88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
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"""United States Macroeconomic data"""
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from statsmodels.datasets import utils as du
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__docformat__ = 'restructuredtext'
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COPYRIGHT = """This is public domain."""
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TITLE = __doc__
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SOURCE = """
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Compiled by Skipper Seabold. All data are from the Federal Reserve Bank of St.
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Louis [1] except the unemployment rate which was taken from the National
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Bureau of Labor Statistics [2]. ::
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[1] Data Source: FRED, Federal Reserve Economic Data, Federal Reserve Bank of
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St. Louis; http://research.stlouisfed.org/fred2/; accessed December 15,
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2009.
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[2] Data Source: Bureau of Labor Statistics, U.S. Department of Labor;
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http://www.bls.gov/data/; accessed December 15, 2009.
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"""
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DESCRSHORT = """US Macroeconomic Data for 1959Q1 - 2009Q3"""
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DESCRLONG = DESCRSHORT
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NOTE = """::
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Number of Observations - 203
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Number of Variables - 14
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Variable name definitions::
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year - 1959q1 - 2009q3
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quarter - 1-4
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realgdp - Real gross domestic product (Bil. of chained 2005 US$,
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seasonally adjusted annual rate)
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realcons - Real personal consumption expenditures (Bil. of chained
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2005 US$, seasonally adjusted annual rate)
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realinv - Real gross private domestic investment (Bil. of chained
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2005 US$, seasonally adjusted annual rate)
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realgovt - Real federal consumption expenditures & gross investment
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(Bil. of chained 2005 US$, seasonally adjusted annual rate)
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realdpi - Real private disposable income (Bil. of chained 2005
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US$, seasonally adjusted annual rate)
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cpi - End of the quarter consumer price index for all urban
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consumers: all items (1982-84 = 100, seasonally adjusted).
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m1 - End of the quarter M1 nominal money stock (Seasonally
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adjusted)
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tbilrate - Quarterly monthly average of the monthly 3-month
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treasury bill: secondary market rate
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unemp - Seasonally adjusted unemployment rate (%)
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pop - End of the quarter total population: all ages incl. armed
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forces over seas
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infl - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)
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realint - Real interest rate (tbilrate - infl)
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"""
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def load_pandas():
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data = _get_data()
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return du.Dataset(data=data, names=list(data.columns))
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def load():
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"""
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Load the US macro data and return a Dataset class.
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Returns
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-------
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Dataset
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See DATASET_PROPOSAL.txt for more information.
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Notes
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-----
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The macrodata Dataset instance does not contain endog and exog attributes.
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"""
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return load_pandas()
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def _get_data():
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return du.load_csv(__file__, 'macrodata.csv').astype(float)
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variable_names = ["realcons", "realgdp", "realinv"]
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def __str__():
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return "macrodata"
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