Using the FRED API to get and display key macro series¶
FRED, a database maintained by the Federal Reserve Bank of St. Louis, is a realiable source of macroecononomic and financial data. The first step is to install the FRED API. https://fred.stlouisfed.org/docs/api/fred/
In [ ]:
pip install fredapi
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: fredapi in /usr/local/lib/python3.7/dist-packages (0.5.0) Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from fredapi) (1.3.5) Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->fredapi) (2.8.2) Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas->fredapi) (1.21.6) Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->fredapi) (2022.1) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->fredapi) (1.15.0)
In [ ]:
from fredapi import Fred
Next we will download the FRED API, ask the FED for an ID (go here https://research.stlouisfed.org/useraccount/apikeys), and enter it.
In [ ]:
############## please get your own API KEY instead of using mine ####################
fred = Fred(api_key='52b55ac11946f5749148befd9094e6bf') # this key won't work. get your own key please.
In [ ]:
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
%matplotlib inline
In [ ]:
gdp = fred.get_series('GDPC1', observation_start='1995-1-1') # Seasonally adjusted real GDP # seasonally ajusted non-fram employment in thousands
empl = fred.get_series('PAYEMS', observation_start='1995-1-1') # seasonally ajusted non-farm employment in thousands
unemp =fred.get_series('UNRATE', observation_start='1995-1-1') # seasonally ajusted unemployment rate in percent
SP500=fred.get_series('SP500', observation_start='1995-1-1') # only starts in 2010 at Fred for some reason
consumer_confidence=fred.get_series('UMCSENT', observation_start='1995-1-1')
dollar_euro=fred.get_series('DEXUSEU', observation_start='1995-1-1')
civilian_labor_force=fred.get_series('CLF16OV', observation_start='1995-1-1')
PPI = fred.get_series('PPIACO', observation_start='1995-1-1')
CO2_emission = fred.get_series('EMISSCO2TOTVTTTOUSA', observation_start='1995-1-1')
In [ ]:
fig, axs = plt.subplots(3, 3,figsize=(20, 15))
fig.suptitle('US Economy since January 1995')
# plt.subplot(221)
axs[0][0].plot(gdp.index,gdp.values)
axs[0][0].set_title("Real GDP, SA")
axs[0,0].set_ylabel("2012 $bn")
axs[0][1].plot(empl.index,empl.values)
axs[0][1].set_title("Non-farm employment, SA")
axs[0,1].set_ylabel("thousands")
axs[0][2].plot(unemp.index,unemp.values)
axs[0][2].set_title("Unemployment, SA")
axs[0,2].set_ylabel("%")
axs[1][0].plot(SP500.index,SP500.values)
axs[1][0].set_title("S&P500")
axs[1][1].plot(consumer_confidence.index,consumer_confidence.values)
axs[1][1].set_title("Consumer Sentiment, SA")
axs[1][2].plot(dollar_euro.index,dollar_euro.values)
axs[1][2].set_title("Dollar to Euro Spot Exchange Rate, NSA")
axs[2][0].plot(civilian_labor_force.index, civilian_labor_force.values)
axs[2][0].set_title("Civilian Labor Force, NSA")
axs[2][1].plot(PPI.index,PPI.values)
axs[2][1].set_title("PPI, SA")
axs[2][2].plot(CO2_emission.index,CO2_emission.values)
axs[2][2].set_title("CO2_emission, NSA")
plt.show()
In [ ]:
type(gdp)
Out[ ]:
pandas.core.series.Series
In [ ]:
consumer_confidence
Out[ ]:
1995-01-01 97.6
1995-02-01 95.1
1995-03-01 90.3
1995-04-01 92.5
1995-05-01 89.8
...
2021-12-01 70.6
2022-01-01 67.2
2022-02-01 62.8
2022-03-01 59.4
2022-04-01 65.2
Length: 328, dtype: float64
In [ ]:
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.tsa.api as tsa
import statsmodels.graphics.tsaplots as tsaplt
In [ ]:
dmbp = pd.read_csv("http://web.pdx.edu/~crkl/ceR/data/dmbp.txt",sep='\s+',
header=None,names=['xrate','friday'],nrows=1974)
dmbp
Out[ ]:
| xrate | friday | |
|---|---|---|
| 0 | 0.125333 | 0.0 |
| 1 | 0.028874 | 0.0 |
| 2 | 0.063462 | 0.0 |
| 3 | 0.226719 | 1.0 |
| 4 | -0.214267 | 0.0 |
| ... | ... | ... |
| 1969 | -0.408540 | 1.0 |
| 1970 | -0.030468 | 1.0 |
| 1971 | -0.117546 | 1.0 |
| 1972 | -0.231271 | 0.0 |
| 1973 | 0.528047 | 1.0 |
1974 rows × 2 columns
In [ ]:
In [ ]:
dollar_euro=fred.get_series('DEXUSEU', observation_start='1995-1-1')
type(dollar_euro) #One-dimensional ndarray with axis labels (including time series)
Out[ ]:
pandas.core.series.Series
In [ ]:
print(dollar_euro.shape)
print(dollar_euro.size)
print(dollar_euro.dtype)
print(dollar_euro.astype)
print(dollar_euro.attrs)
print(dollar_euro.max)
print(dollar_euro.empty)
dollar_euro.isnull().sum() #232 missing vlues
dollar_euro.dropna(inplace=True) #drop 232
dollar_euro.isnull().sum() #check
(6100,)
6100
float64
<bound method NDFrame.astype of 1999-01-04 1.1812
1999-01-05 1.1760
1999-01-06 1.1636
1999-01-07 1.1672
1999-01-08 1.1554
...
2022-05-16 1.0420
2022-05-17 1.0532
2022-05-18 1.0494
2022-05-19 1.0587
2022-05-20 1.0559
Length: 6100, dtype: float64>
{}
<bound method NDFrame._add_numeric_operations.<locals>.max of 1999-01-04 1.1812
1999-01-05 1.1760
1999-01-06 1.1636
1999-01-07 1.1672
1999-01-08 1.1554
...
2022-05-16 1.0420
2022-05-17 1.0532
2022-05-18 1.0494
2022-05-19 1.0587
2022-05-20 1.0559
Length: 6100, dtype: float64>
False
Out[ ]:
0
In [ ]:
In [ ]:
In [ ]:
dollar_euro2=dollar_euro.to_frame().reset_index() # convert index into a column and convert it to DataFrame
print(dollar_euro2)
print(type(dollar_euro2))
dollar_euro2.info()
dollar_euro2.rename(columns={'index': "date", 0:'xrate'}, inplace=True)
dollar_euro2.info()
dollar_euro2['weekday']=pd.to_datetime(dollar_euro2['date']).dt.weekday
print(dollar_euro2)
dollar_euro2.info()
print(dollar_euro2)
dollar_euro2['friday'] = np.where(dollar_euro2['weekday'] == 4, 1, 0)
dollar_euro2.info()
print(dollar_euro2)
index 0
0 1999-01-04 1.1812
1 1999-01-05 1.1760
2 1999-01-06 1.1636
3 1999-01-07 1.1672
4 1999-01-08 1.1554
... ... ...
5863 2022-05-16 1.0420
5864 2022-05-17 1.0532
5865 2022-05-18 1.0494
5866 2022-05-19 1.0587
5867 2022-05-20 1.0559
[5868 rows x 2 columns]
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5868 entries, 0 to 5867
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 index 5868 non-null datetime64[ns]
1 0 5868 non-null float64
dtypes: datetime64[ns](1), float64(1)
memory usage: 91.8 KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5868 entries, 0 to 5867
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 date 5868 non-null datetime64[ns]
1 xrate 5868 non-null float64
dtypes: datetime64[ns](1), float64(1)
memory usage: 91.8 KB
date xrate weekday
0 1999-01-04 1.1812 0
1 1999-01-05 1.1760 1
2 1999-01-06 1.1636 2
3 1999-01-07 1.1672 3
4 1999-01-08 1.1554 4
... ... ... ...
5863 2022-05-16 1.0420 0
5864 2022-05-17 1.0532 1
5865 2022-05-18 1.0494 2
5866 2022-05-19 1.0587 3
5867 2022-05-20 1.0559 4
[5868 rows x 3 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5868 entries, 0 to 5867
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 date 5868 non-null datetime64[ns]
1 xrate 5868 non-null float64
2 weekday 5868 non-null int64
dtypes: datetime64[ns](1), float64(1), int64(1)
memory usage: 137.7 KB
date xrate weekday
0 1999-01-04 1.1812 0
1 1999-01-05 1.1760 1
2 1999-01-06 1.1636 2
3 1999-01-07 1.1672 3
4 1999-01-08 1.1554 4
... ... ... ...
5863 2022-05-16 1.0420 0
5864 2022-05-17 1.0532 1
5865 2022-05-18 1.0494 2
5866 2022-05-19 1.0587 3
5867 2022-05-20 1.0559 4
[5868 rows x 3 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5868 entries, 0 to 5867
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 date 5868 non-null datetime64[ns]
1 xrate 5868 non-null float64
2 weekday 5868 non-null int64
3 friday 5868 non-null int64
dtypes: datetime64[ns](1), float64(1), int64(2)
memory usage: 183.5 KB
date xrate weekday friday
0 1999-01-04 1.1812 0 0
1 1999-01-05 1.1760 1 0
2 1999-01-06 1.1636 2 0
3 1999-01-07 1.1672 3 0
4 1999-01-08 1.1554 4 1
... ... ... ... ...
5863 2022-05-16 1.0420 0 0
5864 2022-05-17 1.0532 1 0
5865 2022-05-18 1.0494 2 0
5866 2022-05-19 1.0587 3 0
5867 2022-05-20 1.0559 4 1
[5868 rows x 4 columns]
In [ ]:
lm2 = sm.OLS.from_formula('xrate~friday',data=dmbp).fit()
print(lm2.summary())
lm1 = sm.OLS.from_formula('xrate~1',data=dmbp).fit()
print(lm1.summary())
OLS Regression Results
==============================================================================
Dep. Variable: xrate R-squared: 0.000
Model: OLS Adj. R-squared: -0.000
Method: Least Squares F-statistic: 0.4440
Date: Mon, 30 May 2022 Prob (F-statistic): 0.505
Time: 21:20:09 Log-Likelihood: -1310.9
No. Observations: 1974 AIC: 2626.
Df Residuals: 1972 BIC: 2637.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept -0.0126 0.012 -1.041 0.298 -0.036 0.011
friday -0.0167 0.025 -0.666 0.505 -0.066 0.033
==============================================================================
Omnibus: 190.536 Durbin-Watson: 1.979
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1101.300
Skew: -0.242 Prob(JB): 7.17e-240
Kurtosis: 6.627 Cond. No. 2.52
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
==============================================================================
Dep. Variable: xrate R-squared: 0.000
Model: OLS Adj. R-squared: 0.000
Method: Least Squares F-statistic: nan
Date: Mon, 30 May 2022 Prob (F-statistic): nan
Time: 21:20:09 Log-Likelihood: -1311.1
No. Observations: 1974 AIC: 2624.
Df Residuals: 1973 BIC: 2630.
Df Model: 0
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept -0.0164 0.011 -1.552 0.121 -0.037 0.004
==============================================================================
Omnibus: 191.764 Durbin-Watson: 1.981
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1102.882
Skew: -0.250 Prob(JB): 3.25e-240
Kurtosis: 6.628 Cond. No. 1.00
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
In [ ]:
lm2 = sm.OLS.from_formula('xrate~friday',data=dollar_euro2).fit()
print(lm2.summary())
lm1 = sm.OLS.from_formula('xrate~1',data=dollar_euro2).fit()
print(lm1.summary())
OLS Regression Results
==============================================================================
Dep. Variable: xrate R-squared: 0.000
Model: OLS Adj. R-squared: -0.000
Method: Least Squares F-statistic: 0.04123
Date: Mon, 30 May 2022 Prob (F-statistic): 0.839
Time: 21:24:35 Log-Likelihood: 2475.2
No. Observations: 5868 AIC: -4946.
Df Residuals: 5866 BIC: -4933.
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 1.1978 0.002 515.720 0.000 1.193 1.202
friday -0.0010 0.005 -0.203 0.839 -0.011 0.009
==============================================================================
Omnibus: 54.378 Durbin-Watson: 0.002
Prob(Omnibus): 0.000 Jarque-Bera (JB): 41.518
Skew: -0.116 Prob(JB): 9.65e-10
Kurtosis: 2.659 Cond. No. 2.60
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
==============================================================================
Dep. Variable: xrate R-squared: -0.000
Model: OLS Adj. R-squared: -0.000
Method: Least Squares F-statistic: nan
Date: Mon, 30 May 2022 Prob (F-statistic): nan
Time: 21:24:35 Log-Likelihood: 2475.2
No. Observations: 5868 AIC: -4948.
Df Residuals: 5867 BIC: -4942.
Df Model: 0
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 1.1976 0.002 578.041 0.000 1.194 1.202
==============================================================================
Omnibus: 54.365 Durbin-Watson: 0.002
Prob(Omnibus): 0.000 Jarque-Bera (JB): 41.511
Skew: -0.116 Prob(JB): 9.68e-10
Kurtosis: 2.659 Cond. No. 1.00
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
In [ ]:
e = lm1.resid
e2 = e**2
In [ ]:
# time series analysis: identification
tsaplt.plot_acf(e, zero=False, lags=30)
tsaplt.plot_pacf(e, zero=False, lags=30)
tsaplt.plot_acf(e2, zero=False, lags=30)
tsaplt.plot_pacf(e2, zero=False, lags=30)
/usr/local/lib/python3.7/dist-packages/statsmodels/graphics/tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.
FutureWarning,
/usr/local/lib/python3.7/dist-packages/statsmodels/graphics/tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.
FutureWarning,
Out[ ]:
In [ ]:
pip install arch
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: arch in /usr/local/lib/python3.7/dist-packages (5.2.0) Requirement already satisfied: property-cached>=1.6.4 in /usr/local/lib/python3.7/dist-packages (from arch) (1.6.4) Requirement already satisfied: statsmodels>=0.11 in /usr/local/lib/python3.7/dist-packages (from arch) (0.13.2) Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.7/dist-packages (from arch) (1.3.5) Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from arch) (1.21.6) Requirement already satisfied: scipy>=1.3 in /usr/local/lib/python3.7/dist-packages (from arch) (1.4.1) Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->arch) (2022.1) Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->arch) (2.8.2) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0->arch) (1.15.0) Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.11->arch) (0.5.2) Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.11->arch) (21.3) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->statsmodels>=0.11->arch) (3.0.9)
In [ ]:
# https://arch.readthedocs.io/en/latest/univariate/introduction.html
from arch import arch_model
# GARCH(1,1), a common model
# default model: p=1,q=1, normal distribution
garch1 = arch_model(dollar_euro2.xrate).fit()
print(garch1.summary())
garch1.plot()
garch1.arch_lm_test(lags=10,standardized=True)
Iteration: 1, Func. Count: 6, Neg. LLF: -4962.314514188438
Iteration: 2, Func. Count: 18, Neg. LLF: -4977.472467550329
Iteration: 3, Func. Count: 31, Neg. LLF: -4977.638241929397
Optimization terminated successfully. (Exit mode 0)
Current function value: -4977.638290276602
Iterations: 7
Function evaluations: 31
Gradient evaluations: 3
Constant Mean - GARCH Model Results
==============================================================================
Dep. Variable: xrate R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: 4977.64
Distribution: Normal AIC: -9947.28
Method: Maximum Likelihood BIC: -9920.57
No. Observations: 5868
Date: Mon, May 30 2022 Df Residuals: 5867
Time: 21:25:36 Df Model: 1
Mean Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
mu 1.1831 4.390e-03 269.481 0.000 [ 1.174, 1.192]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 5.0548e-04 1.364e-05 37.067 9.520e-301 [4.788e-04,5.322e-04]
alpha[1] 0.2000 5.057e-03 39.549 0.000 [ 0.190, 0.210]
beta[1] 0.7800 5.124e-03 152.214 0.000 [ 0.770, 0.790]
============================================================================
Covariance estimator: robust
/usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:310: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when estimating the model parameters. The scale of y is 0.02518. Parameter estimation work better when this value is between 1 and 1000. The recommended rescaling is 10 * y. This warning can be disabled by either rescaling y before initializing the model or by setting rescale=False. data_scale_warning.format(orig_scale, rescale), DataScaleWarning
Out[ ]:
ARCH-LM Test H0: Standardized residuals are homoskedastic. ARCH-LM Test H1: Standardized residuals are conditionally heteroskedastic. Statistic: 5334.8713 P-value: 0.0000 Distributed: chi2(10) WaldTestStatistic, id: 0x7fe3839320d0
In [ ]:
# Other modelspecifications are possible:
# GJR-GARCH(1,1) model, set o=1
garch2 = arch_model(dollar_euro2.xrate,p=1,o=1,q=1).fit()
print(garch2.summary())
garch2.plot()
# TARCH/ZARCH model, set power=1
garch3 = arch_model(dollar_euro2.xrate,p=1,o=1,q=1,power=1).fit()
print(garch3.summary())
garch3.plot()
# using Stdent's t distribution
garch4 = arch_model(dollar_euro2.xrate,dist='StudentsT').fit()
print(garch4.summary())
garch4.plot()
/usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:310: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when estimating the model parameters. The scale of y is 0.02518. Parameter estimation work better when this value is between 1 and 1000. The recommended rescaling is 10 * y. This warning can be disabled by either rescaling y before initializing the model or by setting rescale=False. data_scale_warning.format(orig_scale, rescale), DataScaleWarning /usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:310: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when estimating the model parameters. The scale of y is 0.02518. Parameter estimation work better when this value is between 1 and 1000. The recommended rescaling is 10 * y. This warning can be disabled by either rescaling y before initializing the model or by setting rescale=False. data_scale_warning.format(orig_scale, rescale), DataScaleWarning
Iteration: 1, Func. Count: 7, Neg. LLF: -5020.327718598877
Iteration: 2, Func. Count: 20, Neg. LLF: -5033.742504521877
Iteration: 3, Func. Count: 33, Neg. LLF: -5035.578939260673
Optimization terminated successfully. (Exit mode 0)
Current function value: -5035.578969695771
Iterations: 7
Function evaluations: 33
Gradient evaluations: 3
Constant Mean - GJR-GARCH Model Results
==============================================================================
Dep. Variable: xrate R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GJR-GARCH Log-Likelihood: 5035.58
Distribution: Normal AIC: -10061.2
Method: Maximum Likelihood BIC: -10027.8
No. Observations: 5868
Date: Mon, May 30 2022 Df Residuals: 5867
Time: 21:26:28 Df Model: 1
Mean Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
mu 1.1827 4.234e-03 279.319 0.000 [ 1.174, 1.191]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 5.0668e-04 1.226e-05 41.334 0.000 [4.827e-04,5.307e-04]
alpha[1] 0.2000 5.368e-03 37.262 6.885e-304 [ 0.189, 0.211]
gamma[1] 0.1000 9.161e-03 10.915 9.811e-28 [8.203e-02, 0.118]
beta[1] 0.7300 8.104e-03 90.075 0.000 [ 0.714, 0.746]
============================================================================
Covariance estimator: robust
Iteration: 1, Func. Count: 7, Neg. LLF: -5419.928643095245
Iteration: 2, Func. Count: 21, Neg. LLF: -5485.103600690492
Iteration: 3, Func. Count: 35, Neg. LLF: -5485.929735485458
Iteration: 4, Func. Count: 49, Neg. LLF: -5485.939764174315
Iteration: 5, Func. Count: 58, Neg. LLF: -5524.733030633241
Iteration: 6, Func. Count: 72, Neg. LLF: -5524.760797715954
Optimization terminated successfully. (Exit mode 0)
Current function value: -5524.760802474538
Iterations: 10
Function evaluations: 72
Gradient evaluations: 6
Constant Mean - TARCH/ZARCH Model Results
==============================================================================
Dep. Variable: xrate R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: TARCH/ZARCH Log-Likelihood: 5524.76
Distribution: Normal AIC: -11039.5
Method: Maximum Likelihood BIC: -11006.1
No. Observations: 5868
Date: Mon, May 30 2022 Df Residuals: 5867
Time: 21:26:28 Df Model: 1
Mean Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
mu 1.1804 8.792e-04 1342.615 0.000 [ 1.179, 1.182]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 3.1183e-03 2.212e-04 14.100 3.793e-45 [2.685e-03,3.552e-03]
alpha[1] 0.1964 7.907e-03 24.833 3.911e-136 [ 0.181, 0.212]
gamma[1] 9.7850e-03 1.277e-03 7.661 1.838e-14 [7.282e-03,1.229e-02]
beta[1] 0.7609 1.123e-02 67.783 0.000 [ 0.739, 0.783]
============================================================================
Covariance estimator: robust
/usr/local/lib/python3.7/dist-packages/arch/univariate/base.py:310: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when estimating the model parameters. The scale of y is 0.02518. Parameter estimation work better when this value is between 1 and 1000. The recommended rescaling is 10 * y. This warning can be disabled by either rescaling y before initializing the model or by setting rescale=False. data_scale_warning.format(orig_scale, rescale), DataScaleWarning
Iteration: 1, Func. Count: 7, Neg. LLF: -4821.794788001211
Iteration: 2, Func. Count: 24, Neg. LLF: -4841.7585110206655
Optimization terminated successfully. (Exit mode 0)
Current function value: -4841.758510811218
Iterations: 6
Function evaluations: 24
Gradient evaluations: 2
Constant Mean - GARCH Model Results
====================================================================================
Dep. Variable: xrate R-squared: 0.000
Mean Model: Constant Mean Adj. R-squared: 0.000
Vol Model: GARCH Log-Likelihood: 4841.76
Distribution: Standardized Student's t AIC: -9673.52
Method: Maximum Likelihood BIC: -9640.13
No. Observations: 5868
Date: Mon, May 30 2022 Df Residuals: 5867
Time: 21:26:28 Df Model: 1
Mean Model
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
mu 1.1815 4.579e-03 258.032 0.000 [ 1.172, 1.190]
Volatility Model
============================================================================
coef std err t P>|t| 95.0% Conf. Int.
----------------------------------------------------------------------------
omega 5.0369e-04 1.462e-05 34.452 4.262e-260 [4.750e-04,5.323e-04]
alpha[1] 0.2000 4.353e-03 45.941 0.000 [ 0.191, 0.209]
beta[1] 0.7800 5.365e-03 145.383 0.000 [ 0.769, 0.791]
Distribution
========================================================================
coef std err t P>|t| 95.0% Conf. Int.
------------------------------------------------------------------------
nu 12.0001 7.637e-02 157.137 0.000 [ 11.850, 12.150]
========================================================================
Covariance estimator: robust
Out[ ]:
In [ ]:
In [ ]:
Search for data series You can always search for data series on the FRED website. But sometimes it can be more convenient to search programmatically. fredapi provides a search() method that does a fulltext search and returns a DataFrame of results.
In [ ]:
fred.search('consumer confidence')
Out[ ]:
| id | realtime_start | realtime_end | title | observation_start | observation_end | frequency | frequency_short | units | units_short | seasonal_adjustment | seasonal_adjustment_short | last_updated | popularity | notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| series id | |||||||||||||||
| UMCSENT | UMCSENT | 2022-04-07 | 2022-04-07 | University of Michigan: Consumer Sentiment | 1952-11-01 | 2022-02-01 | Monthly | M | Index 1966:Q1=100 | Index 1966:Q1=100 | Not Seasonally Adjusted | NSA | 2022-03-25 10:01:02-05:00 | 78 | At the request of the source, the data is dela... |
| MICH | MICH | 2022-04-07 | 2022-04-07 | University of Michigan: Inflation Expectation | 1978-01-01 | 2022-02-01 | Monthly | M | Percent | % | Not Seasonally Adjusted | NSA | 2022-03-25 10:01:12-05:00 | 70 | Median expected price change next 12 months, S... |
| CSCICP03USM665S | CSCICP03USM665S | 2022-04-07 | 2022-04-07 | Consumer Opinion Surveys: Confidence Indicator... | 1960-01-01 | 2022-02-01 | Monthly | M | Normalised (Normal=100) | Normalised (Normal = 100) | Seasonally Adjusted | SA | 2022-03-10 10:39:06-06:00 | 49 | OECD descriptor ID: CSCICP03 OECD unit ID: IXN... |
| CSCICP03EZM665S | CSCICP03EZM665S | 2022-04-07 | 2022-04-07 | Consumer Opinion Surveys: Confidence Indicator... | 1973-01-01 | 2022-02-01 | Monthly | M | Normalised (Normal=100) | Normalised (Normal = 100) | Seasonally Adjusted | SA | 2022-03-10 10:47:08-06:00 | 22 | OECD descriptor ID: CSCICP03 OECD unit ID: IXN... |
| UMCSENT1 | UMCSENT1 | 2022-04-07 | 2022-04-07 | University of Michigan: Consumer Sentiment (DI... | 1952-11-01 | 1977-11-01 | Not Applicable | NA | Index 1966:Q1=100 | Index 1966:Q1=100 | Not Seasonally Adjusted | NSA | 2004-01-12 12:08:18-06:00 | 22 | Please see FRED data series UMCSENT for monthl... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| CCDIOA17460Q156N | CCDIOA17460Q156N | 2022-04-07 | 2022-04-07 | CredAbility Consumer Distress Index for Clevel... | 2005-01-01 | 2013-01-01 | Quarterly | Q | Percent | % | Not Seasonally Adjusted | NSA | 2013-05-17 14:01:30-05:00 | 1 | Overview of the Index The Index is a quarterly... |
| CCDIOA26420Q156N | CCDIOA26420Q156N | 2022-04-07 | 2022-04-07 | CredAbility Consumer Distress Index for Housto... | 2005-01-01 | 2013-01-01 | Quarterly | Q | Percent | % | Not Seasonally Adjusted | NSA | 2013-05-17 14:01:32-05:00 | 1 | Overview of the Index The Index is a quarterly... |
| CCDIOA37100Q156N | CCDIOA37100Q156N | 2022-04-07 | 2022-04-07 | CredAbility Consumer Distress Index for Oxnard... | 2005-01-01 | 2013-01-01 | Quarterly | Q | Percent | % | Not Seasonally Adjusted | NSA | 2013-05-17 14:01:50-05:00 | 1 | Overview of the Index The Index is a quarterly... |
| CCDIOA40140Q156N | CCDIOA40140Q156N | 2022-04-07 | 2022-04-07 | CredAbility Consumer Distress Index for Rivers... | 2005-01-01 | 2013-01-01 | Quarterly | Q | Percent | % | Not Seasonally Adjusted | NSA | 2013-05-17 14:01:19-05:00 | 1 | Overview of the Index The Index is a quarterly... |
| CCDIOA35300Q156N | CCDIOA35300Q156N | 2022-04-07 | 2022-04-07 | CredAbility Consumer Distress Index for New Ha... | 2005-01-01 | 2013-01-01 | Quarterly | Q | Percent | % | Not Seasonally Adjusted | NSA | 2013-05-17 14:01:42-05:00 | 1 | Overview of the Index The Index is a quarterly... |
297 rows × 15 columns
You can also search by release id and category id with various options
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df1 = fred.search_by_release(11)
df2 = fred.search_by_category(101, limit=10, order_by='popularity', sort_order='desc')
In [ ]:
df1
In [ ]:
df2
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info = fred.get_series_info('PAYEMS')
info['title']
Out[ ]:
'All Employees, Total Nonfarm'
In [ ]:
s = fred.get_series('SP500', observation_start='1/31/2014')
s.tail()
Out[ ]:
2022-03-30 4602.45 2022-03-31 4530.41 2022-04-01 4545.86 2022-04-04 4582.64 2022-04-05 4525.12 dtype: float64
In [ ]:
s = fred.get_series('SP500', observation_start='2014-09-02', observation_end='2014-09-05')
s.tail()
Out[ ]:
2014-09-02 2002.28 2014-09-03 2000.72 2014-09-04 1997.65 2014-09-05 2007.71 dtype: float64
You can also get a set of series IDs programmatically by release or category IDs. Several sorting options are also available. On the FRED website I know that the release ID 175 contains some personal income data. Let's fetch 5 most popular series in that set.
In [ ]:
personal_income_series = fred.search_by_release(175, limit=10, order_by='popularity', sort_order='desc')
personal_income_series['title']
Out[ ]:
series id PCPI06037 Per Capita Personal Income in Los Angeles Coun... SEAT653PCPI Per Capita Personal Income in Seattle-Tacoma-B... DALL148PCPI Per Capita Personal Income in Dallas-Fort Wort... ATLA013PCPI Per Capita Personal Income in Atlanta-Sandy Sp... SANF806PCPI Per Capita Personal Income in San Francisco-Oa... PCPI06073 Per Capita Personal Income in San Diego County... PCPI12086 Per Capita Personal Income in Miami-Dade Count... HOUS448PCPI Per Capita Personal Income in Houston-The Wood... SANA748PCPI Per Capita Personal Income in San Antonio-New ... PHOE004PCPI Per Capita Personal Income in Phoenix-Mesa-Sco... Name: title, dtype: object
In [ ]:
import pandas as pd
df = {}
df['SF'] = fred.get_series('PCPI06075')
df['NY'] = fred.get_series('PCPI36061')
df['DC'] = fred.get_series('PCPI11001')
df = pd.DataFrame(df)
df.plot()
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7ff3691b2910>
In [ ]: