A Generalized ARFIMA Process with Markov-Switching Fractional Differencing Parameter

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.

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Year of publication:	2007-04
Authors:	Tsay, Wen-Jen ; Härdle, Wolfgang
Institutions:	Sonderforschungsbereich 649: Ökonomisches Risiko, Wirtschaftswissenschaftliche Fakultät
Subject:	Markov chain \| ARFIMA process \| Viterbi algorithm \| Long memory

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Extent:	application/pdf
Series:	SFB 649 Discussion Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number SFB649DP2007-022 29 pages
Classification:	C14 - Semiparametric and Nonparametric Methods ; C22 - Time-Series Models ; C32 - Time-Series Models ; C52 - Model Evaluation and Testing ; C53 - Forecasting and Other Model Applications ; G12 - Asset Pricing
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005678044