head = '<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n<?xml-stylesheet href="Blank_US_Map.css" type="text/css"?>'
svg = head + svg
svg = svg.replace('width="959"', 'width="1035"')
with open("images/stateRelativeIncome" + str(year) + ".svg", "wb") as file:
file.write(bytes(svg, 'UTF-8'))
file = open("images/stateRelativeIncome" + str(year) + ".svg", "a")
convert = 'convert -density 144 images/stateRelativeIncome' + str(
year) + '.svg images/stateRelativeIncome' + str(year) + '.png'
subprocess.call(convert, shell=True)
# In[ ]:
# 4.4 Creat gif with imagemagick
makegif = 'convert -loop 0 -delay 50x100 images/*.png usStateConvergence.gif'
subprocess.call(makegif, shell=True)
# In[ ]:
# 5. Clean up
# os.chdir(os.getcwd())
# for files in os.listdir('.'):
# if files.endswith('.css') or files.endswith('.svg'):
# os.remove(files)
# In[ ]:
# 6. Export notebook to .py
runProcs.exportNb('usConvergenceMap')
# Construct series for real incomes in 1840, 1880, and 1900
df_1840 = easterlin_data['Income per capita - 1840 - A [cur dollars]']/float(historic_cpi_data.loc[1840])
df_1880 = easterlin_data['Income per capita - 1880 [cur dollars]']/float(historic_cpi_data.loc[1890])
df_1900 = easterlin_data['Income per capita - 1900 [cur dollars]']/float(historic_cpi_data.loc[1900])
# Put into a DataFrame and concatenate with previous data beginning in 1929
df = pd.DataFrame({pd.to_datetime('1840'):df_1840,pd.to_datetime('1880'):df_1880,pd.to_datetime('1900'):df_1900}).transpose()
df = pd.concat([data_y,df]).sort_index()
# In[115]:
# Export data to csv
series = df.sort_index()
dropCols = [u'AK', u'HI', u'New England', u'Mideast', u'Great Lakes', u'Plains', u'Southeast', u'Southwest', u'Rocky Mountain', u'Far West']
for c in dropCols:
series = series.drop([c],axis=1)
series.to_csv('../csv/state_income_data.csv',na_rep='NaN')
# In[11]:
# Export notebook to .py
runProcs.exportNb('state_income_data')
with open("../frames/state_relative_income" + str(year.year) + ".svg",
"wb") as file:
file.write(bytes(svg, 'UTF-8'))
file = open("../frames/state_relative_income" + str(year.year) + ".svg",
"a")
convert = 'convert -density 144 ../frames/state_relative_income' + str(
year.year) + '.svg ../frames/state_relative_income' + str(
year.year) + '.png'
subprocess.call(convert, shell=True)
# In[ ]:
# 4.4 Creat gif with imagemagick
makegif = 'convert -loop 0 -delay 50x100 ../frames/*.png ../gif/us_state_convergence.gif'
subprocess.call(makegif, shell=True)
# In[ ]:
# 5. Clean up
# os.chdir(os.getcwd())
# for files in os.listdir('.'):
# if files.endswith('.css') or files.endswith('.svg'):
# os.remove(files)
# In[ ]:
# 6. Export notebook to .py
runProcs.exportNb('us_convergence_map')
# Drop countries with inf values
qtyTheoryData = qtyTheoryData.replace([np.inf, -np.inf], np.nan).dropna()
qtyTheoryDataL = qtyTheoryData.loc[indexL]
qtyTheoryDataM = qtyTheoryData.loc[indexM]
qtyTheoryDataH = qtyTheoryData.loc[indexH]
qtyTheoryDataOecd = qtyTheoryData.loc[indexOecd]
# 4.6 Export dataframes to csv
qtyTheoryData.to_csv('qtyTheoryOpenData.csv',
index=True,
index_label='country')
qtyTheoryDataL.to_csv('qtyTheoryOpenDataL.csv',
index=True,
index_label='country')
qtyTheoryDataM.to_csv('qtyTheoryOpenDataM.csv',
index=True,
index_label='country')
qtyTheoryDataH.to_csv('qtyTheoryOpenDataH.csv',
index=True,
index_label='country')
qtyTheoryDataOecd.to_csv('qtyTheoryOpenDataOecd.csv',
index=True,
index_label='country')
# In[ ]:
# 5. Export notebook to python script
runProcs.exportNb('quantityTheoryData')
alpha=0.25,
facecolor='green',
interpolate=True)
ax.fill_between(annual_data_frame.index,
actualInflation,
expectedInflation,
where=expectedInflation > actualInflation,
alpha=0.25,
facecolor='red',
interpolate=True)
ax.set_ylabel('%')
ax.xaxis.set_major_locator(years5)
ax.legend(['actual inflation (year ahead)', 'expected inflation (year ahead)'],
bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=3,
mode="expand",
borderaxespad=0.,
prop={
'weight': 'normal',
'size': '15'
})
plt.grid()
fig.autofmt_xdate()
plt.savefig('../img/fig_US_Inflation_Forecast_site.png', bbox_inches='tight')
# In[17]:
progName = 'realRateData'
runProcs.exportNb(progName)
growth = 100*((df.iloc[-1]/df.iloc[0])**(1/(len(df.index)-1))-1)
# Construct plot
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(1,1,1)
colors = ['red','blue','magenta','green']
plt.scatter(income60,growth,s=0.0001)
for i, txt in enumerate(df.columns):
ax.annotate(txt[-3:], (income60[i],growth[i]),fontsize=10,color = colors[np.mod(i,4)])
ax.grid()
ax.set_xlabel('GDP per capita in 1960\n (thousands of 2011 $ PPP)')
ax.set_ylabel('Real GDP per capita growth\nfrom 1970 to '+str(df.index[0].year)+ ' (%)')
xlim = ax.get_xlim()
ax.set_xlim([0,xlim[1]])
fig.tight_layout()
# Save image
plt.savefig('../png/fig_GDP_GDP_Growth_site.png',bbox_inches='tight')
# In[ ]:
# Export notebook to python script
runProcs.exportNb('cross_country_income_data')
# z.extractall()
# 1.3 Remove the zip file
os.remove('FRB_Z1.zip')
# In[16]:
# 2. Import the xml data and create a legend
# 2.2 parse
tree = etree.parse("Z1_data.xml")
root = tree.getroot()
# 2.2 create a legend in csv format
# legend= createLegend(root)
# In[17]:
# 3. Sample plot: US T-bill volume
tBills = getSeries('FL313161113.A')
tBills.plot(x_compat=True)
# In[18]:
# 4. export the notebook
runProcs.exportNb('z1data')
# In[9]:
df = pd.concat([dataY, df]).sort_index()
# In[17]:
df.loc['1880'].sort_values()
# In[10]:
# 3. Export data to csv
series = dataY.sort_index()
series = df.sort_index()
dropCols = [
u'AK', u'HI', u'New England', u'Mideast', u'Great Lakes', u'Plains',
u'Southeast', u'Southwest', u'Rocky Mountain', u'Far West'
]
for c in dropCols:
series = series.drop([c], axis=1)
series.to_csv('stateIncomeData.csv', na_rep='NaN')
# In[11]:
len(dataY.columns)
# In[12]:
# 4. Export notebook to .py
runProcs.exportNb('stateIncomeData')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{./png/fig_inflation_interest_differentials_open.png}\n')
nf.write('\\end{figure}')
nf.close()
# 9.5 Money growth and exchange rate depreciation
nf = open('../tex/figure_money_differential_depreciation_open.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig:money_differential_depreciation_open} \\textbf{Money growth and depreciation for '),nf.write(str(len(quantity_theory_data))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{./png/fig_money_differential_depreciation_open.png}\n')
nf.write('\\end{figure}')
nf.close()
# In[10]:
# 10. Correlations
print(quantity_theory_data[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(quantity_theory_data_H[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(quantity_theory_data_M[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(quantity_theory_data_L[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(quantity_theory_data_oecd[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
# In[11]:
# 11. Export notebook to python script
runProcs.exportNb('quantity_theory_figures')
r_pred_A = sigma * np.array(cons.data -
np.mean(cons.data)) - 100 * np.log(beta)
print(gc)
# In[14]:
r_pred_A
# In[15]:
# 6.3 Plot the predicted real interest rate
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(real_ex_ante_A, 'b-', lw=3)
ax.plot(real_ex_ante_A.index, r_pred_A, 'r--', lw=3)
ax.set_title('Annual ex ante real interest rate')
ax.set_xlabel('Date')
ax.set_ylabel('%')
ax.legend(['actual', 'predicted'], loc='upper right')
# interest_A.recessions()
plt.grid()
# In[16]:
np.corrcoef(cons.data, real_ex_ante_A)
# In[17]:
# 7. Export to notebook to .py
runProcs.exportNb('inflation_forecasts')
# 6.2 Predicted real interest rate: sigma = 1
sigma = 1
beta = .98
gc = np.mean(cons.data)
rPredA = sigma * np.array(cons.data - np.mean(cons.data)) - 100 * np.log(beta)
print(gc)
# In[14]:
# 6.3 Plot the predicted real interest rate
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot_date(interestA.datenumbers, realExAnteA, 'b-', lw=3)
ax.plot_date(interestA.datenumbers, rPredA, 'r--', lw=3)
ax.set_title('Annual ex ante real interest rate')
ax.set_xlabel('Date')
ax.set_ylabel('%')
ax.legend(['actual', 'predicted'], loc='upper right')
# interestA.recessions()
plt.grid()
# In[15]:
np.corrcoef(cons.data, realExAnteA)
# In[16]:
# 7. Export to notebook to .py
runProcs.exportNb('consumptionEuler')