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Long-Memory Processes and Exchange Rate Forecasting

  • Jonas Mockus
  • William Eddy
  • Audris Mockus
  • Linas Mockus
  • Gintaras Reklaitis
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 17)

Abstract

Modeling persistence in economic and financial time series using the autoregressive fractionally integrated moving average (ARFIMA) method has attracted the attention of many researchers in recent years [29, 16, 161, 17, 81, 103]. The frequency of the use of ARFIMA modeling in empirical research underscores the importance of efficient, both computational and statistical, estimation of the models. In estimating the parameters of the ARFIMA models, three approaches have been used: Maximum Likelihood(ML) [142], approximate ML [86, 46, 65, 66], and two-step procedures [51, 70]. Geweke and Porter-Hudak’s method [51], unlike the ML approach, is less computationally demanding but some analysts consider it to be inadequate for finite samples.

Keywords

Exchange Rate Random Walk Global Optimization Artificial Neural Network Model ARMA Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Jonas Mockus
    • 1
    • 2
    • 3
  • William Eddy
    • 4
  • Audris Mockus
    • 5
  • Linas Mockus
    • 6
  • Gintaras Reklaitis
    • 6
  1. 1.Institute of Mathematics and InformaticsKaunas Technological UniversityVilniusLithuania
  2. 2.Vytautas Magnus UniversityVilniusLithuania
  3. 3.Vilnius Technical UniversityVilniusLithuania
  4. 4.Department of StatisticsCarnegie-Mellon UniversityPittsburghUSA
  5. 5.Lucent Technologies AT&T Bell LaboratoriesPittsburghUSA
  6. 6.School of Chemical EngineeringPurdue UniversityW. LafayetteUSA

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