Description

Book Synopsis
Long memory processes constitute a broad class of models for stationary and nonstationary time series data in economics, finance, and other fields. Their key feature is persistence, with high correlation between events that are remote in time. A single ''memory'' parameter economically indexes this persistence, as part of a rich parametric or nonparametric structure for the process. Unit root processes can be covered, along with processes that are stationary but with stronger persistence than autoregressive moving averages, these latter being included in a broader class which describes both short memory and negative memory. Long memory processes have in recent years attracted considerable interest from both theoretical and empirical researchers in time series and econometrics.This book of readings collects articles on a variety of topics in long memory time series including modelling and statistical inference for stationary processes, stochastic volatility models, nonstationary process

Table of Contents
Introduction ; 1. Long Memory Time Series ; 2. On Large-Sample Estimation of the Mean of a Stationary Random Sequence ; 3. Long Memory Relationships and the Aggregation of Dynamic Models ; 4. Large Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series ; 5. Long-Term Memory in Stock Market Prices ; 6. The Estimation and Application of Long-Memory Time Series Models ; 7. Gaussian Semiparametric Estimation of Long-Range Dependence ; 8. Testing for Strong Serial Correlation and Dynamic Conditional Heteroskedasticity in Multiple Regression ; 9. On the Detection and Estimation of Long Memory in Stochastic Volatility ; 10. Efficient Tests of Nonstationary Hypotheses ; 11. Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes ; 12. Limit Theorems for Regression with Unequal and Dependent Errors ; 13. Time Series Regression with Long Range Dependence ; 14. Semiparametric Frequency-Domain Analysis of Fractional Cointegration

Time Series With Long Memory Advanced Texts In Econometrics

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    A Paperback by Peter M. Robinson

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      View other formats and editions of Time Series With Long Memory Advanced Texts In Econometrics by Peter M. Robinson

      Publisher: Oxford University Press
      Publication Date: 6/26/2003 12:00:00 AM
      ISBN13: 9780199257300, 978-0199257300
      ISBN10: 0199257302

      Description

      Book Synopsis
      Long memory processes constitute a broad class of models for stationary and nonstationary time series data in economics, finance, and other fields. Their key feature is persistence, with high correlation between events that are remote in time. A single ''memory'' parameter economically indexes this persistence, as part of a rich parametric or nonparametric structure for the process. Unit root processes can be covered, along with processes that are stationary but with stronger persistence than autoregressive moving averages, these latter being included in a broader class which describes both short memory and negative memory. Long memory processes have in recent years attracted considerable interest from both theoretical and empirical researchers in time series and econometrics.This book of readings collects articles on a variety of topics in long memory time series including modelling and statistical inference for stationary processes, stochastic volatility models, nonstationary process

      Table of Contents
      Introduction ; 1. Long Memory Time Series ; 2. On Large-Sample Estimation of the Mean of a Stationary Random Sequence ; 3. Long Memory Relationships and the Aggregation of Dynamic Models ; 4. Large Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series ; 5. Long-Term Memory in Stock Market Prices ; 6. The Estimation and Application of Long-Memory Time Series Models ; 7. Gaussian Semiparametric Estimation of Long-Range Dependence ; 8. Testing for Strong Serial Correlation and Dynamic Conditional Heteroskedasticity in Multiple Regression ; 9. On the Detection and Estimation of Long Memory in Stochastic Volatility ; 10. Efficient Tests of Nonstationary Hypotheses ; 11. Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes ; 12. Limit Theorems for Regression with Unequal and Dependent Errors ; 13. Time Series Regression with Long Range Dependence ; 14. Semiparametric Frequency-Domain Analysis of Fractional Cointegration

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