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.

• 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

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.

• 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