Time Series Analysis of Long Memory versus Structural Breaks: A Time-Varying Memory Approach

 
9783639246018: Time Series Analysis of Long Memory versus Structural Breaks: A Time-Varying Memory Approach

Several real world processes exhibit a very slowly decaying dependence over time, e.g. river flow data, tree ring width data, or stock volatility. In the time series literature this phenomenon is known as long memory or long range dependence. An alternative view are structural breaks occurring over time that make the process appear to have long memory, but in fact it does not. This work gives a brief introduction to univariate time series analysis and then studies the long memory versus structural breaks debate. A detailed study of an error duration model gives a nice view of stochastic processes in general and sheds new light on the aforementioned controversy. After presenting various estimators and tests for long range dependence, a chapter with applications compares short and long memory models for financial data. The contribution of this work is a model for time-varying (long) memory and herewith tries to unify the concurring views of long memory and structural breaks. This book is intended for readers interested in applied math and statistics, in particular time series analysis.

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Georg M Goerg
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Book Description Book Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | A Time-Varying Memory Approach | Several real world processes exhibit a very slowly decaying dependence over time, e.g. river flow data, tree ring width data, or stock volatility. In the time series literature this phenomenon is known as long memory or long range dependence. An alternative view are structural breaks occurring over time that make the process appear to have long memory, but in fact it does not. This work gives a brief introduction to univariate time series analysis and then studies the long memory versus structural breaks debate. A detailed study of an error duration model gives a nice view of stochastic processes in general and sheds new light on the aforementioned controversy. After presenting various estimators and tests for long range dependence, a chapter with applications compares short and long memory models for financial data. The contribution of this work is a model for time-varying (long) memory and herewith tries to unify the concurring views of long memory and structural breaks. This book is intended for readers interested in applied math and statistics, in particular time series analysis. | Format: Paperback | Language/Sprache: english | 175 gr | 220x150x6 mm | 120 pp. Bookseller Inventory # K9783639246018

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Book Description VDM Verlag Mrz 2010, 2010. Taschenbuch. Book Condition: Neu. Neuware - Several real world processes exhibit a very slowly decaying dependence over time, e.g. river flow data, tree ring width data, or stock volatility. In the time series literature this phenomenon is known as long memory or long range dependence. An alternative view are structural breaks occurring over time that make the process appear to have long memory, but in fact it does not. This work gives a brief introduction to univariate time series analysis and then studies the long memory versus structural breaks debate. A detailed study of an error duration model gives a nice view of stochastic processes in general and sheds new light on the aforementioned controversy. After presenting various estimators and tests for long range dependence, a chapter with applications compares short and long memory models for financial data. The contribution of this work is a model for time-varying (long) memory and herewith tries to unify the concurring views of long memory and structural breaks. This book is intended for readers interested in applied math and statistics, in particular time series analysis. 120 pp. Englisch. Bookseller Inventory # 9783639246018

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Book Description VDM Verlag Mrz 2010, 2010. Taschenbuch. Book Condition: Neu. Neuware - Several real world processes exhibit a very slowly decaying dependence over time, e.g. river flow data, tree ring width data, or stock volatility. In the time series literature this phenomenon is known as long memory or long range dependence. An alternative view are structural breaks occurring over time that make the process appear to have long memory, but in fact it does not. This work gives a brief introduction to univariate time series analysis and then studies the long memory versus structural breaks debate. A detailed study of an error duration model gives a nice view of stochastic processes in general and sheds new light on the aforementioned controversy. After presenting various estimators and tests for long range dependence, a chapter with applications compares short and long memory models for financial data. The contribution of this work is a model for time-varying (long) memory and herewith tries to unify the concurring views of long memory and structural breaks. This book is intended for readers interested in applied math and statistics, in particular time series analysis. 120 pp. Englisch. Bookseller Inventory # 9783639246018

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Book Description VDM Verlag Dr. Müller, 2010. Paperback. Book Condition: New. This item is printed on demand. Bookseller Inventory # INGM9783639246018

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Book Description VDM Verlag, Germany, 2010. Paperback. Book Condition: New. Language: English . Brand New Book. Several real world processes exhibit a very slowly decaying dependence over time, e.g. river flow data, tree ring width data, or stock volatility. In the time series literature this phenomenon is known as long memory or long range dependence. An alternative view are structural breaks occurring over time that make the process appear to have long memory, but in fact it does not. This work gives a brief introduction to univariate time series analysis and then studies the long memory versus structural breaks debate. A detailed study of an error duration model gives a nice view of stochastic processes in general and sheds new light on the aforementioned controversy. After presenting various estimators and tests for long range dependence, a chapter with applications compares short and long memory models for financial data. The contribution of this work is a model for time-varying (long) memory and herewith tries to unify the concurring views of long memory and structural breaks. This book is intended for readers interested in applied math and statistics, in particular time series analysis. Bookseller Inventory # KNV9783639246018

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