Merge branch 'main' into fix-codefromfile

.github/workflows/ci.yml

@@ -1,4 +1,4 @@
-name: Build Project [using jupyter-book]
+name: Build HTML [using jupyter-book]
 on: [push]
 jobs:
   tests:
@@ -14,17 +14,29 @@ jobs:
           miniconda-version: 'latest'
           python-version: 3.8
           environment-file: environment.yml
-          activate-environment: lecture-python-advanced.myst
+          activate-environment: quantecon
       - name: Display Conda Environment Versions
         shell: bash -l {0}
         run: conda list
       - name: Display Pip Versions
         shell: bash -l {0}
         run: pip list
+      # - name: Download "build" folder (cache)
+      #   uses: dawidd6/action-download-artifact@v2
+      #   with:
+      #     workflow: publish.yml
+      #     branch: main
+      #     name: build-cache
+      #     path: _build
       - name: Build HTML
         shell: bash -l {0}
         run: |
           jb build lectures --path-output ./
+      - name: Save Build as Artifact
+        uses: actions/upload-artifact@v1
+        with:
+          name: _build
+          path: _build
       - name: Preview Deploy to Netlify
         uses: nwtgck/actions-netlify@v1.1
         with:

.github/workflows/publish.yml

@@ -0,0 +1,74 @@
+name: Build & 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@v2
+      - name: Setup Anaconda
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          miniconda-version: 'latest'
+          python-version: 3.8
+          environment-file: environment.yml
+          activate-environment: quantecon
+      - name: Display Conda Environment Versions
+        shell: bash -l {0}
+        run: conda list
+      - name: Display Pip Versions
+        shell: bash -l {0}
+        run: pip list
+      - name: Build HTML
+        shell: bash -l {0}
+        run: |
+          jb build lectures --path-output ./
+      - name: Build Download Notebooks (sphinx-tojupyter)
+        shell: bash -l {0}
+        run: |
+          jb build lectures --path-output ./ --builder=custom --custom-builder=jupyter
+          zip -r download-notebooks.zip _build/jupyter
+      - uses: actions/upload-artifact@v2
+        with:
+          name: download-notebooks
+          path: download-notebooks.zip
+      - name: Copy Download Notebooks for GH-PAGES
+        shell: bash -l {0}
+        run: |
+          mkdir _build/html/_notebooks
+          cp _build/jupyter/*.ipynb _build/html/_notebooks
+      - name: Deploy website to gh-pages
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: _build/html/
+          cname: python-advanced.quantecon.org
+      - name: Upload "_build" folder (cache)
+        uses: actions/upload-artifact@v2
+        with:
+          name: build-cache
+          path: _build
+      - name: Prepare lecture-python-advanced.notebooks sync
+        shell: bash -l {0}
+        run: |
+          mkdir -p _build/lecture-python-advanced.notebooks
+          cp -a _notebook_repo/. _build/lecture-python-advanced.notebooks
+          cp _build/jupyter/*.ipynb _build/lecture-python-advanced.notebooks
+          ls -a _build/lecture-python-advanced.notebooks
+      - name: Commit latest notebooks to lecture-python-advanced.notebooks
+        uses: cpina/github-action-push-to-another-repository@master
+        env:
+          API_TOKEN_GITHUB: ${{ secrets.QUANTECON_SERVICES_PAT }}
+        with:
+          source-directory: '_build/lecture-python-advanced.notebooks/'
+          destination-repository-username: 'QuantEcon'
+          destination-repository-name: 'lecture-python-advanced.notebooks'
+          commit-message: 'auto publishing updates to notebooks'
+          destination-github-username: 'quantecon-services'
+          user-email: services@quantecon.org

README.md

@@ -1,2 +1,6 @@
 # lecture-python-advanced.myst
 Migration to Myst (Source files for https://python-advanced.quantecon.org)
+
+# Current Hosting
+
+https://5ff577b6c5ec3f9de6b87508--wonderful-lalande-528d1c.netlify.app/intro.html

environment.yml

@@ -1,15 +1,18 @@
-name: lecture-python-advanced.myst
+name: quantecon
 channels:
   - default
 dependencies:
-  - pip
   - python=3.8
-  - anaconda=2020.07
+  - anaconda=2020.11
+  - pip
   - pip:
-    - jupytext
     - jupyter-book
-    - quantecon
-    - joblib
-    - git+https://github.com/QuantEcon/quantecon-book-theme
-    - git+https://github.com/executablebooks/sphinx-multitoc-numbering.git
-    - interpolation
+    - sphinx-multitoc-numbering
+    - quantecon-book-theme
+    - sphinx-tojupyter
+    - sphinxext-rediraffe
+    - sphinx-exercise
+    - jupytext
+    - ghp-import
+    - jupinx
+

lectures/_config.yml

@@ -12,4 +12,9 @@ sphinx:
   config:
     html_theme: quantecon_book_theme
     html_static_path: ['_static']
-    mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
+    mathjax_config:
+      TeX:
+        Macros:
+          "argmax" : "arg\\,max"
+          "argmin" : "arg\\,min"
+    mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js

lectures/_toc.yml

@@ -49,6 +49,7 @@
     - file: additive_functionals
     - file: lu_tricks
     - file: classical_filtering
+    - file: knowing_forecasts_of_others
 
 - part: Asset Pricing and Finance
   chapters:

lectures/additive_functionals.md

@@ -1265,9 +1265,11 @@ These probability density functions help us understand mechanics underlying the
 
 ### Multiplicative Martingale as Likelihood Ratio Process
 
-{doc}`This lecture <likelihood_ratio_process>` studies **likelihood processes** and **likelihood ratio processes**.
+[This lecture](https://python.quantecon.org/likelihood_ratio_process.html) studies **likelihood processes**
+and **likelihood ratio processes**.
 
 A **likelihood ratio process** is  a  multiplicative  martingale with mean unity.
 
-Likelihood ratio processes exhibit the peculiar property that naturally also appears in {doc}`this lecture <likelihood_ratio_process>`.
+Likelihood ratio processes exhibit the peculiar property that naturally also appears
+[here](https://python.quantecon.org/likelihood_ratio_process.html).
 

lectures/amss.md

@@ -24,21 +24,6 @@ kernelspec:
 :depth: 2
 ```
 
-**Software Requirement:**
-
-This lecture requires the use of some older software versions to run. If
-you would like to execute this lecture please download the following
-<a href=_static/downloads/amss_environment.yml download>amss_environment.yml</a>
-file. This specifies the software required and an environment can be
-created using [conda](https://docs.conda.io/en/latest/):
-
-Open a terminal:
-
-```{code-block} bash
-conda env create --file amss_environment.yml
-conda activate amss
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython

lectures/amss2.md

@@ -24,21 +24,6 @@ kernelspec:
 :depth: 2
 ```
 
-**Software Requirement:**
-
-This lecture requires the use of some older software versions to run. If
-you would like to execute this lecture please download the following
-<a href=_static/downloads/amss_environment.yml download>amss_environment.yml</a>
-file. This specifies the software required and an environment can be
-created using [conda](https://docs.conda.io/en/latest/):
-
-Open a terminal:
-
-```{code-block} bash
-conda env create --file amss_environment.yml
-conda activate amss
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython

lectures/amss3.md

@@ -24,21 +24,6 @@ kernelspec:
 :depth: 2
 ```
 
-**Software Requirement:**
-
-This lecture requires the use of some older software versions to run. If
-you would like to execute this lecture please download the following
-<a href=_static/downloads/amss_environment.yml download>amss_environment.yml</a>
-file. This specifies the software required and an environment can be
-created using [conda](https://docs.conda.io/en/latest/):
-
-Open a terminal:
-
-```{code-block} bash
-conda env create --file amss_environment.yml
-conda activate amss
-```
-
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython

lectures/arellano.md

@@ -79,8 +79,8 @@ import numpy as np
 import quantecon as qe
 import random
 
-from numba import jit, jitclass, int64, float64
-
+from numba import jit, int64, float64
+from numba.experimental import jitclass
 %matplotlib inline
 ```
 

lectures/black_litterman.md

@@ -197,8 +197,7 @@ sample = excess_return.rvs(T)
 w = np.linalg.solve(δ * Σ_est, μ_est)
 
 fig, ax = plt.subplots(figsize=(8, 5))
-ax.set_title('Mean-variance portfolio weights recommendation \
-              and the market portfolio')
+ax.set_title('Mean-variance portfolio weights recommendation and the market portfolio')
 ax.plot(np.arange(N)+1, w, 'o', c='k', label='$w$ (mean-variance)')
 ax.plot(np.arange(N)+1, w_m, 'o', c='r', label='$w_m$ (market portfolio)')
 ax.vlines(np.arange(N)+1, 0, w, lw=1)
@@ -219,7 +218,7 @@ Black and Litterman's responded to this situation in the following way:
 - They want to continue to allow the customer to express his or her
   risk tolerance by setting $\delta$.
 - Leaving $\Sigma$ at its maximum-likelihood value, they push
-  $\mu$ away from its maximum value in a way designed to make
+  $\mu$ away from its maximum-likelihood value in a way designed to make
   portfolio choices that are more plausible in terms of conforming to
   what most people actually do.
 
@@ -314,8 +313,7 @@ d_m = r_m / σ_m
 μ_m = (d_m * Σ_est @ w_m).reshape(N, 1)
 
 fig, ax = plt.subplots(figsize=(8, 5))
-ax.set_title(r'Difference between $\hat{\mu}$ (estimate) and \
-              $\mu_{BL}$ (market implied)')
+ax.set_title(r'Difference between $\hat{\mu}$ (estimate) and $\mu_{BL}$ (market implied)')
 ax.plot(np.arange(N)+1, μ_est, 'o', c='k', label='$\hat{\mu}$')
 ax.plot(np.arange(N)+1, μ_m, 'o', c='r', label='$\mu_{BL}$')
 ax.vlines(np.arange(N) + 1, μ_m, μ_est, lw=1)
@@ -418,8 +416,7 @@ def BL_plot(τ):
     ax[0].vlines(np.arange(N)+1, μ_m, μ_est, lw=1)
     ax[0].axhline(0, c='k', ls='--')
     ax[0].set(xlim=(0, N+1), xlabel='Assets',
-              title=r'Relationship between $\hat{\mu}$, \
-                      $\mu_{BL}$and$\tilde{\mu}$')
+              title=r'Relationship between $\hat{\mu}$, $\mu_{BL}$, and  $ \tilde{\mu}$')
     ax[0].xaxis.set_ticks(np.arange(1, N+1, 1))
     ax[0].legend(numpoints=1)
 

lectures/cattle_cycles.md

@@ -357,15 +357,15 @@ econ2.irf(ts_length=25, shock=shock_demand)
 econ3.irf(ts_length=25, shock=shock_demand)
 
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
-ax1.plot(econ1.c_irf, label='$\\rho=0.6$')
-ax1.plot(econ2.c_irf, label='$\\rho=1$')
-ax1.plot(econ3.c_irf, label='$\\rho=0$')
+ax1.plot(econ1.c_irf, label=r'$\rho=0.6$')
+ax1.plot(econ2.c_irf, label=r'$\rho=1$')
+ax1.plot(econ3.c_irf, label=r'$\rho=0$')
 ax1.set_title('Consumption response to demand shock')
 ax1.legend()
 
-ax2.plot(econ1.k_irf[:, 0], label='$\\rho=0.6$')
-ax2.plot(econ2.k_irf[:, 0], label='$\\rho=1$')
-ax2.plot(econ3.k_irf[:, 0], label='$\\rho=0$')
+ax2.plot(econ1.k_irf[:, 0], label=r'$\rho=0.6$')
+ax2.plot(econ2.k_irf[:, 0], label=r'$\rho=1$')
+ax2.plot(econ3.k_irf[:, 0], label=r'$\rho=0$')
 ax2.set_title('Breeding stock response to demand shock')
 ax2.legend()
 plt.show()
@@ -393,11 +393,11 @@ total3_irf = econ3.k_irf[:, 0] + g * econ3.k_irf[:, 1] + g * econ3.k_irf[:, 2]
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
 ax1.plot(econ1.k_irf[:, 0], label='Breeding Stock')
 ax1.plot(total1_irf, label='Total Stock')
-ax1.set_title('$\\rho=0.6$')
+ax1.set_title(r'$\rho=0.6$')
 
 ax2.plot(econ3.k_irf[:, 0], label='Breeding Stock')
 ax2.plot(total3_irf, label='Total Stock')
-ax2.set_title('$\\rho=0$')
+ax2.set_title(r'$\rho=0$')
 plt.show()
 ```
 

lectures/classical_filtering.md

@@ -172,7 +172,7 @@ or
 ```{math}
 :label: eq_55
 
-x_t = \sum^{t-1}_{j=0} L^{-1}_{t,t-j}\, \varepsilon_{t-j}\
+x_t = \sum^{t-1}_{j=0} L^{-1}_{t,t-j}\, \varepsilon_{t-j}
 ```
 
 where $L^{-1}_{i,j}$ denotes the $i,j$ element of $L^{-1}$.
@@ -223,7 +223,7 @@ $$
 
 ### Implementation
 
-Here's the code that computes solutions to LQ control and filtering problems using the methods described here and in :doc: lu_tricks.
+Here's the code that computes solutions to LQ control and filtering problems using the methods described here and in {doc}`lu_tricks <lu_tricks>`.
 
 ```{code-cell} python3
 :file: _static/lecture_specific/lu_tricks/control_and_filter.py
@@ -256,7 +256,7 @@ h = 0.0
 example = LQFilter(d, h, y_m, r=d)
 ```
 
-The Wold representation is computed by example.coefficients_of_c().
+The Wold representation is computed by `example.coeffs_of_c()`.
 
 Let's check that it "flips roots" as required
 

lectures/cons_news.md

@@ -123,7 +123,7 @@ y_{t+1} - y_t =  a_{t+1} - \beta  a_t \quad
 ```
 
 where $\{a_t\}$ is another i.i.d. normally distributed scalar
-process, with means of zero and now variances $\sigma_a^2$.
+process, with means of zero and now variances $\sigma_a^2 > \sigma_\epsilon^2$.
 
 The two i.i.d. shock variances are related by
 
@@ -175,7 +175,7 @@ Using calculations in the {doc}`quantecon lecture <classical_filtering>`, where
 $z \in C$ is a complex variable, the covariance generating
 function $g (z) =
 \sum_{j=-\infty}^\infty g_j z^j$
-of the $\{(y_t - y_{t-1})\}$ process equals
+of the $\{y_t - y_{t-1}\}$ process equals
 
 $$
 g(z) = \sigma_\epsilon^2  h(z) h(z^{-1}) = \beta^{-2} \sigma_\epsilon^2 > \sigma_\epsilon^2 ,
@@ -420,9 +420,11 @@ exactly the same histories of nonfinancial income.
 
 The consumer with information associated with representation {eq}`eqn_1`
 responds to each shock $\epsilon_{t+1}$ by leaving his consumption
-unaltered and **saving** all of $a_{t+1}$ in anticipation of the
-permanently increased taxes that he will bear to pay for the addition
-$a_{t+1}$ to his time $t+1$ nonfinancial income.
+unaltered and **saving** all of $\epsilon_{t+1}$ in anticipation of the
+permanently increased taxes that he will bear in order to service the  permanent interest payments on the risk-free
+bonds  that the government has
+presumably issued to pay for the one-time  addition
+$\epsilon_{t+1}$ to his time $t+1$ nonfinancial income.
 
 The consumer with information associated with representation {eq}`eqn_2`
 responds to a shock $a_{t+1}$ by increasing his consumption by