Semiparametric Estimation of Long-memory Models

This article revises semiparametric methods of inference on different aspects of long memory time series. The main focus is on estimation of the memory parameter of linear models, analyzing bandwidth choice, bias reduction techniques and robustness properties of different estimates, with some emphasis on nonstationarity and trending behaviors. These techniques extend naturally to multivariate series, where the important issues are the estimation of the long run relationship and testing of fractional cointegration. Specific techniques for the estimation of the degree of persistence of volatility for nonlinear time series are also considered.