2024-10-02 22:15:59 +04:00

77 lines
1.7 KiB
Python

"""Danish Money Demand Data"""
import pandas as pd
from statsmodels.datasets import utils as du
__docformat__ = "restructuredtext"
COPYRIGHT = """This is public domain."""
TITLE = __doc__
SOURCE = """
Danish data used in S. Johansen and K. Juselius. For estimating
estimating a money demand function::
[1] Johansen, S. and Juselius, K. (1990), Maximum Likelihood Estimation
and Inference on Cointegration - with Applications to the Demand
for Money, Oxford Bulletin of Economics and Statistics, 52, 2,
169-210.
"""
DESCRSHORT = """Danish Money Demand Data"""
DESCRLONG = DESCRSHORT
NOTE = """::
Number of Observations - 55
Number of Variables - 5
Variable name definitions::
lrm - Log real money
lry - Log real income
lpy - Log prices
ibo - Bond rate
ide - Deposit rate
"""
def load_pandas():
data = _get_data()
data.index.freq = "QS-JAN"
return du.Dataset(data=data, names=list(data.columns))
def load():
"""
Load the US macro data and return a Dataset class.
Returns
-------
Dataset
See DATASET_PROPOSAL.txt for more information.
Notes
-----
The Dataset instance does not contain endog and exog attributes.
"""
return load_pandas()
def _get_data():
data = du.load_csv(__file__, "data.csv")
for i, val in enumerate(data.period):
parts = val.split("Q")
month = (int(parts[1]) - 1) * 3 + 1
data.loc[data.index[i], "period"] = f"{parts[0]}-{month:02d}-01"
data["period"] = pd.to_datetime(data.period)
return data.set_index("period").astype(float)
variable_names = ["lrm", "lry", "lpy", "ibo", "ide"]
def __str__():
return "danish_data"