We present a generalisation of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coefficients both in the mean and in the variance. The model satisfies the empirical evidence of changing memory observed in average temperature series and can provide useful improvements in the forecasting, simulation and pricing issues related to weather derivatives. We provide an applic...

We address the question of unemployment hysteresis within the context of ARFIMA models. Our results suggest that in the post-1973 era, hysteresis is considerably less of a stylized fact for the unemployment rates of key OECD economies. JEL Categories C22, E30 Please address all correspondence to: Philip Rothman, Department of Economics, Brewster * Building, East Carolina University, Greenville,...

In this paper, we consider a continuous-time autoregressive fractionally integrated moving average (CARFIMA) model, which is defined as the stationary solution of a stochastic differential equation driven by a standard fractional Brownian motion. Like the discrete-time ARFIMA model, the CARFIMA model is useful for studying time series with short memory, long memory and antipersistence. We inves...

We propose a hybrid forecast based on extended discrete grey Markov and variable dimension Kalman model and show that our hybrid model can improve much more the performance of forecast than traditional grey Markov and Kalman models. Our simulation results are given to demonstrate that our hybrid forecast method combined with degree of grey incidence are better than grey Markov and ARFIMA model ...

In a recent paper in The Journal of Monetary Economics, Michelacci and Zaffaroni (2000) estimate long memory parameters for GDP per capita of 16 OECD countries. In this note we argue that these estimations are questionable for the purposes of clarifying the time series properties of these data (presence of unit roots, mean reversion, long memory) because the authors a) filter out a deterministi...

Gegenbauer processes allow for flexible and convenient modeling of time series data with multiple spectral peaks, where the qualitative description of these peaks is via the concept of cyclical long-range dependence. The Gegenbauer class is extensive, including ARFIMA, seasonal ARFIMA, and GARMA processes as special cases. Model estimation is challenging for Gegenbauer processes when multiple z...

Quantifying how patterns of behavior relate across multiple levels of measurement typically requires long time series for reliable parameter estimation. We describe a novel analysis that estimates patterns of variability across multiple scales of analysis suitable for time series of short duration. The multiscale coefficient of variation (MSCV) measures the distance between local coefficient of...

Abstract We analyze by simulation the properties of two time domain and two frequency domain estimators for low order autoregressive fractionally integrated moving average Gaussian models, ARFIMA (p; d; q). The estimators considered are the exact maximum likelihood for demeaned data, EML, the associated modi ed pro le likelihood, MPL, and the Whittle estimator with, WLT, and without tapered dat...