In the last decade, several HRV based novel methodologies for describing and assessing heart rate dynamics have been proposed in the literature with the aim of risk assessment. Such methodologies attempt to describe the non-linear and complex characteristics of HRV, and hereby the focus is in two of these characteristics, namely long memory and heteroscedasticity with variance clustering. The A...

We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC o...

in this paper we investigate the long memory of tehran securities price index and fit arfima model using 970 daily data since 1382/1/6 until 1386/4/17. furthermore, we compare the forecasting performance of arfima and arima models. the results show that the series is a long memory one and therefore it can become stationary by fractional differencing. we obtaine the fractional differencing param...

Indonesia's price index serves as a barometer for the nation's economic condition. One of Indonesia’s is Wholesale Price Index (WPI). WPI that tracks average change in wholesale prices over time. Time series analysis can be used forecasting because one time data. long memory, which condition data from different periods have high link despite being separated by large amount The Autoregressive Fr...

Jiang and and Tian (2010) have estimated an ARFIMA model for stock return volatility. We argue that this result does not imply actual 'long memory' in such time series -as any kind of instability in the population mean yields apparent fractional integration as a statistical artifact. Alternative high-pass filters for studying stock market volatility data are suggested.

This review offers a guided tour to PcGive 10 modules for econometrics analysis of time series (PcGive), limited dependent variable (LogitJD) and static and dynamic panel data analyses (DPD), financial econometric (GARCH) and time series (ARFIMA) modelling. Several empirical applications are reported to illustrate the package.

We consider the fractionally integrated ARFIMA Processes with seasonality s, denoted by SARFIMA(0, D, 0)s. This work presents a closed formula for the Durbin-Levinson’s algorithm relating the partial autocorrelation and the autocorrelation functions of these processes. In order to obtain the closed formula we show a hypergeometric identity, namely