3. Business Cycles#

# Set graphical parameters
cycler = plt.cycler(linestyle=['-', '-.', '--', ':'], 
        color=['#377eb8', '#ff7f00', '#4daf4a', '#ff334f'])
plt.rc('axes', prop_cycle=cycler)

def plot_series(data, country, ylabel, 
                txt_pos, ax, g_params,
                b_params, t_params, ylim=15, baseline=0):
    """
    Plots a time series with recessions highlighted. 

    Parameters
    ----------
    data : pd.DataFrame
        Data to plot
    country : str
        Name of the country to plot
    ylabel : str
        Label of the y-axis
    txt_pos : float
        Position of the recession labels
    y_lim : float
        Limit of the y-axis
    ax : matplotlib.axes._subplots.AxesSubplot
        Axes to plot on
    g_params : dict
        Parameters for the line
    b_params : dict
        Parameters for the recession highlights
    t_params : dict
        Parameters for the recession labels
    baseline : float, optional
        Dashed baseline on the plot, by default 0
    
    Returns
    -------
    ax : matplotlib.axes.Axes
        Axes with the plot.
    """

    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 ylim != None:
        ax.set_ylim([-ylim, ylim])
    else:
        ylim = ax.get_ylim()[1]
    ax.text(1974, ylim + ylim*txt_pos,
            'Oil Crisis\n(1974)', **t_params) 
    ax.text(1991, ylim + ylim*txt_pos,
            '1990s recession\n(1991)', **t_params) 
    ax.text(2008, ylim + ylim*txt_pos,
            'GFC\n(2008)', **t_params) 
    ax.text(2020, ylim + ylim*txt_pos,
            'Covid-19\n(2020)', **t_params)

    # Add a baseline for reference
    if baseline != None:
        ax.axhline(y=baseline, 
                   color='black', 
                   linestyle='--')
    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'}

start_date = datetime.datetime(1929, 1, 1)
end_date = datetime.datetime(1942, 6, 1)

unrate_history = web.DataReader('M0892AUSM156SNBR', 
                    'fred', start_date,end_date)
unrate_history.rename(columns={'M0892AUSM156SNBR': 'UNRATE'}, 
                inplace=True)

start_date = datetime.datetime(1948, 1, 1)
end_date = datetime.datetime(2022, 12, 31)

unrate = web.DataReader('UNRATE', 'fred', 
                    start_date, end_date)

# 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)

# 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)

fig, ax = plt.subplots()

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 (%)')

plt.show()

def plot_comparison(data, countries, 
                        ylabel, txt_pos, y_lim, ax, 
                        g_params, b_params, t_params, 
                        baseline=0):
    """
    Plot multiple series on the same graph

    Parameters
    ----------
    data : pd.DataFrame
        Data to plot
    countries : list
        List of countries to plot
    ylabel : str
        Label of the y-axis
    txt_pos : float
        Position of the recession labels
    y_lim : float
        Limit of the y-axis
    ax : matplotlib.axes._subplots.AxesSubplot
        Axes to plot on
    g_params : dict
        Parameters for the lines
    b_params : dict
        Parameters for the recession highlights
    t_params : dict
        Parameters for the recession labels
    baseline : float, optional
        Dashed baseline on the plot, by default 0
    
    Returns
    -------
    ax : matplotlib.axes.Axes
        Axes with the plot.
    """
    
    # 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*txt_pos, 
            'Oil Crisis\n(1974)', **t_params) 
    ax.text(1991, ylim + ylim*txt_pos, 
            '1990s recession\n(1991)', **t_params) 
    ax.text(2008, ylim + ylim*txt_pos, 
            'GFC\n(2008)', **t_params) 
    ax.text(2020, ylim + ylim*txt_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_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'}

# 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)

fig, ax = plt.subplots()
countries = ['United Kingdom', 'United States', 'Germany', 'Japan']
ylabel = 'GDP growth rate (%)'
plot_comparison(gdp_growth.loc[countries, 1962:], 
                countries, ylabel,
                0.1, 20, ax, 
                g_params, b_params, t_params)
plt.show()

fig, ax = plt.subplots()
countries = ['Brazil', 'China', 'Argentina', 'Mexico']
plot_comparison(gdp_growth.loc[countries, 1962:], 
                countries, ylabel, 
                0.1, 20, ax, 
                g_params, b_params, t_params)
plt.show()

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)

fig, ax = plt.subplots()

countries = ['United Kingdom', 'United States', 'Japan', 'France']
ylabel = 'unemployment rate (national estimate) (%)'
plot_comparison(unempl_rate, countries, 
                ylabel, 0.05, None, ax, g_params, 
                b_params, t_params, baseline=None)
plt.show()