def startDriver(cmdopt):
if not cmdopt == "":
connectionParameters = {
"key": cmdopt,
"url": "https://apps.bea.gov/api/data/"
}
else:
connectionParameters = {}
dataBea = dpbea.data(connectionParameters)
return (dataBea)
def test_startDriver(cmdopt):
global dataBea
if not cmdopt == "":
connectionParameters = {
"key": cmdopt,
"url": "https://apps.bea.gov/api/data/"
}
else:
connectionParameters = {}
dataBea = dpbea.data(connectionParameters)
assert dataBea
return (dataBea)
10: 2, # 3 years of college
11: 3, # 4 years of college
12: 3
} # 5+ years of college
# Load the ASEC CPS data
CPS = pd.read_csv(os.path.join(CPSdata, 'CPSraw.csv'),
header=0,
usecols=columns,
dtype=types)
# Define a list of years
years = CPS.YEAR.unique().tolist()
# Load the NIPA personal income data from the BEA
bea = dpb.data(BEAkey)
personalincome = 1e6 * (bea.NIPA('T20100', frequency='A', year=years).iloc[2].values + bea.NIPA('T20100', frequency='A', year=years).iloc[8].values) \
/ (1e3 * bea.NIPA('T20100', frequency='A', year=years).iloc[39].values \
* bea.NIPA('T10104', frequency='A', year=years).iloc[0].values / 1e2)
# Recode the race variable
CPS.loc[:, 'RACE'] = CPS.RACE.map(racemap)
# Split the White and Black observations in each category
second = CPS.loc[CPS.RACE == 12, :].copy(deep=True)
second.loc[:, 'RACE'] = 2
second.loc[:, 'ASECWT'] = second.ASECWT / 2
CPS.loc[CPS.RACE == 12, 'RACE'] = 1
CPS.loc[CPS.RACE == 12, 'ASECWT'] = CPS.ASECWT / 2
CPS = CPS.append(second, ignore_index=True)
colors = [colormap(i) for i in np.linspace(0.3, 0.5,len(ax.lines))]
for i,j in enumerate(ax.lines):
j.set_color(colors[i])
ax.legend(fontsize = 10,loc=2)
fig.text(0.04, 0.5, 'Time series of ' +plot_name, va='center', ha='center',rotation='vertical',fontsize = 14)
plt.savefig("Time series of "+plot_name)
plt.show()
portfolio_plot(dfFactor[['Spread', 'RF']], 1, plot_name='Spread and RF' ,figsize=(8,4), cmap ='twilight')
# #### For monthly time-series of labor income growth (BEA)
# In[57]:
BEA_data = dpb.data('FDA2D756-CC0A-4AAA-A1D5-980FA23F31BB') #or data = dpb.data("API Key")
NIPA_cons=BEA_data.NIPA('T20600',frequency='M')
#Download annual consumption data on nondurable goods from Table 2.6.
#on “Personal Income and Its Disposition, Monthly”
NIPA_cons.reset_index(inplace=True)
Compensation_data=NIPA_cons[NIPA_cons['LineDescription']=='-Compensation of employees']
Compensation_data = Compensation_data.T.iloc[4:,:]
Compensation_data.columns=['Compensation']
Compensation_data.index = pd.to_datetime(Compensation_data.index.values, format='%YM%m')
Compensation_data['Income Growth'] = (Compensation_data['Compensation']-Compensation_data['Compensation'].shift(1))/Compensation_data['Compensation'].shift(1)
# Convert strings to datetime
Compensation_data = Compensation_data[(Compensation_data.index<=pd.to_datetime(edate)) & (Compensation_data.index>=pd.to_datetime(sdate))]
Compensation_data['Mkt-RF'] = dfFactor['Mkt-RF']/100
Compensation_data['Income Growth'] = Compensation_data['Income Growth']
labor_market = (Compensation_data[['Income Growth','Mkt-RF']]+1).astype('f').resample('Y').prod()-1
portfolio_plot(labor_market, 1, plot_name='Income Growth and Mkt-RF (monthly)' ,figsize=(8,4), cmap ='twilight')