77 lines
1.7 KiB
Python
77 lines
1.7 KiB
Python
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"""Danish Money Demand Data"""
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import pandas as pd
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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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Danish data used in S. Johansen and K. Juselius. For estimating
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estimating a money demand function::
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[1] Johansen, S. and Juselius, K. (1990), Maximum Likelihood Estimation
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and Inference on Cointegration - with Applications to the Demand
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for Money, Oxford Bulletin of Economics and Statistics, 52, 2,
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169-210.
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"""
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DESCRSHORT = """Danish Money Demand Data"""
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DESCRLONG = DESCRSHORT
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NOTE = """::
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Number of Observations - 55
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Number of Variables - 5
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Variable name definitions::
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lrm - Log real money
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lry - Log real income
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lpy - Log prices
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ibo - Bond rate
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ide - Deposit rate
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"""
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def load_pandas():
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data = _get_data()
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data.index.freq = "QS-JAN"
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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 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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data = du.load_csv(__file__, "data.csv")
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for i, val in enumerate(data.period):
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parts = val.split("Q")
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month = (int(parts[1]) - 1) * 3 + 1
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data.loc[data.index[i], "period"] = f"{parts[0]}-{month:02d}-01"
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data["period"] = pd.to_datetime(data.period)
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return data.set_index("period").astype(float)
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variable_names = ["lrm", "lry", "lpy", "ibo", "ide"]
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def __str__():
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return "danish_data"
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