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Long Memory Processes (long + memory_process)

Distribution by Scientific Domains

Mathematics and Statistics	50%
Business, Economics, Finance and Accounting	50%

Selected Abstracts

Forecasting with k -factor Gegenbauer Processes: Theory and Applications

JOURNAL OF FORECASTING, Issue 8 2001
L. Ferrara
Abstract This paper deals with the k -factor extension of the long memory Gegenbauer process proposed by Gray et al. (1989). We give the analytic expression of the prediction function derived from this long memory process and provide the h -step-ahead prediction error when parameters are either known or estimated. We investigate the predictive ability of the k -factor Gegenbauer model on real data of urban transport traffic in the Paris area, in comparison with other short- and long-memory models. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Estimation of the location and exponent of the spectral singularity of a long memory process

JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2004
Javier Hidalgo
Abstract., We consider the estimation of the location of the pole and memory parameter ,0 and d of a covariance stationary process with spectral density We investigate optimal rates of convergence for the estimators of ,0 and d, and the consequence that the lack of knowledge of ,0 has on the estimation of the memory parameter d. We present estimators which achieve the optimal rates. A small Monte-Carlo study is included to illustrate the finite sample performance of our estimators. [source]

A NOTE ON CHAMBERS'S "LONG MEMORY AND AGGREGATION IN MACROECONOMIC TIME SERIES",

INTERNATIONAL ECONOMIC REVIEW, Issue 3 2005
Leonardo Rocha Souza
This note reviews some results on aggregating discrete-time long memory processes, providing a formula for the spectrum of the aggregates that considers the aliasing effect. [source]

Generalized long memory processes, failure of cointegration tests and exchange rate dynamics

JOURNAL OF APPLIED ECONOMETRICS, Issue 4 2006
Aaron D. Smallwood
This paper presents evidence that the equilibrium relationship in a system of nominal exchange rates is best described as a stationary GARMA process. The implementation of the GARMA methodology helps explain conflicting and puzzling results from the use of linear cointegration and fractional cointegration methods. Furthermore, we use Monte Carlo analysis to document problems with standard cointegration tests when the attraction process is distributed as a long memory GARMA process. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Simulating a class of stationary Gaussian processes using the Davies,Harte algorithm, with application to long memory processes

JOURNAL OF TIME SERIES ANALYSIS, Issue 5 2003
PETER F. CRAIGMILE
We demonstrate that the fast and exact Davies,Harte algorithm is valid for simulating a certain class of stationary Gaussian processes , those with a negative autocovariance sequence for all non-zero lags. The result applies to well known classes of long memory processes: Gaussian fractionally differenced (FD) processes, fractional Gaussian noise (fGn) and the nonstationary fractional Brownian Motion (fBm). [source]

Inducing normality from non-Gaussian long memory time series and its application to stock return data

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010
Kyungduk Ko
Abstract Motivated by Lee and Ko (Appl. Stochastic Models. Bus. Ind. 2007; 23:493,502) but not limited to the study, this paper proposes a wavelet-based Bayesian power transformation procedure through the well-known Box,Cox transformation to induce normality from non-Gaussian long memory processes. We consider power transformations of non-Gaussian long memory time series under the assumption of an unknown transformation parameter, a situation that arises commonly in practice, while most research has been devoted to non-linear transformations of Gaussian long memory time series with known transformation parameter. Specially, this study is mainly focused on the simultaneous estimation of the transformation parameter and long memory parameter. To this end, posterior estimations via Markov chain Monte Carlo methods are performed in the wavelet domain. Performances are assessed on a simulation study and a German stock return data set. Copyright © 2009 John Wiley & Sons, Ltd. [source]