Estimation of the fractionally integrated process with Missing Values: Simulation and Application
AbstractTime series with long-memory behavior have recently received much attention. Much interest attaches to parameter estimation in the ARFIMA model by considering different situations of this process, and specifically when there are missing observations. This is the focus of this paper. To estimate the parameters of the ARFIMA model, parametric and semiparametric approaches are considered. The way the missing values are distributed can affect the performance of these estimators. We consider two ways for the generating the missing observations: random and block. We also consider innovations that are not normally distributed. The results are obtained through Monte Carlo simulation and a real data set is used to illustrate the methodology
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 251.
Date of creation: 11 Aug 2004
Date of revision:
Long Memory; ARFIMA; Parametric and semi-parametric methods.;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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