diff --git a/lectures/business_cycle.md b/lectures/business_cycle.md
index a396ab41c..ae1fc0cb8 100644
--- a/lectures/business_cycle.md
+++ b/lectures/business_cycle.md
@@ -11,23 +11,25 @@ kernelspec:
   name: python3
 ---
 
-# Business Cycle
+# Business Cycles
 
 ## Overview
 
-This lecture is about illustrating business cycles in different countries and period.
+In this lecture we study business cycles, which 
+are fluctuations in economic activity over time.
 
-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.
+These fluctuations can be observed in the form of expansions (booms), contractions (recessions), and recoveries.
 
-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 this lecture, we will look into a series of economic indicators to visualize the expansions and contractions of economies from the 1960s to the recent pandemic using [World Bank](https://documents.worldbank.org/en/publication/documents-reports/api) and [FRED](https://fred.stlouisfed.org/) data.
 
-In addition to what's in Anaconda, this lecture will need the following libraries to get World bank data
+In addition to those installed by Anaconda, this lecture requires
+libraries to obtain World Bank and FRED data:
 
 ```{code-cell} ipython3
 :tags: [hide-output]
 
 !pip install wbgapi
-!pip install imfpy
+!pip install pandas-datareader
 ```
 
 We use the following imports
@@ -37,342 +39,547 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np
 import scipy.stats as st
+import datetime
 import wbgapi as wb
-from imfpy.retrievals import dots
+import pandas_datareader.data as web
 ```
 
-## Data Acquaisition
-
-We will use `wbgapi` and `imfpy` to retrieve data from the World Bank and IMF API throughout this lecture.
-
-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 expansion and contraction of level of economic activities.
-
-Let's retrive GDP growth data together
-
 ```{code-cell} ipython3
-wb.series.info(q='GDP growth')
+:tags: [hide-input]
+# Set Graphical Parameters
+cycler = plt.cycler(linestyle=['-', '-.', '--', ':'], color=['#377eb8', '#ff7f00', '#4daf4a', '#ff334f'])
+plt.rc('axes', prop_cycle=cycler)
 ```
 
-In a similar fashion, we can also retrieve data that covers different aspects of economic activities.
+## Data Acquisition
 
-We can cover indicators of 
+We will use `wbgapi`, and `pandas_datareader` to retrieve data throughout this
+lecture.
 
-- labour market
+So let's explore how to query data first.
 
-```{code-cell} ipython3
-:tags: [hide-output]
+We can use `wb.series.info` with the argument `q` to query available data from
+the [World Bank](https://www.worldbank.org/en/home).
 
-wb.series.info(q='unemployment')
-```
-
-- credit level
+For example, let's retrieve the ID to query GDP growth data.
 
 ```{code-cell} ipython3
-:tags: [hide-output]
-
-wb.series.info(q='credit')
+wb.series.info(q='GDP growth')
 ```
 
-- consumer price index
+After retrieving the series ID, we use it to obtain the data.
 
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-wb.series.info(q='consumer')
+gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
+            ['USA', 'ARG', 'GBR', 'GRC', 'JPN'], labels=True)
+gdp_growth
 ```
 
-- consumption level
+We can learn more about the data by checking the metadata of the series
 
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-wb.series.info(q='consumption')
+wb.series.metadata.get('NY.GDP.MKTP.KD.ZG')
 ```
 
-```{code-cell} ipython3
-wb.series.info(q='capital account') # TODO: Check if it is to be plotted
-```
+Let's dive into the data with the tools we have.
 
-- international trade volume
 
-+++
 
-These indicators will give us an overview of variations in economic activities across time.
 
-## GDP Growth Rate and Unemployment
+## GDP Growth Rate
 
-First we look at the GDP growth rate and unemployment rate.
+First we look at the GDP growth rate 
 
 Let's source our data from the World Bank and clean the data
 
 ```{code-cell} ipython3
-gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'MEX', 'CHL', 'COL', 'SLV', 'HTI'], labels=True)
+# Use the series ID retrived before
+gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
+            ['USA', 'ARG', 'GBR', 'GRC', 'JPN'], labels=True)
 gdp_growth = gdp_growth.set_index('Country')
 gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
 ```
 
+Here's a first look at the data, which measures the GDP growth rate in
+percentages.
+
 ```{code-cell} ipython3
 gdp_growth
 ```
 
-```{code-cell} ipython3
-cycler = plt.cycler(linestyle=['-', '-.', '--'], color=['#377eb8', '#ff7f00', '#4daf4a'])
-plt.rc('axes', prop_cycle=cycler)
-```
+The cell below contains a function to generate plots for individual countries.
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
-ax.set_xticks([i for i in range(1960, 2021, 10)], minor=False)
-plt.locator_params(axis='x', nbins=10)
-
-def plot_comparison(data, countries, title, ylabel, title_pos, ax, g_params, b_params, t_params):
-    for country in countries:
-        ax.plot(data.loc[country], label=country, **g_params)
+:tags: [hide-input]
+def plot_comparison(data, country, title, 
+                    ylabel, title_pos, ax, g_params,
+                     b_params, t_params, ylim=15, baseline=0):
+    
+    ax.plot(data.loc[country], label=country, **g_params)
+    
+    # Highlight Recessions
     ax.axvspan(1973, 1975, **b_params)
     ax.axvspan(1990, 1992, **b_params)
     ax.axvspan(2007, 2009, **b_params)
     ax.axvspan(2019, 2021, **b_params)
-    ylim = ax.get_ylim()[1]
-    ax.text(1974, ylim + ylim * title_pos, 'Oil Crisis\n(1974)', **t_params) 
-    ax.text(1991, ylim + ylim * title_pos, '1990s recession\n(1991)', **t_params) 
-    ax.text(2008, ylim + ylim * title_pos, 'GFC\n(2008)', **t_params) 
-    ax.text(2020, ylim + ylim * title_pos, 'Covid-19\n(2020)', **t_params) 
+    if ylim != None:
+        ax.set_ylim([-ylim, ylim])
+    else:
+        ylim = ax.get_ylim()[1]
+    ax.text(1974, ylim + ylim * title_pos,
+            'Oil Crisis\n(1974)', **t_params) 
+    ax.text(1991, ylim + ylim * title_pos,
+            '1990s recession\n(1991)', **t_params) 
+    ax.text(2008, ylim + ylim * title_pos,
+            'GFC\n(2008)', **t_params) 
+    ax.text(2020, ylim + ylim * title_pos,
+            'Covid-19\n(2020)', **t_params)
+    if baseline != None:
+        ax.axhline(y=baseline, color='black', linestyle='--')
     ax.set_title(title, pad=40)
     ax.set_ylabel(ylabel)
     ax.legend()
     return ax
 
+# Define graphical parameters 
 g_params = {'alpha': 0.7}
 b_params = {'color':'grey', 'alpha': 0.2}
-t_params = {'color':'grey', 'fontsize': 9, 'va':'center', 'ha':'center'}
-countries = ['United Kingdom', 'United States', 'Germany']
-title = 'United Kingdom, United States, and Germany (GDP Growth Rate %)'
-ylabel = 'GDP Growth Rate (%)'
-title_height = 0.1
-ax = plot_comparison(gdp_growth, countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+t_params = {'color':'grey', 'fontsize': 9, 
+            'va':'center', 'ha':'center'}
 ```
 
+Now we can show some time series.
+
+Let's start with the United States.
+
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['Brazil', 'China', 'Argentina']
-title = 'Brazil, China, Argentina (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+country = 'United States'
+title = 'United States (GDP Growth Rate %)'
+ylabel = 'GDP Growth Rate (%)'
+_ = plot_comparison(gdp_growth, country, 
+                    title, ylabel, 0.1, ax, 
+                    g_params, b_params, t_params)
 ```
 
+GDP growth is positive on average and trending slightly downward over time.
+
+We also see fluctuations over GDP growth over time, some of which are quite large.
+
+Let's look at a few more countries to get a basis for comparison.
+
++++
+
+The United Kingdom (UK) has a similar pattern to the US, with a slow decline
+in the growth rate and significant fluctuations.
+
+Notice the very large dip during the Covid-19 pandemic.
+
+
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['Argentina']
-title = 'Argentina (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+country = 'United Kingdom'
+title = ' United Kingdom (GDP Growth Rate %)'
+title_height = 0.1
+_ = plot_comparison(gdp_growth, country, title, 
+                    ylabel, 0.1, ax, g_params, 
+                    b_params, t_params)
 ```
 
+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.
+
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['Mexico']
-title = 'Mexico (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+country = 'Japan'
+title = 'Japan (GDP Growth Rate %)'
+_ = plot_comparison(gdp_growth, country, title, 
+                    ylabel, 0.1, ax, g_params, 
+                    b_params, t_params)
 ```
 
+Now let's study Greece.
+
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['Chile']
-title = 'Chile (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+country = 'Greece'
+title = ' Greece (GDP Growth Rate %)'
+_ = plot_comparison(gdp_growth, country, title, 
+                    ylabel, 0.1, ax, g_params, 
+                    b_params, t_params)
 ```
 
+
+Greece had a significant drop in GDP growth around 2010-2011, during the peak
+of the Greek debt crisis.
+
+Next let's consider Argentina.
+
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['Colombia']
-title = 'Colombia (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+country = 'Argentina'
+title = 'Argentina (GDP Growth Rate %)'
+_ = plot_comparison(gdp_growth, country, title, 
+                    ylabel, 0.1, ax, 
+                    g_params, b_params, t_params)
 ```
 
+
+The figure shows that Argentina has experienced more volatile cycles than
+the economies mentioned above.
+
+At the same time, growth of Argentina did not fall during the two developed
+economy recessions in the 1970s and 1990s. 
+
+
++++
+
+## Unemployment
+
+Another important measure of business cycles is the unemployment rate.
+
+During a recession, it is more likely that a larger proportion of the working
+population will be laid off.
+
+We demonstrate this using a long-run unemployment rate from FRED spanning from [1929-1942](https://fred.stlouisfed.org/series/M0892AUSM156SNBR) to [1948-2022](https://fred.stlouisfed.org/series/UNRATE) with the unemployment rate between 1942 and 1948 estimated by [The Census Bureau](https://www.census.gov/library/publications/1975/compendia/hist_stats_colonial-1970.html).
+
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+start_date = datetime.datetime(1929, 1, 1)
+end_date = datetime.datetime(1942, 6, 1)
 
-countries = ['El Salvador']
-title = 'El Salvador (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+unrate_history = web.DataReader('M0892AUSM156SNBR', 'fred', start_date,end_date)
+unrate_history.rename(columns={'M0892AUSM156SNBR': 'UNRATE'}, inplace=True)
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+start_date = datetime.datetime(1948, 1, 1)
+end_date = datetime.datetime(2022, 12, 31)
 
-countries = ['Haiti']
-title = 'Haiti (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+unrate = web.DataReader('UNRATE', 'fred', start_date, end_date)
 ```
 
 ```{code-cell} ipython3
-unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'MEX', 'CHL', 'COL', 'SLV', 'HTI'], labels=True)
-unempl_rate = unempl_rate.set_index('Country')
-unempl_rate.columns = unempl_rate.columns.str.replace('YR', '').astype(int)
+# We use the census bureau's estimate for the unemployment rate 
+# between 1942 and 1948
+years = [datetime.datetime(year, 6, 1) for year in range(1942,1948)]
+unrate_census = [4.7, 1.9, 1.2, 1.9, 3.9, 3.9]
+
+unrate_census = {'DATE': years, 'UNRATE': unrate_census}
+unrate_census = pd.DataFrame(unrate_census)
+unrate_census.set_index('DATE', inplace=True)
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+:tags: []
 
-countries = ['United Kingdom', 'United States', 'Germany']
-title = 'United Kingdom, United States, and Germany (Unemployment Rate %)'
-ylabel = 'Unemployment Rate (National Estimate) (%)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.03, ax, g_params, b_params, t_params)
+# Obtain the NBER-defined recession periods
+start_date = datetime.datetime(1929, 1, 1)
+end_date = datetime.datetime(2022, 12, 31)
+
+nber = web.DataReader('USREC', 'fred', start_date, end_date)
 ```
 
 ```{code-cell} ipython3
+:tags: []
+
 fig, ax = plt.subplots()
 
-countries = ['Brazil', 'China', 'Argentina']
-title = 'Brazil, China, Argentina (Unemployment Rate %)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+ax.plot(unrate_history, **g_params, color='#377eb8', linestyle='-', linewidth=2)
+ax.plot(unrate_census, **g_params, color='black', linestyle='--', label='Census Estimates', linewidth=2)
+ax.plot(unrate, **g_params, color='#377eb8', linestyle='-', linewidth=2)
+
+# Draw gray boxes according to NBER recession indicators
+ax.fill_between(nber.index, 0, 1, where=nber['USREC']==1, 
+                color='grey', edgecolor='none',
+                alpha=0.3, transform=ax.get_xaxis_transform(), 
+                label='NBER Recession Indicators')
+ax.set_ylim([0, ax.get_ylim()[1]])
+ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
+          ncol=3, fancybox=True, shadow=True)
+ax.set_ylabel('Unemployment Rate (%)')
+
+_ = ax.set_title('Long-run Unemployment Rate, 1929-2022\n with Recession Indicators (United States)', pad=30)
 ```
 
+In the plot, we can see that the expansions and contractions of the labor
+market have been highly correlated with recessions. 
+
+However, there is often a delay in the recovery of the labor market after
+recessions.
+
+This trend is clearly visible in the 1930s, as well as in recessions in the
+1980s. 
+
+It also shows us how unique labor market conditions have been during the
+post-pandemic recovery. 
+
+The labor market has recovered at an unprecedented rate, leading to the
+tightest point in the past decades after the shock in 2020-2021.
+
++++
+
+(synchronization)=
+## Synchronization
+
+In our previous discussion, we found that developed economies have had
+relatively synchronized period of recessions. 
+
+At the same time, this synchronization does 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
+
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
+def plot_comparison_multi(data, countries, title, 
+                        ylabel, title_pos, y_lim, ax, 
+                        g_params, b_params, t_params, baseline=0):
+
+    # Allow the function to go through more than one series
+    for country in countries:
+        ax.plot(data.loc[country], label=country, **g_params)
+    
+    # Highlight Recessions
+    ax.axvspan(1973, 1975, **b_params)
+    ax.axvspan(1990, 1992, **b_params)
+    ax.axvspan(2007, 2009, **b_params)
+    ax.axvspan(2019, 2021, **b_params)
+    if y_lim != None:
+        ax.set_ylim([-y_lim, y_lim])
+    ylim = ax.get_ylim()[1]
+    ax.text(1974, ylim + ylim * title_pos, 
+            'Oil Crisis\n(1974)', **t_params) 
+    ax.text(1991, ylim + ylim * title_pos, 
+            '1990s recession\n(1991)', **t_params) 
+    ax.text(2008, ylim + ylim * title_pos, 
+            'GFC\n(2008)', **t_params) 
+    ax.text(2020, ylim + ylim * title_pos, 
+            'Covid-19\n(2020)', **t_params) 
+    if baseline != None:
+        ax.hlines(y=baseline, xmin=ax.get_xlim()[0], 
+                  xmax=ax.get_xlim()[1], color='black', 
+                  linestyle='--')
+    ax.set_title(title, pad=40)
+    ax.set_ylabel(ylabel)
+    ax.legend()
+    return ax
 
-countries = ['Brazil']
-title = 'Brazil (Unemployment Rate %)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+# Define graphical parameters 
+g_params = {'alpha': 0.7}
+b_params = {'color':'grey', 'alpha': 0.2}
+t_params = {'color':'grey', 'fontsize': 9, 
+            'va':'center', 'ha':'center'}
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
-
-countries = ['Chile']
-title = 'Chile (Unemployment Rate %)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+# Obtain GDP growth rate for a list of countries
+gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',
+            ['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'JPN', 'MEX'], labels=True)
+gdp_growth = gdp_growth.set_index('Country')
+gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
 ```
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
-
-countries = ['Colombia']
-title = 'Colombia (Unemployment Rate %)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+countries = ['United Kingdom', 'United States', 'Germany', 'Japan']
+title = 'United Kingdom, United States, Germany, and Japan (GDP Growth Rate %)'
+ylabel = 'GDP Growth Rate (%)'
+title_height = 0.1
+_ = plot_comparison_multi(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, 20, ax, g_params, b_params, t_params)
 ```
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
-
-countries = ['El Salvador']
-title = 'El Salvador (Unemployment Rate %)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+countries = ['Brazil', 'China', 'Argentina', 'Mexico']
+title = 'Brazil, China, Argentina, and Mexico (GDP Growth Rate %)'
+_ = plot_comparison_multi(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, 20, ax, g_params, b_params, t_params)
 ```
 
-## Credit Level
+By comparing the trend of GDP growth rates between developed and developing
+economies, we find that business cycles are becoming more and more
+synchronized in 21st-century recessions.
+
+However, emerging and less developed economies often experience more volatile
+changes throughout the economic cycles. 
+
+Although we have seen synchronization in GDP growth as a general trend, we
+also need to acknowledge that the experience of individual countries during
+the recession is often very different. 
+
+Here we use the unemployment rate and the recovery of labor market condition
+as another example.
 
 ```{code-cell} ipython3
-private_credit = wb.data.DataFrame('FD.AST.PRVT.GD.ZS',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
-private_credit = private_credit.set_index('Country')
-private_credit.columns = private_credit.columns.str.replace('YR', '').astype(int)
+unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',
+    ['USA', 'FRA', 'GBR', 'JPN'], labels=True)
+unempl_rate = unempl_rate.set_index('Country')
+unempl_rate.columns = unempl_rate.columns.str.replace('YR', '').astype(int)
 ```
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['United Kingdom', 'United States', 'Germany']
-title = 'United Kingdom, United States, and Germany \n Domestic credit to private sector by banks (% of GDP)'
-ylabel = '% of GDP'
-ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+countries = ['United Kingdom', 'United States', 'Japan', 'France']
+title = 'United Kingdom, United States, Japan, and France (Unemployment Rate %)'
+ylabel = 'Unemployment Rate (National Estimate) (%)'
+_ = plot_comparison_multi(unempl_rate, countries, title, 
+                          ylabel, 0.05, None, ax, g_params, 
+                          b_params, t_params, baseline=None)
 ```
 
-```{code-cell} ipython3
-fig, ax = plt.subplots()
+France, with its strong labor unions, has a prolonged labor market recovery
+compared to the US and UK. 
 
-countries = ['Brazil', 'China', 'Argentina']
-title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks (% of GDP)'
-ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
-```
+However, Japan has a history of very low and stable unemployment rates due to
+a constellation of social, demographic, and cultural factors.
 
-```{code-cell} ipython3
-fig, ax = plt.subplots()
++++
 
-countries = ['United Kingdom', 'China']
-title = 'United Kingdom and China \n Domestic credit to private sector by banks (% of GDP)'
-ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
-```
+## Leading Indicators and Correlated Factors for Business Cycles
 
-## Inflation
+Understanding leading indicators and correlated factors helps policymakers to
+better understand and reflect on the causes and results of business cycles. 
 
-```{code-cell} ipython3
-cpi = wb.data.DataFrame('FP.CPI.TOTL.ZG',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
-cpi = cpi.set_index('Country')
-cpi.columns = cpi.columns.str.replace('YR', '').astype(int)
-```
+We will discuss potential leading indicators and correlated factors from three
+perspectives: consumption, production, and credit level.
+
+### Consumption
+
++++
+
+Consumption is dependent on how confident consumers are toward their
+income and the overall performance of the economy in the future. 
+
+One widely cited indicator for consumer confidence is the [Consumer Sentiment Index](https://fred.stlouisfed.org/series/UMCSENT) published by the University
+of Michigan.
+
+We find that consumer sentiment maintains a high level during the expansion
+period, but there are significant drops before the recession hits.
+
+There is also a clear negative correlation between consumer sentiment and [core consumer price index](https://fred.stlouisfed.org/series/CPILFESL).
+
+This trend is more significant in the period of [stagflation](https://en.wikipedia.org/wiki/Stagflation).
+
+When the price of consumer commodities rises, consumer confidence diminishes.
 
 ```{code-cell} ipython3
+start_date = datetime.datetime(1978, 1, 1)
+end_date = datetime.datetime(2022, 12, 31)
+
+# Limit the plot to a specific range
+start_date_graph = datetime.datetime(1977, 1, 1)
+end_date_graph = datetime.datetime(2023, 12, 31)
+
+nber = web.DataReader('USREC', 'fred', start_date, end_date)
+consumer_confidence = web.DataReader('UMCSENT', 'fred', start_date, end_date)
+
 fig, ax = plt.subplots()
+ax.plot(consumer_confidence, **g_params, 
+        color='#377eb8', linestyle='-', 
+        linewidth=2)
+ax.fill_between(nber.index, 0, 1, 
+            where=nber['USREC']==1, color='grey', edgecolor='none',
+            alpha=0.3, transform=ax.get_xaxis_transform(), 
+            label='NBER Recession Indicators')
+ax.set_ylim([0, ax.get_ylim()[1]])
+ax.set_ylabel('Consumer Sentiment Index')
 
-countries = ['United Kingdom', 'United States', 'Germany']
-title = 'United Kingdom, United States, and Germany \n Inflation, consumer prices (annual %)'
-ylabel = 'annual %'
-ax = plot_comparison(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
-```
+# Plot CPI on another y-axis
+ax_t = ax.twinx()
+inflation = web.DataReader('CPILFESL', 'fred', 
+                start_date, end_date).pct_change(12) * 100
 
-## International Trade
+# Add CPI on the legend without drawing the line again
+ax_t.plot(2020, 0, **g_params, linestyle='-', 
+          linewidth=2, label='Consumer Sentiment Index')
+ax_t.plot(inflation, **g_params, color='#ff7f00', linestyle='--', 
+          linewidth=2, label='CPI YoY Change (%)')
+ax_t.fill_between(nber.index, 0, 1, where=nber['USREC']==1, 
+                  color='grey', edgecolor='none',
+                  alpha=0.3, transform=ax.get_xaxis_transform(), 
+                  label='NBER Recession Indicators')
+ax_t.set_ylim([0, ax_t.get_ylim()[1]])
+ax_t.set_xlim([start_date_graph, end_date_graph])
+ax_t.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
+            ncol=3, fontsize=9)
+ax_t.set_ylabel('Consumer Price Index (% Change)',)
 
-```{code-cell} ipython3
-trade_us = dots('US','W00', 1960, 2020, freq='A')
-trade_us['Period'] = trade_us['Period'].astype('int')
+# Suppress the text output
+_ = ax.set_title('University of Michigan Consumer Sentiment Index,\n and \
+Year-over-year Consumer Price Index Change, 1978-2022 (United States)', pad=30)
 ```
 
+
+### Production
+
+Consumer confidence often influences the consumption pattern of consumers.
+
+This often manifests on the production side.
+
+We find that the real output of the industry is also highly correlated with
+recessions in the economy. 
+
+However, instead of being a leading factor, the peak of the contraction in the
+production delays compared to consumer confidence and inflation
+
 ```{code-cell} ipython3
-def plot_trade(data, title, ylabel, title_pos, ax, g_params, b_params, t_params):
-    ax.plot(data['Period'], data['Twoway Trade'], **g_params)
-    ax.axvspan(1973, 1975, **b_params)
-    ax.axvspan(1990, 1992, **b_params)
-    ax.axvspan(2007, 2009, **b_params)
-    ax.axvspan(2019, 2021, **b_params)
-    ylim = ax.get_ylim()[1]
-    ax.text(1974, ylim + ylim * title_pos, 'Oil Crisis\n(1974)', **t_params) 
-    ax.text(1991, ylim + ylim * title_pos, '1990s recession\n(1991)', **t_params) 
-    ax.text(2008, ylim + ylim * title_pos, 'GFC\n(2008)', **t_params) 
-    ax.text(2020, ylim + ylim * title_pos, 'Covid-19\n(2020)', **t_params) 
-    ax.set_title(title, pad=40)
-    ax.set_ylabel(ylabel)
-    return ax
+start_date = datetime.datetime(1919, 1, 1)
+end_date = datetime.datetime(2022, 12, 31)
 
+nber = web.DataReader('USREC', 'fred', start_date, end_date)
+consumer_confidence = web.DataReader('INDPRO', 'fred', start_date, end_date).pct_change(12) * 100
 
 fig, ax = plt.subplots()
-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)
+ax.plot(consumer_confidence, **g_params, color='#377eb8', linestyle='-', linewidth=2, label='Consumer Price Index')
+ax.fill_between(nber.index, 0, 1, where=nber['USREC']==1, color='grey', edgecolor='none',
+                alpha=0.3, transform=ax.get_xaxis_transform(), 
+                label='NBER Recession Indicators')
+ax.set_ylim([ax.get_ylim()[0], ax.get_ylim()[1]])
+ax.set_ylabel('YoY Real Ouput Change (%)')
+ax = ax.set_title('Year-over-year Industrial Production: \
+Total Index, 1919-2022 (United States)', pad=20)
 ```
 
-```{code-cell} ipython3
-fig, ax = plt.subplots()
-trade_cn = dots('CN','W00', 1960, 2020, freq='A')
+### Credit Level
 
-trade_cn['Period'] = trade_cn['Period'].astype('int')
-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)
-```
+Credit contractions often occur during recessions, as lenders become more
+cautious and borrowers become more hesitant to take on additional debt.
 
-```{code-cell} ipython3
-fig, ax = plt.subplots()
-trade_mx = dots('MX','W00', 1960, 2020, freq='A')
+This can be due to several factors such as a decrease in overall economic
+activity, rising unemployment, and gloomy expectations for the future.
 
-trade_mx['Period'] = trade_mx['Period'].astype('int')
-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)
+One example is domestic credit to the private sector by banks in the UK.
+
+```{code-cell} ipython3
+private_credit = wb.data.DataFrame('FS.AST.PRVT.GD.ZS',['GBR'], labels=True)
+private_credit = private_credit.set_index('Country')
+private_credit.columns = private_credit.columns.str.replace('YR', '').astype(int)
+private_credit
 ```
 
 ```{code-cell} ipython3
 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 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)
+countries = 'United Kingdom'
+title = 'Domestic Credit to Private Sector by Banks, United Kingdom (% of GDP)'
+ylabel = 'Credit Level (% of GDP)'
+ax = plot_comparison(private_credit, countries, title, 
+                     ylabel, 0.05, ax, g_params, b_params, 
+                     t_params, ylim=None, baseline=None)
 ```
+
+Note that the credit level expands rapidly in the period of economic expansion
+and stagnates or even contracts after recessions.