Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25194
Authors: 
Tsay, Wen-Jen
Härdle, Wolfgang Karl
Year of Publication: 
2007
Series/Report no.: 
SFB 649 discussion paper 2007,022
Abstract: 
We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm combines the Durbin-Levinson and Viterbi procedures. A Monte Carlo experiment reveals that the finite sample performance of the proposed algorithm for a simple mixture model of Markov-switching mean and ARFIMA(1, d, 1) process is satisfactory. We apply the Markov-switching-ARFIMA models to the U.S. real interest rates, the Nile river level, and the U.S. unemployment rates, respectively. The results are all highly consistent with the conjectures made or empirical results found in the literature. Particularly, we confirm the conjecture in Beran and Terrin (1996) that the observations 1 to about 100 of the Nile river data seem to be more independent than the subsequent observations, and the value of differencing parameter is lower for the first 100 observations than for the subsequent data.
Subjects: 
Markov chain
ARFIMA process
Viterbi algorithm
Long memory
JEL: 
C14
C22
C32
C52
C53
G12
Document Type: 
Working Paper

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