Time Series with Long Memory: (Advanced Texts in Econometrics)

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:
Peter M. Robinson: Introduction 1: P. M. Robinson: Long Memory Time Series 2: R. K. Adenstedt: On Large-Sample Estimation of the Mean of a Stationary Random Sequence 3: C. W. J. Granger and R. Joyeux: Long Memory Relationships and the Aggregation of Dynamic Models 4: R. Fox and M. S. Taqqu: Large Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series 5: A. W. Lo: Long-Term Memory in Stock Market Prices 6: J. Geweke and S. Porter-Hudak: The Estimation and Application of Long-Memory Time Series Models 7: P. M. Robinson: Gaussian Semiparametric Estimation of Long-Range Dependence 8: P. M. Robinson: Testing for Strong Serial Correlation and Dynamic Conditional Heteroskedasticity in Multiple Regression 9: F. J. Breidt, N. Crato, and P. de Lima: On the Detection and Estimation of Long Memory in Stochastic Volatility 10: P. M. Robinson: Efficient Tests of Nonstationary Hypotheses 11: C. M. Hurvich and B. K. Ray: Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes 12: F. Eicker: Limit Theorems for Regression with Unequal and Dependent Errors 13: P. M. Robinson and J. F. Hidalgo: Time Series Regression with Long Range Dependence 14: P. M. Robinson and D. Marinucci: Semiparametric Frequency-Domain Analysis of Fractional Cointegration

About the Author :
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 the Institute of Mathematical Statistics, and Fellow of the Econometric Society.