Merge remote-tracking branch 'origin/main' into add-sd

Author: Smit-create

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

lectures/about.md

@@ -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/business_cycle.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/cobweb.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/lln_clt.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/long_run_growth.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/lp_intro.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 $\sum_{j=1}^n a_{ij}x_j + s_i = 0$.
 

lectures/schelling.md

@@ -260,7 +260,7 @@ def plot_distribution(agents, cycle_num):
 And here's some pseudocode for the main loop, where we cycle through the
 agents until no one wishes to move.
 
-The psueudo code is
+The pseudocode is
 
 ```{code-block} none
 plot the distribution

lectures/sd_foundations.md

@@ -29,7 +29,7 @@ We shall eventually describe two classic welfare theorems:
 
 * **first welfare theorem:** for a  given a distribution of wealth among consumers,  a competitive  equilibrium  allocation of goods solves a  social planning problem.
 
-* **second welfare theorem:** An allocation of goods to consumers that solves a social planning problem can be supported by a compeitive equilibrium with an appropriate initial distribution of  wealth.   
+* **second welfare theorem:** An allocation of goods to consumers that solves a social planning problem can be supported by a competitive equilibrium with an appropriate initial distribution of  wealth.   
 
 Key infrastructure concepts that we'll encounter in this lecture are
 
@@ -126,7 +126,7 @@ supply to demand brings us a **key finding:**
 
 It also brings  a useful  **competitive equilibrium computation strategy:** 
 
-* after solving the welfare problem for an optimal  quantity, we can read a competive equilibrium price from  either supply price or demand price at the  competitive equilibrium quantity
+* after solving the welfare problem for an optimal  quantity, we can read a competitive equilibrium price from  either supply price or demand price at the  competitive equilibrium quantity
 
 Soon we'll derive generalizations of the above demand and supply 
 curves from other objects.

lectures/simple_linear_regression.md

@@ -44,7 +44,7 @@ Let us consider a simple dataset of 10 observations for variables $x_i$ and $y_i
 |9| 1800 | 27 |
 |10 | 250 | 2 |
 
-Let us think about $y_i$ as sales for an ice-cream cart, while $x_i$ is a variable that records the day's temperature in Celcius.
+Let us think about $y_i$ as sales for an ice-cream cart, while $x_i$ is a variable that records the day's temperature in Celsius.
 
 ```{code-cell} ipython3
 x = [32, 21, 24, 35, 10, 11, 22, 21, 27, 2]
@@ -54,7 +54,7 @@ df.columns = ['X', 'Y']
 df
 ```
 
-We can use a scatter plot of the data to see the relationship between $y_i$ (ice-cream sales in dollars (\$\'s)) and $x_i$ (degrees celcius).
+We can use a scatter plot of the data to see the relationship between $y_i$ (ice-cream sales in dollars (\$\'s)) and $x_i$ (degrees Celsius).
 
 ```{code-cell} ipython3
 ax = df.plot(
@@ -407,9 +407,9 @@ df.dropna(inplace=True)
 df
 ```
 
-We have now droped the number of rows in our DataFrame from 62156 to 12445 removing a lot of empty data relationships.
+We have now dropped the number of rows in our DataFrame from 62156 to 12445 removing a lot of empty data relationships.
 
-Now we have a dataset containing life expectency and GDP per capita for a range of years.
+Now we have a dataset containing life expectancy and GDP per capita for a range of years.
 
 It is always a good idea to spend a bit of time understanding what data you actually have. 
 
@@ -458,8 +458,8 @@ df.plot(x='gdppc', y='life_expectency', kind='scatter',  xlabel="GDP per capita"
 
 This data shows a couple of interesting relationships.
 
-1. there are a number of countries with similar GDP per capita levels but a wide range in Life Expectency
-2. there appears to be a positive relationship between GDP per capita and life expectency. Countries with higher GDP per capita tend to have higher life expectency outcomes
+1. there are a number of countries with similar GDP per capita levels but a wide range in Life Expectancy
+2. there appears to be a positive relationship between GDP per capita and life expectancy. Countries with higher GDP per capita tend to have higher life expectency outcomes
 
 Even though OLS is solving linear equations -- one option we have is to transform the variables, such as through a log transform, and then use OLS to estimate the transformed variables
 
@@ -470,7 +470,7 @@ ln -> ln == elasticities
 By specifying `logx` you can plot the GDP per Capita data on a log scale
 
 ```{code-cell} ipython3
-df.plot(x='gdppc', y='life_expectency', kind='scatter',  xlabel="GDP per capita", ylabel="Life Expectency (Years)", logx=True);
+df.plot(x='gdppc', y='life_expectency', kind='scatter',  xlabel="GDP per capita", ylabel="Life Expectancy (Years)", logx=True);
 ```
 
 As you can see from this transformation -- a linear model fits the shape of the data more closely.
@@ -488,7 +488,7 @@ df
 ```{code-cell} ipython3
 data = df[['log_gdppc', 'life_expectency']].copy()  # Get Data from DataFrame
 
-# Calcuate the sample means
+# Calculate the sample means
 x_bar = data['log_gdppc'].mean()
 y_bar = data['life_expectency'].mean()
 ```