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DOC: Refactor econ docstrings to numpydoc format #20

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DOC: Refactor econ docstrings to numpydoc format #20

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39 changes: 23 additions & 16 deletions fecon236/econ/infl.py
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Original file line number Diff line number Diff line change
@@ -1,16 +1,21 @@
# Python Module for import Date : 2018-06-17
# vim: set fileencoding=utf-8 ff=unix tw=78 ai syn=python : per PEP 0263
'''
_______________| infl.py :: Inflation module for fecon236
"""Inflation module for ``fecon236``

- "Unified" inflation is a sythesis of CPI, CPIc, PCE, PCEc.
- For derivation of ``foreinfl`` see fecon235/nb/fred-inflation.ipynb,
which is rendered as https://git.io/infl

- For derivation of foreinfl() see fecon235/nb/fred-inflation.ipynb,
which is rendered as https://git.io/infl
Notes
-----
For LATEST version, see https://git.io/fecon236

CHANGE LOG For LATEST version, see https://git.io/fecon236
2018-06-17 Spin-off foreinfl() from top.py.
'''
Change Log
----------

* 2018-06-17 Spin-off ``foreinfl`` from ``top.py``.

"""

from __future__ import absolute_import, print_function, division

Expand All @@ -23,15 +28,17 @@


def foreinfl(n=120, alpha=1.0, beta=0.3673):
'''Forecast Unified Inflation 1-year ahead per https://git.io/infl
which a rendering of fecon235/nb/fred-inflation.ipynb.
SUMMARY output: [Average, "infl-date", GMR, HW, BEI]
e.g. [2.2528, '2018-01-01', 1.5793, 3.0791, 2.1000]
where Average is the mean of three orthogonal methods:
GMR for geometric mean rate, HW for Holt-Winters time-series,
and BEI for Break-even Inflation from the Treasury bond market.
Default n denotes 120-month history, i.e. last 10 years.
'''
"""Forecast Unified Inflation 1-year ahead

per https://git.io/infl which a rendering of
fecon235/nb/fred-inflation.ipynb.

output: [Average, "infl-date", GMR, HW, BEI] e.g. [2.2528, '2018-01-01',
1.5793, 3.0791, 2.1000] where Average is the mean of three orthogonal
methods: GMR for geometric mean rate, HW for Holt-Winters time-series,
and BEI for Break-even Inflation from the Treasury bond market. Default n
denotes 120-month history, i.e. last 10 years.
"""
# Holt-Winters parameters alpha and beta are optimized
# from the 1960-2018 dataset, consisting of 697 monthly points.
# Each "way" is an orthogonal method, to be averaged into way[0].
Expand Down