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 INDICE 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