Quasi-Maximum Likelihood Estimation for a Class of Continuous-Time Long-Memory Processes PDF Download
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Author: Henghsiu Tsai Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Tsai and Chan (2003) has recently introduced the Continuous-time Auto-Regressive Fractionally Integrated Moving-Average (CARFIMA) models useful for studying long-memory data. We consider the estimation of the CARFIMA models with discrete-time data by maximizing the Whittle likelihood. We show that the quasi-maximum likelihood estimator is asymptotically normal and efficient. Finite-sample properties of the quasi-maximum likelihood estimator and those of the exact maximum likelihood estimator are compared by simulations. Simulations suggest that for finite samples, the quasi-maximum likelihood estimator of the Hurst parameter is less biased but more variable than the exact maximum likelihood estimator. We illustrate the method with a real application.
Author: Henghsiu Tsai Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Tsai and Chan (2003) has recently introduced the Continuous-time Auto-Regressive Fractionally Integrated Moving-Average (CARFIMA) models useful for studying long-memory data. We consider the estimation of the CARFIMA models with discrete-time data by maximizing the Whittle likelihood. We show that the quasi-maximum likelihood estimator is asymptotically normal and efficient. Finite-sample properties of the quasi-maximum likelihood estimator and those of the exact maximum likelihood estimator are compared by simulations. Simulations suggest that for finite samples, the quasi-maximum likelihood estimator of the Hurst parameter is less biased but more variable than the exact maximum likelihood estimator. We illustrate the method with a real application.
Author: Jan Beran Publisher: Springer Science & Business Media ISBN: 3642355129 Category : Mathematics Languages : en Pages : 892
Book Description
Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.
Author: Jaya P. N. Bishwal Publisher: Springer ISBN: 3540744487 Category : Mathematics Languages : en Pages : 271
Book Description
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Author: Christopher C. Heyde Publisher: Springer Science & Business Media ISBN: 0387226796 Category : Mathematics Languages : en Pages : 236
Book Description
The first account in book form of all the essential features of the quasi-likelihood methodology, stressing its value as a general purpose inferential tool. The treatment is rather informal, emphasizing essential principles rather than detailed proofs, and readers are assumed to have a firm grounding in probability and statistics at the graduate level. Many examples of the use of the methods in both classical statistical and stochastic process contexts are provided.
Author: Henghsiu Tsai
Author: Henghsiu Tsai
Author: Jan Beran
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Author: Jeffrey Pai
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Author: Jaya P. N. Bishwal
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Author: Daniel Straumann
Author: Christopher C. Heyde
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