defl_forecast_1q.data,
'deflator inflation - 6mo forecast':
defl_forecast_2q.data,
'deflator inflation - 1yr forecast':
defl_forecast_1y.data
})
# ## Actual data
# In[5]:
interest3mo = fp.series('TB3MS').as_frequency('Q')
interest6mo = fp.series('TB6MS').as_frequency('Q')
interest1yr = fp.series('GS1').as_frequency('Q')
interest3mo, interest6mo, interest1yr = fp.window_equalize(
[interest3mo, interest6mo, interest1yr])
interest_frame = pd.DataFrame({
'nominal interest - 3mo': interest3mo.data,
'nominal interest - 6mo': interest6mo.data,
'nominal interest - 1yr': interest1yr.data
})
# In[6]:
defl_3mo = fp.series('GDPDEF')
defl_6mo = fp.series('GDPDEF')
defl_1yr = fp.series('GDPDEF')
defl_3mo = defl_3mo.pc(method='forward', annualized=True)
# In[2]:
# Dowload data from FRED
u = series('LNS14000028')
p = series('CPIAUCSL')
# Construct the inflation series
p.pc(annualized=True)
p.ma2side(length=6)
p.data = p.ma2data
p.datenumbers = p.ma2datenumbers
p.dates = p.ma2dates
window_equalize([p,u])
p.bpfilter(low=24,high=84,K=84)
u.bpfilter(low=24,high=84,K=84)
# Set data for animation
x = u.bpcycle
y = p.bpcycle
d=u.bpdates
n=len(x)
# In[3]:
# Plot setup
font = {'weight' : 'bold',
'size' : 15}
# The purpose behind providing incomplete columns is to give students a starting point for completing the columns using Excel or a comparable tool.
#
# ## Download and manage data
# In[2]:
# Download monetary base and GDP deflator data
m_base = fp.series('BOGMBASE')
gdp_deflator = fp.series('A191RD3A086NBEA')
# Convert monetary base data to annual frequency
m_base = m_base.as_frequency('A')
# Equalize data ranges for monetary base and GDP deflator data
m_base, gdp_deflator = fp.window_equalize([m_base, gdp_deflator])
# GDP deflator base year
base_year = gdp_deflator.units.split(' ')[-1].split('=')[0]
# In[3]:
# Construct real monetary base
real_m_base = m_base.data/gdp_deflator.data*100/1000/1000
# Construct inflation data
inflation = (gdp_deflator.data/gdp_deflator.data.shift(1))-1
governmentA = series('GCEA')
exportsA = series('EXPGSA')
importsA = series('IMPGSA')
netExportsA = series('A019RC1A027NBEA')
deflatorA = series('A191RD3A086NBEA')
depreciationA = series('Y0000C1A027NBEA')
gdpA = series('GDPA')
tfpA = series('GDPA')
capitalA = series('GDPA')
laborA = series('B4701C0A222NBEA'
) # BEA index: fred('HOANBS') / .quartertoannual(method='AVG')
# annualSeries = [investmentA,consumptionA,governmentA,exportsA,importsA,netExportsA,deflatorA,depreciationA,gdpA,tfpA,capitalA,laborA]
investmentA, consumptionA, governmentA, netExportsA, exportsA, importsA, deflatorA, depreciationA, gdpA, tfpA, capitalA, laborA = window_equalize(
[
investmentA, consumptionA, governmentA, netExportsA, exportsA,
importsA, deflatorA, depreciationA, gdpA, tfpA, capitalA, laborA
])
# 2.2 Compute real annual data series
investmentA.data = 100 * investmentA.data / deflatorA.data
consumptionA.data = 100 * consumptionA.data / deflatorA.data
governmentA.data = 100 * governmentA.data / deflatorA.data
exportsA.data = 100 * exportsA.data / deflatorA.data
importsA.data = 100 * importsA.data / deflatorA.data
netExportsA.data = 100 * netExportsA.data / deflatorA.data
gdpA.data = 100 * gdpA.data / deflatorA.data
TA = len(investmentA.data)
# 2.3 Convert labor from millions of hours to billions
laborA.data = laborA.data / 1000
dates=dates,
frequency_short='Q')
gdp_deflator_forecast_A = fp.to_fred_series(
data=inflation_forecasts['INFPGDP1YR'].values,
dates=dates,
frequency_short='A')
gdp_deflator_forecast_A = gdp_deflator_forecast_A.as_frequency(freq='A',
method='mean')
# In[ ]:
# In[5]:
# 3.5 Create data frames with forecast inflation, actual inflation, and the 1-year bond rate
gdp_deflator_Q, gdp_deflator_forecast_Q, interest_Q = fp.window_equalize(
[gdp_deflator_Q, gdp_deflator_forecast_Q, interest_Q])
gdp_deflator_A, gdp_deflator_forecast_A, interest_A = fp.window_equalize(
[gdp_deflator_A, gdp_deflator_forecast_A, interest_A])
inflation_forecast_Q_df = pd.DataFrame({
'1-year inflation forecast':
gdp_deflator_forecast_Q.data,
'1-year actual inflation':
gdp_deflator_Q.data,
'1-year nominal interest rate':
interest_Q.data
})
inflation_forecast_A_df = pd.DataFrame({
'1-year inflation forecast':
gdp_deflator_forecast_A.data,
'1-year actual inflation':
gdp_deflator_A.data,
# In[2]:
# Dowload data
u = series('LNS14000028')
p = series('CPIAUCSL')
# Construct the inflation series
p.pc(annualized=True)
p.ma2side(length=6)
p.data = p.ma2data
p.datenumbers = p.ma2datenumbers
p.dates = p.ma2dates
# Make sure that the data inflation and unemployment series cver the same time interval
window_equalize([p, u])
# Filter the data
p.bpfilter(low=24, high=84, K=84)
p.hpfilter(lamb=129600)
u.bpfilter(low=24, high=84, K=84)
u.hpfilter(lamb=129600)
# ## Plots
# In[3]:
# BP-filtered data
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.plot_date(p.datenumbers, p.data, 'b-', lw=2)
dateNumbers = [dateutil.parser.parse(s) for s in dates]
# 3.4 Create the FRED objects
gdpDeflatorForecastQ.data = inflationForecasts['INFPGDP1YR'].values
gdpDeflatorForecastQ.dates = dates
gdpDeflatorForecastQ.datenumbers = dateNumbers
gdpDeflatorForecastA.data = inflationForecasts['INFPGDP1YR'].values.tolist()
gdpDeflatorForecastA.dates = dates
gdpDeflatorForecastA.datenumbers = dateNumbers
gdpDeflatorForecastA.quartertoannual(method='average')
# In[5]:
# 3.5 Create data frames with forecast inflation, actual inflation, and the 1-year bond rate
window_equalize([gdpDeflatorQ, gdpDeflatorForecastQ, interestQ])
window_equalize([gdpDeflatorA, gdpDeflatorForecastA, interestA])
inflationForecastQDf = pd.DataFrame(
{
'1-year inflation forecast': gdpDeflatorForecastQ.data,
'1-year actual inflation': gdpDeflatorQ.data,
'1-year nominal interest rate': interestQ.data
},
index=interestQ.dates)
inflationForecastADf = pd.DataFrame(
{
'1-year inflation forecast': gdpDeflatorForecastA.data,
'1-year actual inflation': gdpDeflatorA.data,
'1-year nominal interest rate': interestA.data
},
index=interestA.dates)
deflator_frame = deflator_frame.set_index(
pd.DatetimeIndex(defl_forecast_1q.datenumbers))
# ## Actual data
# In[8]:
interest3mo = series('TB3MS')
interest6mo = series('TB6MS')
interest1yr = series('GS1')
interest3mo.monthtoquarter()
interest6mo.monthtoquarter()
interest1yr.monthtoquarter()
window_equalize([interest3mo, interest6mo, interest1yr])
interest_frame = pd.DataFrame({
'nominal interest - 3mo': interest3mo.data,
'nominal interest - 6mo': interest6mo.data,
'nominal interest - 1yr': interest1yr.data
})
interest_frame = interest_frame.set_index(
pd.DatetimeIndex(interest3mo.datenumbers))
# In[9]:
defl_3mo = series('GDPDEF')
defl_6mo = series('GDPDEF')
defl_1yr = series('GDPDEF')
