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We report the results of applying several long-memory models to the historical monthly U.S. inflation rate series and analyze their out-of-sample forecasting performance over different horizons. We find that the time-varying approach to estimating inflation persistence outperforms the models that assume a constant long-memory process. In addition, we examine the link between inflation persistence and exchange rate regimes. Our results support the hypothesis that floating exchange rates associate with increased inflation persistence. This finding, however, is less pronounced during the era of the Great Moderation and the Federal Reserve System's commitment to inflation targeting. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid approach, based on the combination of parametric and nonparametric models by adopting wavelet estimation approach, to model and predict the price electricity for Nord Pool market. Our hybrid methodology consists into two steps. The first step aims at modeling the conditional mean of the time series, using a generalized fractional model with k‐factor of Gegenbauer termed the k‐factor GARMA model; the parameters of this model are estimated using the wavelet approach based on the discrete wavelet packet transform (DWPT). The second step aims at estimating the conditional variance, so we adopt the local linear wavelet neural network (LLWNN) model. The proposed hybrid model is tested using the hourly log‐returns of electricity spot price from the Nord Pool market. The empirical results were compared with the predictions of the ARFIMA–LLWNN, the k‐factor GARMA–FIGARCH and the individual LLWNN models. It is shown that the proposed hybrid k‐factor GARMA–LLWNN model outperforms all other competing methods. Hence it is a robust tool in forecasting time series. [ABSTRACT FROM AUTHOR]

This paper proposes a model of time-varying fractional integration where the long-memory parameter, d, in an ARFIMA model is allowed to depend on t and evolve according to a Smooth Transition Regressive (STR) model advanced by Teräsvirta (1994, 1998). To estimate the time-varying fractional integration parameter, we suggest a new multi-step estimation method based on the wavelet approach using the instantaneous least squares estimator (ILSE). We conduct some simulation experiments and we find that our estimation iterative procedure performs better than that proposed by Boutahar, Dufrénot, and Péguin-Feissolle (2008). An empirical application of this methodology to the volatility of some financial time series is used for illustration purposes. Finally, it is shown that the model proposed offers an interesting framework to describe long-range dependence in the volatility with heterogeneous persistence. [ABSTRACT FROM AUTHOR]

In this paper, we examine the relationship between the Tunisian Dinar relative to US Dollar, the Euro and the Japanese Yen using multivariate fractional cointegration approach developed by Davidson (2002, 2006). The main advantage of this approach is to detect the long term relationship as well as the short term dynamics and to represent the interdependence between the variables. Moreover, we implement a causal analysis (Granger (1988)) to examine the links between the variables. The empirical results provide evidence of fractional cointegration between the exchanges rates and show long-run causal links between the variables. In particular, we find a significant bidirectional causal links. Thus, we conclude to the existence of interdependence between the exchange rate series and we reject the hypothesis of weak informational efficiency in the Tunisian exchange market. [ABSTRACT FROM AUTHOR]