A Generalized ARFIMA Process with Markov-Switching Fractional Differencing Parameter
Wen-Jen Tsay () and
Wolfgang Härdle ()
SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649
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.
Keywords: Markov chain; ARFIMA process; Viterbi algorithm; Long memory. (search for similar items in EconPapers)
JEL-codes: C14 C22 C32 C52 C53 G12 (search for similar items in EconPapers)
Pages: 29 pages
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:hum:wpaper:sfb649dp2007-022
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