Merge pull request #255 from QuantEcon/review_lectures

Review lectures

Author: jstac

lectures/business_cycle.md

@@ -20,11 +20,11 @@ In this lecture we review some empirical aspects of business cycles.
 
 Business cycles are fluctuations in economic activity over time.
 
-The include expansions (also called booms) and contractions (also called recessions).
+These include expansions (also called booms) and contractions (also called recessions).
 
 For our study, we will use economic indicators from the [World Bank](https://documents.worldbank.org/en/publication/documents-reports/api) and [FRED](https://fred.stlouisfed.org/).
 
-In addition to those installed by Anaconda, this lecture requires
+In addition to the packages already installed by Anaconda, this lecture requires
 
 ```{code-cell} ipython3
 :tags: [hide-output]
@@ -50,7 +50,7 @@ Here's some minor code to help with colors in our plots.
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-# Set Graphical Parameters
+# Set graphical parameters
 cycler = plt.cycler(linestyle=['-', '-.', '--', ':'], 
         color=['#377eb8', '#ff7f00', '#4daf4a', '#ff334f'])
 plt.rc('axes', prop_cycle=cycler)
@@ -59,12 +59,12 @@ plt.rc('axes', prop_cycle=cycler)
 
 ## Data acquisition
 
-We will use `wbgapi` and `pandas_datareader` to retrieve data.
+We will use the World Bank's data API `wbgapi` and `pandas_datareader` to retrieve data.
 
 We can use `wb.series.info` with the argument `q` to query available data from
 the [World Bank](https://www.worldbank.org/en/home).
 
-For example, let's retrieve the ID to query GDP growth data.
+For example, let's retrieve the GDP growth data ID to query GDP growth data.
 
 ```{code-cell} ipython3
 wb.series.info(q='GDP growth')
@@ -81,7 +81,7 @@ gdp_growth
 ```
 
 
-We can the metadata to learn more about the series (click to expand).
+We can look at the series' metadata to learn more about the series (click to expand).
 
 ```{code-cell} ipython3
 :tags: [hide-output]
@@ -155,7 +155,7 @@ def plot_series(data, country, ylabel,
 
     ax.plot(data.loc[country], label=country, **g_params)
     
-    # Highlight Recessions
+    # Highlight recessions
     ax.axvspan(1973, 1975, **b_params)
     ax.axvspan(1990, 1992, **b_params)
     ax.axvspan(2007, 2009, **b_params)
@@ -248,7 +248,7 @@ Now let's consider Japan, which experienced rapid growth in the 1960s and
 1970s, followed by slowed expansion in the past two decades.
 
 Major dips in the growth rate coincided with the Oil Crisis of the 1970s, the
-GFC and the Covid-19 pandemic.
+Global Financial Crisis (GFC) and the Covid-19 pandemic.
 
 ```{code-cell} ipython3
 ---
@@ -311,7 +311,7 @@ plt.show()
 Notice that Argentina has experienced far more volatile cycles than
 the economies examined above.
 
-At the same time, growth of Argentina did not fall during the two developed
+At the same time, Argentina's growth rate did not fall during the two developed
 economy recessions in the 1970s and 1990s.
 
 
@@ -413,12 +413,12 @@ The labor market recovered at an unprecedented rate after the shock in 2020-2021
 In our {ref}`previous discussion<gdp_growth>`, we found that developed economies have had
 relatively synchronized periods of recession. 
 
-At the same time, this synchronization does not appear in Argentina until the 2000s. 
+At the same time, this synchronization did not appear in Argentina until the 2000s. 
 
 Let's examine this trend further. 
 
 With slight modifications, we can use our previous function to draw a plot
-that includes many countries
+that includes multiple countries.
 
 ```{code-cell} ipython3
 ---
@@ -466,7 +466,7 @@ def plot_comparison(data, countries,
     for country in countries:
         ax.plot(data.loc[country], label=country, **g_params)
     
-    # Highlight Recessions
+    # Highlight recessions
     ax.axvspan(1973, 1975, **b_params)
     ax.axvspan(1990, 1992, **b_params)
     ax.axvspan(2007, 2009, **b_params)
@@ -513,7 +513,7 @@ gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
 
 ```
 
-We use the United Kingdom, United States, Germany, and Japan as examples of developed economies
+We use the United Kingdom, United States, Germany, and Japan as examples of developed economies.
 
 ```{code-cell} ipython3
 ---
@@ -534,7 +534,7 @@ plot_comparison(gdp_growth.loc[countries, 1962:],
 plt.show()
 ```
 
-We choose Brazil, China, Argentina, and Mexico as representative developing economies
+We choose Brazil, China, Argentina, and Mexico as representative developing economies.
 
 ```{code-cell} ipython3
 ---
@@ -561,14 +561,14 @@ business cycles are becoming more synchronized in 21st-century recessions.
 However, emerging and less developed economies often experience more volatile
 changes throughout the economic cycles. 
 
-Despite of the synchronization in GDP growth, the experience of individual countries during
+Despite the synchronization in GDP growth, the experience of individual countries during
 the recession often differs. 
 
-We use unemployment rate and the recovery of labor market conditions
+We use the unemployment rate and the recovery of labor market conditions
 as another example.
 
 Here we compare the unemployment rate of the United States, 
-United Kingdom, Japan, and France
+the United Kingdom, Japan, and France.
 
 ```{code-cell} ipython3
 ---
@@ -597,7 +597,7 @@ plt.show()
 We see that France, with its strong labor unions, typically experiences
 relatively slow labor market recoveries after negative shocks.
 
-We also notice that, Japan has a history of very low and stable unemployment rates.
+We also notice that Japan has a history of very low and stable unemployment rates.
 
 
 ## Leading indicators and correlated factors 
@@ -684,19 +684,18 @@ plt.show()
 
 We see that 
 
-* consumer sentiment often remains high during during expansion and
-drops before a recession.
+* consumer sentiment often remains high during expansions and
+drops before recessions.
 * there is a clear negative correlation between consumer sentiment and the CPI.
 
 When the price of consumer commodities rises, consumer confidence diminishes.
 
-This trend is more significant in the during [stagflation](https://en.wikipedia.org/wiki/Stagflation).
+This trend is more significant during [stagflation](https://en.wikipedia.org/wiki/Stagflation).
 
 
 
 ### Production
 
-
 Real industrial output is highly correlated with recessions in the economy. 
 
 However, it is not a leading indicator, as the peak of contraction in production 
@@ -751,7 +750,7 @@ activity and gloomy expectations for the future.
 One example is domestic credit to the private sector by banks in the UK.
 
 The following graph shows the domestic credit to the private sector as a
-percentage of GDP by banks from 1970 to 2022 in the UK
+percentage of GDP by banks from 1970 to 2022 in the UK.
 
 ```{code-cell} ipython3
 ---
@@ -777,7 +776,5 @@ ax = plot_series(private_credit, countries,
 plt.show()
 ```
 
-
-Note that the credit rises during economic expansion
+Note that the credit rises during economic expansions
 and stagnates or even contracts after recessions.
-