Wavelet Improvement in Turning Point Detection using a HMM Model
AbstractThe Hidden Markov Model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (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 volatility or combine multiple types of cycles that have different frequency bands. Moreover, the outliers will be frequently misidentified as turning points in the HMM framework. The present paper will also show that these issues can be resolved by wavelet multi-resolution analysis based methods, due to their ability to decompose a series into different frequency bands. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. Thus, the underlying information for the data at different frequency bands can be extracted by wavelet decomposition with different frequency bands, and the outliers can be detected by high-frequency wavelet detail. We apply a Monte Carlo experiment to show that detection accuracy is highly improved for HMM when it is combined with the wavelet approach. An empirical example is illustrated using US GDP growth rate data.
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Bibliographic InfoPaper provided by Lund University, Department of Economics in its series Working Papers with number 2012:14.
Length: 22 pages
Date of creation: 21 May 2012
Date of revision:
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Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
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Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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HMM; turning point; wavelet; outlier;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-29 (All new papers)
- NEP-ECM-2012-05-29 (Econometrics)
- NEP-ETS-2012-05-29 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
- Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, EconWPA.
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