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Wavelet improvement in turning point detection using a hidden Markov model: from the aspects of cyclical identification and outlier correction

Li, Yushu LU and Reese, Simon LU (2014) In Computational Statistics 29(6). p.1481-1496
Abstract
The hidden Markov model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (Econometrica 57(2):357–384, 1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process... (More)
The hidden Markov model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (Econometrica 57(2):357–384, 1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
HMM, Turning point, Wavelet, Wavelet power spectrum, Outlier
in
Computational Statistics
volume
29
issue
6
pages
1481 - 1496
publisher
Physica Verlag
external identifiers
  • wos:000345099200005
  • scopus:84912016395
ISSN
0943-4062
DOI
10.1007/s00180-014-0502-5
language
English
LU publication?
yes
id
92f03e7d-a9ea-4753-bb52-cac4eda11fce (old id 4456719)
alternative location
http://link.springer.com/article/10.1007%2Fs00180-014-0502-5
date added to LUP
2014-06-13 16:24:55
date last changed
2017-05-07 03:11:05
@article{92f03e7d-a9ea-4753-bb52-cac4eda11fce,
  abstract     = {The hidden Markov model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (Econometrica 57(2):357–384, 1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice.},
  author       = {Li, Yushu and Reese, Simon},
  issn         = {0943-4062},
  keyword      = {HMM,Turning point,Wavelet,Wavelet power spectrum,Outlier},
  language     = {eng},
  number       = {6},
  pages        = {1481--1496},
  publisher    = {Physica Verlag},
  series       = {Computational Statistics},
  title        = {Wavelet improvement in turning point detection using a hidden Markov model: from the aspects of cyclical identification and outlier correction},
  url          = {http://dx.doi.org/10.1007/s00180-014-0502-5},
  volume       = {29},
  year         = {2014},
}