It is known that, in the presence of short memory components, the estimation of the fractional parameter d in an Autoregressive Fractionally Integrated Moving Average, ARFIMA(p, d, q), process leads to some difficulties (Smith et al. (1997)). In this paper, we continue the efforts made by Smith et al. (1997) by conducting a simulation study to evaluate the convergence properties of the iterative estimation procedure suggested by Hosking (1981 ). In this conteJ...'t we consider some semiparametric approaches and a parametric method proposed by Whittle (1953). We also investigate the method proposed by Robinson (1995a) and a modification using the smoothed periodogram function
Computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models are address...
Fractionally integrated processes ARFIMA(p,d,q), introduced by Granger (1980) and Hosking (1981) ind...
This paper investigates the out-of-sample forecast performance of the autoregressive fractionally in...
It is known that, in the presence of short memory components, the estimation of the fractional param...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
In this paper we consider the estimation of the fractional parameter d and the au-toregressive and m...
Processes with correlated errors have been widely used in economic time series. The fractionally int...
Strong coupling between values at different times that exhibit properties of long range dependence, ...
This paper describes a parameter estimation method for both stationary and non-stationary ARFIMA (p,...
Computational aspects of likelihood-based estimation of univariate ARFIMA(p,d,q) models are addresse...
In this paper, it is proposed to modify autoregressive fractionally integrated moving average (ARFIM...
Abstract. For the autoregressive fractionally integrated moving average (ARFIMA) processes which cha...
This article considers the fractionally autoregressive integrated moving average [ARFIMA(p, d, q)] m...
In practice, several time series exhibit long-range dependence or per-sistence in their observations...
Computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models are address...
Fractionally integrated processes ARFIMA(p,d,q), introduced by Granger (1980) and Hosking (1981) ind...
This paper investigates the out-of-sample forecast performance of the autoregressive fractionally in...
It is known that, in the presence of short memory components, the estimation of the fractional param...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
In this paper we consider the estimation of the fractional parameter d and the au-toregressive and m...
Processes with correlated errors have been widely used in economic time series. The fractionally int...
Strong coupling between values at different times that exhibit properties of long range dependence, ...
This paper describes a parameter estimation method for both stationary and non-stationary ARFIMA (p,...
Computational aspects of likelihood-based estimation of univariate ARFIMA(p,d,q) models are addresse...
In this paper, it is proposed to modify autoregressive fractionally integrated moving average (ARFIM...
Abstract. For the autoregressive fractionally integrated moving average (ARFIMA) processes which cha...
This article considers the fractionally autoregressive integrated moving average [ARFIMA(p, d, q)] m...
In practice, several time series exhibit long-range dependence or per-sistence in their observations...
Computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models are address...
Fractionally integrated processes ARFIMA(p,d,q), introduced by Granger (1980) and Hosking (1981) ind...
This paper investigates the out-of-sample forecast performance of the autoregressive fractionally in...