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The Topology of Time Series: Improving Recession Forecasting from Yield Spreads
EconPapers
Swansea University Author: Simon Rudkin
Abstract
Recession forecasting ranges from the simplistic inference from witnessing an inverted yield curve to sophisticated models drawing data from across the macroeconomic and financial spectra. Each has advantages, in simplicity and informativeness respectively, but each suffers for these. Demonstrating...
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2019
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https://econpapers.repec.org/RePEc:swn:wpaper:2019-02 |
URI: | https://cronfa.swan.ac.uk/Record/cronfa51888 |
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2021-03-16T12:14:49.4255547 v2 51888 2019-09-12 The Topology of Time Series: Improving Recession Forecasting from Yield Spreads 93f12293ea6ed07ae8162cf25659c5f2 0000-0001-8622-7318 Simon Rudkin Simon Rudkin true false 2019-09-12 ECON Recession forecasting ranges from the simplistic inference from witnessing an inverted yield curve to sophisticated models drawing data from across the macroeconomic and financial spectra. Each has advantages, in simplicity and informativeness respectively, but each suffers for these. Demonstrating how the properties of yield spread time series can foretell of impending recessions we introduce data topology to economics. Through an exploration of the topology of time series we highlight an untapped source of information with the potential to significantly improve understanding of the economy. Advantageously we do so without risking the overfitting of introducing other variables. Working paper EconPapers Economic Cycles, Topological Data Analysis, Periodicity, Yield Curve, Persistent Homology 4 7 2019 2019-07-04 https://econpapers.repec.org/RePEc:swn:wpaper:2019-02 Working paper available at https://econpapers.repec.org/paper/swnwpaper/2019-02.htm COLLEGE NANME Economics COLLEGE CODE ECON Swansea University 2021-03-16T12:14:49.4255547 2019-09-12T16:33:56.0794982 Faculty of Humanities and Social Sciences School of Management Pawel Dlotko 1 Simon Rudkin 0000-0001-8622-7318 2 |
title |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads |
spellingShingle |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads Simon Rudkin |
title_short |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads |
title_full |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads |
title_fullStr |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads |
title_full_unstemmed |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads |
title_sort |
The Topology of Time Series: Improving Recession Forecasting from Yield Spreads |
author_id_str_mv |
93f12293ea6ed07ae8162cf25659c5f2 |
author_id_fullname_str_mv |
93f12293ea6ed07ae8162cf25659c5f2_***_Simon Rudkin |
author |
Simon Rudkin |
author2 |
Pawel Dlotko Simon Rudkin |
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Working paper |
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EconPapers |
publishDate |
2019 |
institution |
Swansea University |
college_str |
Faculty of Humanities and Social Sciences |
hierarchytype |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
hierarchy_parent_title |
Faculty of Humanities and Social Sciences |
department_str |
School of Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management |
url |
https://econpapers.repec.org/RePEc:swn:wpaper:2019-02 |
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description |
Recession forecasting ranges from the simplistic inference from witnessing an inverted yield curve to sophisticated models drawing data from across the macroeconomic and financial spectra. Each has advantages, in simplicity and informativeness respectively, but each suffers for these. Demonstrating how the properties of yield spread time series can foretell of impending recessions we introduce data topology to economics. Through an exploration of the topology of time series we highlight an untapped source of information with the potential to significantly improve understanding of the economy. Advantageously we do so without risking the overfitting of introducing other variables. |
published_date |
2019-07-04T04:03:56Z |
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1763753314322743296 |
score |
11.035349 |