L"S_t")
+        ax.plot((exercise_date,), (s_vals[exercise_date]), "ko")
+        ax.vlines((exercise_date,), 0, (s_vals[exercise_date]), ls="--", colors="black")
+        ax.legend(loc="upper left", fontsize=fontsize)
+        ax.text(-10, 11, "in the money", fontsize=fontsize, rotation=90)
+        ax.text(-10, 7.2, "out of the money", fontsize=fontsize, rotation=90)
+        ax.text(exercise_date-20, 6, #s_vals[exercise_date]+0.8, 
+                "exercise date", fontsize=fontsize)
+        ax.set_xticks((1, T))
+        ax.set_yticks((y_min, y_max))
+    end
+
+    if savefig
+        fig.savefig(figname)
+    end
+    plt.show()
+end

code/jl/ar1_spec_rad.jl

@@ -0,0 +1,97 @@
+""" 
+
+Compute r(L) for model
+
+    Zₜ = μ (1 - ρ) + ρ Zₜ₋₁ + σ εₜ
+    β_t = b(Z_t)
+
+The process is discretized using the Tauchen method with n states.
+"""
+
+using LinearAlgebra, QuantEcon
+
+function compute_mc_spec_rad(n, ρ, σ, μ, m, b)
+    mc = tauchen(n, ρ, σ, μ * (1 - ρ), m)
+    state_values, P = mc.state_values, mc.p
+
+    L = zeros(n, n)
+    for i in 1:n
+        for j in 1:n
+            L[i, j] = b(state_values[i]) * P[i, j]
+        end
+    end
+    r = maximum(abs.(eigvals(L)))
+    return r
+end
+
+# Hubmer et al parameter values, p. 24 of May 17 2020 version.
+
+n = 15
+ρ = 0.992
+σ = 0.0006
+μ = 0.944 
+m = 4
+b(z) = z
+
+println("Spectral radius of L in Hubmer et al.:")
+println(compute_mc_spec_rad(n, ρ, σ, μ, m, b))
+
+# ## Hills et al 2019 EER
+
+# For the empirical model,
+#
+# $$
+#     Z_{t+1} = 1 - \rho + \rho Z_t + \sigma \epsilon_{t+1},
+#     \quad \beta_t = \beta Z_t
+# $$
+#
+# with 
+#
+# $$
+#     \beta = 0.99875, \; \rho = 0.85, \; \sigma = 0.0062
+# $$ 
+#
+# They use 15 grid points on $[1-4.5\sigma_\delta, 1+4.5\sigma_\delta]$.
+
+n = 15
+ρ = 0.85
+σ = 0.0062
+μ = 1
+m = 4.5
+beta = 0.99875
+b(z) = beta * z
+
+println("Spectral radius of L in Hills et al.:")
+println(compute_mc_spec_rad(n, ρ, σ, μ, m, b))
+
+# Let's run a simulation of the discount process.
+# Plots
+
+using PyPlot
+using LaTeXStrings
+PyPlot.matplotlib[:rc]("text", usetex=true) # allow tex rendering
+fs=14
+
+function plot_beta_sim(; T=80,
+                         savefig=false, 
+                         figname="./figures/ar1_spec_rad.pdf")
+    β_vals = zeros(T)
+    Z = 1
+    for t in 1:T
+        β_vals[t] = beta * Z
+        Z = 1 - ρ + ρ * Z + σ * randn()
+    end
+
+    fig, ax = plt.subplots(figsize=(6, 3.8))
+
+    ax.plot(β_vals, label=L"\beta_t")
+    ax.plot(1:T, ones(T), "k--", alpha=0.5, label=L"\beta=1")
+    ax.set_yticks((0.97, 1.0, 1.03))
+    ax.set_xlabel("time")
+    ax.legend(frameon=false, fontsize=fs)
+
+    if savefig
+        fig.savefig(figname)
+    end
+    plt.show()
+end