Add to ORKGAbstract A desirable property for an estimator of the fractional ARFIMA parameter is to be first difference invariant. This paper investigates the effects on the fractional parameter estimator in nonstationary ARFIMA(p,d,q) pro-cesses before and after applying a first difference. We consider semiparametric and parametric approaches for estimating d. The study is based on a Monte Carlo simulation for different sample sizes. The Brazilian exchange rate series is given as an application of the methodology
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
Processes with correlated errors have been widely used in economic time series. The fractionally int...
The prime goal of this research is to model the long-range dependency and volatility factors fitting...
In this paper we consider the estimation of the fractional parameter d and the au-toregressive and m...
In this paper we construct a test for the difference parameter d in the fractionally integrated auto...
This paper investigates the out-of-sample forecast performance of the autoregressive fractionally in...
This paper describes a parameter estimation method for both stationary and non-stationary ARFIMA (p,...
This paper investigates the persistence of the long memory property in the daily stock index EGX30. ...
It is known that, in the presence of short memory components, the estimation of the fractional param...
The Purchasing Power Parity (PPP) hypothesis is one of the most important theoretical relationships ...
This article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) mo...
ARFIMA models - AutoRegressive Fractional Integrated Moving Average modelsSIGLEGBUnited Kingdo
Strong coupling between values at different times that exhibit properties of long range dependence, ...
We propose methods for monitoring the residuals of a fitted ARIMA or an autoregressive fractionally ...
In this paper, we introduce a new class of models called Threshold ARFIMA (Fractionally Integrated A...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
Processes with correlated errors have been widely used in economic time series. The fractionally int...
The prime goal of this research is to model the long-range dependency and volatility factors fitting...
In this paper we consider the estimation of the fractional parameter d and the au-toregressive and m...
In this paper we construct a test for the difference parameter d in the fractionally integrated auto...
This paper investigates the out-of-sample forecast performance of the autoregressive fractionally in...
This paper describes a parameter estimation method for both stationary and non-stationary ARFIMA (p,...
This paper investigates the persistence of the long memory property in the daily stock index EGX30. ...
It is known that, in the presence of short memory components, the estimation of the fractional param...
The Purchasing Power Parity (PPP) hypothesis is one of the most important theoretical relationships ...
This article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) mo...
ARFIMA models - AutoRegressive Fractional Integrated Moving Average modelsSIGLEGBUnited Kingdo
Strong coupling between values at different times that exhibit properties of long range dependence, ...
We propose methods for monitoring the residuals of a fitted ARIMA or an autoregressive fractionally ...
In this paper, we introduce a new class of models called Threshold ARFIMA (Fractionally Integrated A...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
Processes with correlated errors have been widely used in economic time series. The fractionally int...
The prime goal of this research is to model the long-range dependency and volatility factors fitting...