Time Series with Long Memory

Front Cover
Peter M. Robinson
Oxford University Press, 2003 - Business & Economics - 382 pages
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 indexesthis 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 describesboth 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 highlytheoretical, 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.
 

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Contents

Introduction
1
References
25
On Largesample Estimation for the Mean
33
An Introduction to Longmemory Time Series Models
49
1 The d 0 Case
63
Longterm Memory in Stock Market Prices
82
Notes
111
The Estimation and Application of Longmemory Time
119
1 Proof of Strong Consistency for Spectrallikelihood
209
1 Derivation of Score Statistic R
238
4 Proof of Theorem 3
245
Estimation of the Memory Parameter for Nonstationary
251
1
269
References
276
References
304
References
332

References
136
References
174
Testing for Strong Serial Correlation and Dynamic Conditional
175
References
189
1
361
References
371
Copyright

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About the author (2003)

Peter M. Robinson is Tooke Professor of Economic Science and Statistics, and Leverhulme Research Professor at the London School of Economics. He was previously Professor of Econometrics at the same institution. He has served as Co-Editor of Econometrica and the Journal of Econometrics and Econometric Theory, and as Associate Editor of The Annals of Statistics and other journals. He is a Fellow of the British Academy, Fellow of theInstitute of Mathematical Statistics, and Fellow of the Econometric Society.

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