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  1. Papers
  2. Gürkaynak, et al. (2007)
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  • Papers
    • Dickerson, et al. (2023)
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    • Gürkaynak, et al. (2007)
    • Hassan, et al. (2019)
    • Hoberg, Phillips (2010, 2016)
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On this page

  • get_raw_data
  • list_all_vars
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  1. Papers
  2. Gürkaynak, et al. (2007)

Gürkaynak, et al. (2007)

Daily estimates of US Treasury yield curve from Gürkaynak, Refet S, Brian Sack, and Jonathan H Wright, 2007

This module downloads and processes data developed by:

  • Gürkaynak, Refet S, Brian Sack, and Jonathan H Wright, 2007, The US Treasury yield curve: 1961 to the present, Journal of Monetary Economics 54, 2291–2304. https://doi.org/10.1016/j.jmoneco.2007.06.029

See the FED Board dedicated website for more information on this dataset: https://www.federalreserve.gov/data/nominal-yield-curve.htm

PROVIDER = 'Gürkaynak, Refet S, Brian Sack, and Jonathan H Wright, 2007'
URL = 'https://www.federalreserve.gov/data/yield-curve-tables/feds200628.csv'
HOST_WEBSITE = 'https://www.federalreserve.gov/data/nominal-yield-curve.htm'
FREQ = 'D'
MIN_YEAR = 1961
MAX_YEAR = None
TIME_VAR_IN_RAW_DSET = 'Date'
TIME_VAR_IN_CLEAN_DSET = f'{FREQ}date'

source

get_raw_data

 get_raw_data (url:str='https://www.federalreserve.gov/data/yield-curve-
               tables/feds200628.csv', nrows:int=None, delimiter:str=',',
               skiprows:int=9, headers:dict=None)

Download raw data from url

Type Default Details
url str https://www.federalreserve.gov/data/yield-curve-tables/feds200628.csv
nrows int None How many rows to download. If None, all rows are downloaded
delimiter str ,
skiprows int 9
headers dict None
Returns pd.DataFrame
raw = get_raw_data(nrows=3)
raw
Date BETA0 BETA1 BETA2 BETA3 SVEN1F01 SVEN1F04 SVEN1F09 SVENF01 SVENF02 ... SVENY23 SVENY24 SVENY25 SVENY26 SVENY27 SVENY28 SVENY29 SVENY30 TAU1 TAU2
0 1961-06-14 3.917606 -1.277955 -1.949397 0 3.8067 3.9562 NaN 3.5492 3.8825 ... NaN NaN NaN NaN NaN NaN NaN NaN 0.339218 -999.99
1 1961-06-15 3.978498 -1.257404 -2.247617 0 3.8694 4.0183 NaN 3.5997 3.9460 ... NaN NaN NaN NaN NaN NaN NaN NaN 0.325775 -999.99

2 rows × 100 columns


source

list_all_vars

 list_all_vars ()
list_all_vars()
name
0 Date
1 BETA0
2 BETA1
3 BETA2
4 BETA3
... ...
95 SVENY28
96 SVENY29
97 SVENY30
98 TAU1
99 TAU2

100 rows × 1 columns


source

process_raw_data

 process_raw_data (df:pandas.core.frame.DataFrame=None)
clean = process_raw_data(raw)
clean
Date dtdate BETA0 BETA1 BETA2 BETA3 SVEN1F01 SVEN1F04 SVEN1F09 SVENF01 ... SVENY23 SVENY24 SVENY25 SVENY26 SVENY27 SVENY28 SVENY29 SVENY30 TAU1 TAU2
Ddate
1961-06-14 1961-06-14 1961-06-14 3.917606 -1.277955 -1.949397 0 3.8067 3.9562 NaN 3.5492 ... NaN NaN NaN NaN NaN NaN NaN NaN 0.339218 -999.99
1961-06-15 1961-06-15 1961-06-15 3.978498 -1.257404 -2.247617 0 3.8694 4.0183 NaN 3.5997 ... NaN NaN NaN NaN NaN NaN NaN NaN 0.325775 -999.99

2 rows × 101 columns

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