From 76f7f9334840977185fc13715a78a5593ac6de2f Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Tue, 21 Feb 2023 21:07:08 +1100
Subject: [PATCH 1/8] update business cycle lecture

---
 in-work/ business_cycle.md | 164 +++++++++++++++++++++++++++++++++++++
 1 file changed, 164 insertions(+)
 create mode 100644 in-work/ business_cycle.md

diff --git a/in-work/ business_cycle.md b/in-work/ business_cycle.md
new file mode 100644
index 000000000..101129ae9
--- /dev/null
+++ b/in-work/ business_cycle.md	
@@ -0,0 +1,164 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.14.4
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+```{code-cell} ipython3
+!pip install wbgapi
+```
+
+# Business Cycle
+
+## Overview
+
+This lecture is about illustrateing business cycles in different countries and period.
+
+Business cycle is one of the widely studied field since the birth of economics as a subject from .
+
+In this lecture, we will see expensions and contractions of economies throughout the history with an emphasise on contemprary business cycles.
+
+We use the following imports.
+
+```{code-cell} ipython3
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import scipy.stats as st
+import wbgapi as wb
+```
+
+```{code-cell} ipython3
+wb.series.info(q='GDP growth')
+```
+
+```{code-cell} ipython3
+wb.series.info(q='unemployment')
+```
+
+```{code-cell} ipython3
+wb.series.info(q='credit')
+```
+
+## GDP Growth Rate and Unemployment
+
+First we look at the GDP growth rate and unemployment 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'], labels=True)
+gdp_growth = gdp_growth.set_index('Country')
+gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
+```
+
+```{code-cell} ipython3
+gdp_growth
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+plt.locator_params(axis='x', nbins=10)
+cycler = plt.cycler(linestyle=['-', '-.', '--'], color=['#377eb8', '#ff7f00', '#4daf4a'])
+plt.rc('axes', prop_cycle=cycler)
+ax.set_xticks([i for i in range(1960, 2021, 10)], minor=False)
+
+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)
+        # ax.plot(data.loc[country, 1990:1992], alpha=0.8)
+        # ax.plot(data.loc[country, 1973:1975], alpha=0.8)
+        # ax.plot(data.loc[country, 2007:2009], alpha=0.8)
+        # ax.plot(data.loc[country, 2019:2021], alpha=0.8)
+    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('GDP Growth Rate (%)')
+    ax.legend()
+    return ax
+
+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)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil', 'China', 'Argentina']
+title = 'Brazil, China, Argentina (GDP Growth Rate %)'
+ax = plot_comparison(gdp_growth, countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
+unempl_rate = unempl_rate.set_index('Country')
+unempl_rate.columns = unempl_rate.columns.str.replace('YR', '').astype(int)
+```
+
+```{code-cell} ipython3
+unempl_rate
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+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)
+```
+
+```{code-cell} ipython3
+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)
+```
+
+```{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)
+```
+
+```{code-cell} ipython3
+private_credit
+```
+
+```{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(unempl_rate, countries, title, '% of GDP', 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil', 'China', 'Argentina']
+title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks (% of GDP)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```

From a5375846f29a98f1cbbd521a887b6e75039d0961 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Tue, 21 Feb 2023 21:13:40 +1100
Subject: [PATCH 2/8] fix small bugs

---
 .github/workflows/ci.yml   | 2 +-
 in-work/ business_cycle.md | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index e85a2f484..1dc38c8b4 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,5 +1,5 @@
 name: Build HTML [using jupyter-book]
-on: [push]
+on: [pull_request]
 jobs:
   tests:
     runs-on: ubuntu-latest
diff --git a/in-work/ business_cycle.md b/in-work/ business_cycle.md
index 101129ae9..1657338b6 100644
--- a/in-work/ business_cycle.md	
+++ b/in-work/ business_cycle.md	
@@ -87,7 +87,7 @@ def plot_comparison(data, countries, title, ylabel, title_pos, ax, g_params, b_p
     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('GDP Growth Rate (%)')
+    ax.set_ylabel(ylabel)
     ax.legend()
     return ax
 
@@ -151,8 +151,8 @@ 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(unempl_rate, countries, title, '% of GDP', 0.05, ax, g_params, b_params, t_params)
+ylabel = '% of GDP'
+ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
 
 ```{code-cell} ipython3
@@ -160,5 +160,5 @@ fig, ax = plt.subplots()
 
 countries = ['Brazil', 'China', 'Argentina']
 title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks (% of GDP)'
-ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```

From 92aaa0127793e43fb2d4455b1eb1c35c7826e87d Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Tue, 21 Feb 2023 21:24:42 +1100
Subject: [PATCH 3/8] update cpi

---
 in-work/ business_cycle.md | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/in-work/ business_cycle.md b/in-work/ business_cycle.md
index 1657338b6..e5c138698 100644
--- a/in-work/ business_cycle.md	
+++ b/in-work/ business_cycle.md	
@@ -47,6 +47,10 @@ wb.series.info(q='unemployment')
 wb.series.info(q='credit')
 ```
 
+```{code-cell} ipython3
+wb.series.info(q='consumer')
+```
+
 ## GDP Growth Rate and Unemployment
 
 First we look at the GDP growth rate and unemployment rate.
@@ -162,3 +166,30 @@ 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)
 ```
+
+```{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)
+```
+
+```{code-cell} ipython3
+cpi
+```
+
+```{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(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil', 'China', 'Argentina']
+title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks (% of GDP)'
+ax = plot_comparison(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```

From 588c057b6be0f7962cc8998620ba16d57a3a3619 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Mon, 27 Feb 2023 10:20:13 +1100
Subject: [PATCH 4/8] add plots

---
 in-work/ business_cycle.md | 146 +++++++++++++++++++++++++++++++++----
 1 file changed, 133 insertions(+), 13 deletions(-)

diff --git a/in-work/ business_cycle.md b/in-work/ business_cycle.md
index e5c138698..a75d3e11f 100644
--- a/in-work/ business_cycle.md	
+++ b/in-work/ business_cycle.md	
@@ -51,6 +51,10 @@ wb.series.info(q='credit')
 wb.series.info(q='consumer')
 ```
 
+```{code-cell} ipython3
+wb.series.info(q='consumption')
+```
+
 ## GDP Growth Rate and Unemployment
 
 First we look at the GDP growth rate and unemployment rate.
@@ -58,7 +62,7 @@ First we look at the GDP growth rate and unemployment 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'], labels=True)
+gdp_growth = wb.data.DataFrame('NY.GDP.MKTP.KD.ZG',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR', 'MEX', 'CHL', 'COL', 'SLV', 'HTI'], labels=True)
 gdp_growth = gdp_growth.set_index('Country')
 gdp_growth.columns = gdp_growth.columns.str.replace('YR', '').astype(int)
 ```
@@ -68,19 +72,18 @@ gdp_growth
 ```
 
 ```{code-cell} ipython3
-fig, ax = plt.subplots()
-plt.locator_params(axis='x', nbins=10)
 cycler = plt.cycler(linestyle=['-', '-.', '--'], color=['#377eb8', '#ff7f00', '#4daf4a'])
 plt.rc('axes', prop_cycle=cycler)
+```
+
+```{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)
-        # ax.plot(data.loc[country, 1990:1992], alpha=0.8)
-        # ax.plot(data.loc[country, 1973:1975], alpha=0.8)
-        # ax.plot(data.loc[country, 2007:2009], alpha=0.8)
-        # ax.plot(data.loc[country, 2019:2021], alpha=0.8)
     ax.axvspan(1973, 1975, **b_params)
     ax.axvspan(1990, 1992, **b_params)
     ax.axvspan(2007, 2009, **b_params)
@@ -110,11 +113,88 @@ fig, ax = plt.subplots()
 
 countries = ['Brazil', 'China', 'Argentina']
 title = 'Brazil, China, Argentina (GDP Growth Rate %)'
-ax = plot_comparison(gdp_growth, countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+ax = plot_comparison(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{code-cell} ipython3
+def plot_comparison_NBER(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)
+    ax.axvspan(1969, 1970, **b_params)
+    ax.axvspan(1973, 1975, **b_params)
+    ax.axvspan(1980.25, 1980.75, **b_params)
+    ax.axvspan(1981, 1982, **b_params)
+    ax.axvspan(1990, 1991, **b_params)
+    ax.axvspan(2001.25, 2002, **b_params)
+    ax.axvspan(2007, 2009, **b_params)
+    ax.axvspan(2020, 2020, **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)
+    ax.legend()
+    return ax
+
+fig, ax = plt.subplots()
+
+countries = ['United States']
+title = 'United States (GDP Growth Rate %)'
+ax = plot_comparison_NBER(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
+```
+
+```{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)
+```
+
+```{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)
+```
+
+```{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)
+```
+
+```{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)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+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)
+```
+
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+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)
 ```
 
 ```{code-cell} ipython3
-unempl_rate = wb.data.DataFrame('SL.UEM.TOTL.NE.ZS',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
+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)
 ```
@@ -140,6 +220,38 @@ title = 'Brazil, China, Argentina (Unemployment Rate %)'
 ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
 ```
 
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+
+countries = ['Brazil']
+title = 'Brazil (Unemployment Rate %)'
+ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
+```
+
+```{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)
+```
+
+```{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)
+```
+
+```{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)
+```
+
 ```{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')
@@ -167,6 +279,14 @@ title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks
 ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
 
+```{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)
+```
+
 ```{code-cell} ipython3
 cpi = wb.data.DataFrame('FP.CPI.TOTL.ZG',['CHN', 'USA', 'DEU', 'BRA', 'ARG', 'GBR'], labels=True)
 cpi = cpi.set_index('Country')
@@ -181,15 +301,15 @@ cpi
 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'
+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)
 ```
 
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
-countries = ['Brazil', 'China', 'Argentina']
-title = 'Brazil, China, Argentina \n Domestic credit to private sector by banks (% of GDP)'
+countries = ['China', 'Argentina']
+title = 'China, Argentina \n Inflation, consumer prices (annual %)'
 ax = plot_comparison(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```

From d0e395d6c7caea2743dfc95b0e549736ba1be359 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Thu, 2 Mar 2023 23:46:22 +1100
Subject: [PATCH 5/8] add business cycle

---
 {in-work => lectures}/ business_cycle.md | 165 ++++++++++++++++-------
 lectures/_toc.yml                        |   1 +
 2 files changed, 115 insertions(+), 51 deletions(-)
 rename {in-work => lectures}/ business_cycle.md (73%)

diff --git a/in-work/ business_cycle.md b/lectures/ business_cycle.md
similarity index 73%
rename from in-work/ business_cycle.md
rename to lectures/ business_cycle.md
index a75d3e11f..f36b2c4b5 100644
--- a/in-work/ business_cycle.md	
+++ b/lectures/ business_cycle.md	
@@ -11,21 +11,26 @@ kernelspec:
   name: python3
 ---
 
-```{code-cell} ipython3
-!pip install wbgapi
-```
-
 # Business Cycle
 
 ## Overview
 
 This lecture is about illustrateing business cycles in different countries and period.
 
-Business cycle is one of the widely studied field since the birth of economics as a subject from .
+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 addition to what's in Anaconda, this lecture will need the following libraries to get World bank data
 
-In this lecture, we will see expensions and contractions of economies throughout the history with an emphasise on contemprary business cycles.
+```{code-cell} ipython3
+:tags: [hide-output]
+
+!pip install wbgapi
+!pip install imfpy
+```
 
-We use the following imports.
+We use the following imports
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
@@ -33,28 +38,71 @@ import pandas as pd
 import numpy as np
 import scipy.stats as st
 import wbgapi as wb
+from imfpy.retrievals import dots
 ```
 
+## 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 expension and contraction of level of economic activities.
+
+Let's retrive GDP growth data together
+
 ```{code-cell} ipython3
 wb.series.info(q='GDP growth')
 ```
 
+In a similar fashion, we can also retrieve data that covers different aspects of economic activities.
+
+We can cover indicators of 
+
+- labour market
+
 ```{code-cell} ipython3
+:tags: [hide-output]
+
 wb.series.info(q='unemployment')
 ```
 
+- credit level
+
 ```{code-cell} ipython3
+:tags: [hide-output]
+
 wb.series.info(q='credit')
 ```
 
+- consumer price index
+
 ```{code-cell} ipython3
+:tags: [hide-output]
+
 wb.series.info(q='consumer')
 ```
 
+- consumption level
+
 ```{code-cell} ipython3
+:tags: [hide-output]
+
 wb.series.info(q='consumption')
 ```
 
+```{code-cell} ipython3
+wb.series.info(q='capital account') # TODO: Check if it is to be plotted
+```
+
+- international trade volumn
+
++++
+
+These indicators will give us an overview of variations in economic activities across time.
+
 ## GDP Growth Rate and Unemployment
 
 First we look at the GDP growth rate and unemployment rate.
@@ -116,35 +164,6 @@ 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)
 ```
 
-```{code-cell} ipython3
-def plot_comparison_NBER(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)
-    ax.axvspan(1969, 1970, **b_params)
-    ax.axvspan(1973, 1975, **b_params)
-    ax.axvspan(1980.25, 1980.75, **b_params)
-    ax.axvspan(1981, 1982, **b_params)
-    ax.axvspan(1990, 1991, **b_params)
-    ax.axvspan(2001.25, 2002, **b_params)
-    ax.axvspan(2007, 2009, **b_params)
-    ax.axvspan(2020, 2020, **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)
-    ax.legend()
-    return ax
-
-fig, ax = plt.subplots()
-
-countries = ['United States']
-title = 'United States (GDP Growth Rate %)'
-ax = plot_comparison_NBER(gdp_growth.loc[countries, 1962:], countries, title, ylabel, 0.1, ax, g_params, b_params, t_params)
-```
-
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
@@ -199,10 +218,6 @@ unempl_rate = unempl_rate.set_index('Country')
 unempl_rate.columns = unempl_rate.columns.str.replace('YR', '').astype(int)
 ```
 
-```{code-cell} ipython3
-unempl_rate
-```
-
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
@@ -252,16 +267,14 @@ title = 'El Salvador (Unemployment Rate %)'
 ax = plot_comparison(unempl_rate, countries, title, ylabel, 0.04, ax, g_params, b_params, t_params)
 ```
 
+## Credit Level
+
 ```{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)
 ```
 
-```{code-cell} ipython3
-private_credit
-```
-
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
@@ -287,16 +300,14 @@ title = 'United Kingdom and China \n Domestic credit to private sector by banks
 ax = plot_comparison(private_credit, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
 
+## Inflation
+
 ```{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)
 ```
 
-```{code-cell} ipython3
-cpi
-```
-
 ```{code-cell} ipython3
 fig, ax = plt.subplots()
 
@@ -306,10 +317,62 @@ ylabel = 'annual %'
 ax = plot_comparison(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
 ```
 
+## International Trade
+
+```{code-cell} ipython3
+trade_us = dots('US','W00', 1960, 2020, freq='A')
+trade_us['Period'] = trade_us['Period'].astype('int')
+```
+
 ```{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
+
+
 fig, ax = plt.subplots()
+title = 'United States International Trade Volumn'
+ylabel = 'US Dollars, Millions'
+plot_UStrade = plot_trade(trade_us[['Period', 'Twoway Trade']], title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```
 
-countries = ['China', 'Argentina']
-title = 'China, Argentina \n Inflation, consumer prices (annual %)'
-ax = plot_comparison(cpi, countries, title, ylabel, 0.05, ax, g_params, b_params, t_params)
+```{code-cell} ipython3
+fig, ax = plt.subplots()
+trade_cn = dots('CN','W00', 1960, 2020, freq='A')
+
+trade_cn['Period'] = trade_cn['Period'].astype('int')
+title = 'United States (International Trade Volumn)'
+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)
+```
+
+```{code-cell} ipython3
+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)'
+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)
+```
+
+```{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 Volumn)'
+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)
 ```
diff --git a/lectures/_toc.yml b/lectures/_toc.yml
index 980833827..f826fbe0b 100644
--- a/lectures/_toc.yml
+++ b/lectures/_toc.yml
@@ -6,6 +6,7 @@ parts:
   chapters:
   - file: about
   - file: long_run_growth
+  - file: business_cycle
   - file: inequality
 - caption: Tools & Techniques
   numbered: true

From c5f45151b4163f5e92b0677c6091ab2b1c25d173 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Thu, 2 Mar 2023 23:47:32 +1100
Subject: [PATCH 6/8] move back to in work

---
 {lectures => in-work}/ business_cycle.md | 0
 lectures/_toc.yml                        | 1 -
 2 files changed, 1 deletion(-)
 rename {lectures => in-work}/ business_cycle.md (100%)

diff --git a/lectures/ business_cycle.md b/in-work/ business_cycle.md
similarity index 100%
rename from lectures/ business_cycle.md
rename to in-work/ business_cycle.md
diff --git a/lectures/_toc.yml b/lectures/_toc.yml
index f826fbe0b..980833827 100644
--- a/lectures/_toc.yml
+++ b/lectures/_toc.yml
@@ -6,7 +6,6 @@ parts:
   chapters:
   - file: about
   - file: long_run_growth
-  - file: business_cycle
   - file: inequality
 - caption: Tools & Techniques
   numbered: true

From 9ed4888d51a9020129de591c35e52604e4232dd1 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Thu, 2 Mar 2023 23:48:47 +1100
Subject: [PATCH 7/8] change to on push

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 1dc38c8b4..e85a2f484 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,5 +1,5 @@
 name: Build HTML [using jupyter-book]
-on: [pull_request]
+on: [push]
 jobs:
   tests:
     runs-on: ubuntu-latest

From 180b99f3725cc46ff59e154cf3ec16b7e2747ad3 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Thu, 2 Mar 2023 23:51:53 +1100
Subject: [PATCH 8/8] add a missing bracket

---
 in-work/ business_cycle.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/in-work/ business_cycle.md b/in-work/ business_cycle.md
index f36b2c4b5..70fa5cc1d 100644
--- a/in-work/ business_cycle.md	
+++ b/in-work/ business_cycle.md	
@@ -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 Volumn)'
 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 = 'United States (International Trade Volumn)'
+title = 'China (International Trade Volumn)'
 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)
 ```