Time Series with Long Memory
Series: Advanced Texts in Econometrics;
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Product details:
- Publisher OUP Oxford
- Date of Publication 26 June 2003
- ISBN 9780199257300
- Binding Paperback
- No. of pages392 pages
- Size 234x157x21 mm
- Weight 560 g
- Language English
- Illustrations numerous tables and figures 0
Categories
Short description:
This volume provides in a convenient format for students and researchers the core papers in long memory time series analysis. Long memory time series are characterized by a strong dependence between distant events. Various methods and their theoretical properties are discussed, with empirical applications. The methods constitute a very flexible approach to analysing time series data arising in economics, finance, and other fields.
MoreLong description:
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 processes, and regression and fractional cointegration models. Some of the articles are highly theoretical, others contain a mix of theory and methods, and an effort has been made to include empirical applications of the main approaches covered. A review article introduces the other articles but also attempts a broader survey, traces the history of the subject, and includes a bibliography.
Table of Contents:
Introduction
Long Memory Time Series
On Large-Sample Estimation of the Mean of a Stationary Random Sequence
Long Memory Relationships and the Aggregation of Dynamic Models
Large Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series
Long-Term Memory in Stock Market Prices
The Estimation and Application of Long-Memory Time Series Models
Gaussian Semiparametric Estimation of Long-Range Dependence
Testing for Strong Serial Correlation and Dynamic Conditional Heteroskedasticity in Multiple Regression
On the Detection and Estimation of Long Memory in Stochastic Volatility
Efficient Tests of Nonstationary Hypotheses
Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes
Limit Theorems for Regression with Unequal and Dependent Errors
Time Series Regression with Long Range Dependence
Semiparametric Frequency-Domain Analysis of Fractional Cointegration
