| dc.description.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
(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. | nb_NO |
