Merge remote-tracking branch 'origin/main' into lake-model

Author: Smit-create

.github/workflows/ci.yml

@@ -37,13 +37,13 @@ jobs:
       - name: Display Pip Versions
         shell: bash -l {0}
         run: pip list
-      # - name: Download "build" folder (cache)
-      #   uses: dawidd6/action-download-artifact@v2
-      #   with:
-      #     workflow: cache.yml
-      #     branch: main
-      #     name: build-cache
-      #     path: _build
+      - name: Download "build" folder (cache)
+        uses: dawidd6/action-download-artifact@v2
+        with:
+          workflow: cache.yml
+          branch: main
+          name: build-cache
+          path: _build
       # Build Assets (Download Notebooks and PDF via LaTeX)
       # - name: Build PDF from LaTeX
       #   shell: bash -l {0}
@@ -63,7 +63,7 @@ jobs:
       - name: Build HTML
         shell: bash -l {0}
         run: |
-          # rm -r _build/.doctrees
+          rm -r _build/.doctrees
           jb build lectures --path-output ./ -W --keep-going
       - name: Upload Execution Reports (HTML)
         uses: actions/upload-artifact@v2

environment.yml

@@ -6,7 +6,7 @@ dependencies:
   - anaconda=2022.10
   - pip
   - pip:
-    - jupyter-book==0.14.0
+    - jupyter-book==0.15.1
     # - quantecon-book-theme==0.3.2
     # - sphinx-tojupyter==0.2.1
     - sphinxext-rediraffe==0.2.7

in-work/supply_demand_foundations_v2.md

@@ -17,6 +17,7 @@ parts:
   - file: linear_equations
   - file: lp_intro
   - file: lln_clt
+  - file: monte_carlo
   - file: markov_chains
 - caption: Estimation
   numbered: true

lectures/_toc.yml

@@ -53,7 +53,7 @@ not essential.
 ## Credits
 
 In building this lecture series, we had invaluable assistance from research
-assistants at QuantEcon, as well as our QuantEcon colleages.  Without their
+assistants at QuantEcon, as well as our QuantEcon colleagues.  Without their
 help this series would not have been possible.
 
 In particular, we sincerely thank and give credit to

lectures/about.md

@@ -15,11 +15,11 @@ kernelspec:
 
 ## Overview
 
-This lecture is about illustrateing business cycles in different countries and period.
+This lecture is about illustrating business cycles in different countries and period.
 
 The business cycle refers to the fluctuations in economic activity over time. These fluctuations can be observed in the form of expansions, contractions, recessions, and recoveries in the economy.
 
-In this lecture, we will see expensions and contractions of economies from 1960s to the recent pandemic using [World Bank API](https://documents.worldbank.org/en/publication/documents-reports/api).
+In this lecture, we will see expansions and contractions of economies from 1960s to the recent pandemic using [World Bank API](https://documents.worldbank.org/en/publication/documents-reports/api).
 
 In addition to what's in Anaconda, this lecture will need the following libraries to get World bank data
 
@@ -49,7 +49,7 @@ So let's explore how to query data  together.
 
 We can use `wb.series.info` with parameter `q` to query available data from the World Bank (`imfpy. searches.database_codes()` in `imfpy`)
 
-For example, GDP growth is a key indicator to show the expension and contraction of level of economic activities.
+For example, GDP growth is a key indicator to show the expansion and contraction of level of economic activities.
 
 Let's retrive GDP growth data together
 
@@ -97,7 +97,7 @@ wb.series.info(q='consumption')
 wb.series.info(q='capital account') # TODO: Check if it is to be plotted
 ```
 
-- international trade volumn
+- international trade volume
 
 +++
 
@@ -342,7 +342,7 @@ def plot_trade(data, title, ylabel, title_pos, ax, g_params, b_params, t_params)
 
 
 fig, ax = plt.subplots()
-title = 'United States (International Trade Volumn)'
+title = 'United States (International Trade Volume)'
 ylabel = 'US Dollars, Millions'
 plot_UStrade = plot_trade(trade_us[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
@@ -352,7 +352,7 @@ fig, ax = plt.subplots()
 trade_cn = dots('CN','W00', 1960, 2020, freq='A')
 
 trade_cn['Period'] = trade_cn['Period'].astype('int')
-title = 'China (International Trade Volumn)'
+title = 'China (International Trade Volume)'
 ylabel = 'US Dollars, Millions'
 plot_trade_cn = plot_trade(trade_cn[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
@@ -362,7 +362,7 @@ fig, ax = plt.subplots()
 trade_mx = dots('MX','W00', 1960, 2020, freq='A')
 
 trade_mx['Period'] = trade_mx['Period'].astype('int')
-title = 'Mexico (International Trade Volumn)'
+title = 'Mexico (International Trade Volume)'
 ylabel = 'US Dollars, Millions'
 plot_trade_mx = plot_trade(trade_mx[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
@@ -372,7 +372,7 @@ fig, ax = plt.subplots()
 trade_ar = dots('AR','W00', 1960, 2020, freq='A')
 
 trade_ar['Period'] = trade_ar['Period'].astype('int')
-title = 'Argentina (International Trade Volumn)'
+title = 'Argentina (International Trade Volume)'
 ylabel = 'US Dollars, Millions'
 plot_trade_ar = plot_trade(trade_ar[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```

lectures/business_cycle.md

@@ -49,7 +49,7 @@ You can imagine how these dynamics could cause cycles in prices and quantities
 that persist over time.
 
 The cobweb model puts these ideas into equations so we can try to quantify
-them, and to study conditions underw which cycles persist (or disappear).
+them, and to study conditions under which cycles persist (or disappear).
 
 In this lecture, we investigate and simulate the basic model under different
 assumptions regarding the way that produces form expectations.

lectures/cobweb.md

@@ -358,7 +358,7 @@ This means that the distribution of $\bar X_n$ does not eventually concentrate o
 
 Hence the LLN does not hold.
 
-The LLN fails to hold here because the assumpton $\mathbb E|X| = \infty$ is violated by the Cauchy distribution.
+The LLN fails to hold here because the assumption $\mathbb E|X| = \infty$ is violated by the Cauchy distribution.
 
 +++
 
@@ -650,7 +650,7 @@ $$
 $$ 
 
 Finally, since both $X_t$ and $\epsilon_0$ are normally distributed and
-independent from each other, any linear combinary of these two variables is
+independent from each other, any linear combination of these two variables is
 also normally distributed.
 
 We have now shown that

lectures/lln_clt.md

@@ -148,8 +148,8 @@ ax.set_ylabel("GDP per capita (current US$) ")
 
 ### Plot for lower middle income and low income
 
-Finally, we compare time-series graphs of GDP per capita between a lower middle income country and a low income country. Again, keeping Pakistan fixed in our set as a lower middle income country, we choose Democratic Republic of Congo as our second country from a low income group. Congo is chosen for no particular reason apart from its unstable political atmoshpere and a dwindling economy. 
-On comapring we see quite a bit of difference between these countries. With Pakistan's GDP per capita being almost four times as much. Further strengthning our assumption that countries from different income groups can be quite different.
+Finally, we compare time-series graphs of GDP per capita between a lower middle income country and a low income country. Again, keeping Pakistan fixed in our set as a lower middle income country, we choose Democratic Republic of Congo as our second country from a low income group. Congo is chosen for no particular reason apart from its unstable political atmosphere and a dwindling economy. 
+On comparing we see quite a bit of difference between these countries. With Pakistan's GDP per capita being almost four times as much. Further strengthening our assumption that countries from different income groups can be quite different.
 
 ```{code-cell} ipython3
 # Pakistan, Congo (Lower middle income, low income)

lectures/long_run_growth.md

@@ -412,7 +412,7 @@ By deploying the following steps, any linear programming problem can be transfor
 
 1. **Objective Function:** If a problem is originally a constrained **maximization** problem, we can construct a new objective function that  is the additive inverse of the original objective function. The transformed problem is then a **minimization** problem.
 
-2. **Decision Variables:** Given a variable $x_j$ satisfying $x_j \le 0$, we can introduce a new variable $x_j' = - x_j$ and subsitute it into original problem. Given a free variable $x_i$ with no restriction on its sign, we can introduce two new variables $x_j^+$ and $x_j^-$ satisfying $x_j^+, x_j^- \ge 0$ and replace $x_j$ by $x_j^+ - x_j^-$.
+2. **Decision Variables:** Given a variable $x_j$ satisfying $x_j \le 0$, we can introduce a new variable $x_j' = - x_j$ and substitute it into original problem. Given a free variable $x_i$ with no restriction on its sign, we can introduce two new variables $x_j^+$ and $x_j^-$ satisfying $x_j^+, x_j^- \ge 0$ and replace $x_j$ by $x_j^+ - x_j^-$.
 
 3. **Inequality constraints:** Given an inequality constraint $\sum_{j=1}^n a_{ij}x_j \le 0$, we can introduce a new variable $s_i$, called a **slack variable** that satisfies $s_i \ge 0$ and replace the original constraint by $\sum_{j=1}^n a_{ij}x_j + s_i = 0$.