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Robust Simulation-Based Estimation of ARMA Models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Luna, Xavier De Genton, Marc G. |
| Copyright Year | 2001 |
| Abstract | This article proposes a new approach to the robust estimation of a mixed autoregressive and moving average (ARMA) model. It is based on the indirect inference method that originally was proposed for models with an intractable likelihood function. The estimation algorithm proposed is based on an auxiliary autoregressive representation whose parameters are first estimated on the observed time series and then on data simulated from the ARMA model. To simulate data the parameters of the ARMA model have to be set. By varying these we can minimize a distance between the simulation-based and the observation-based auxiliary estimate. The argument of the minimum yields then an estimator for the parameterization of the ARMA model. This simulation-based estimation procedure inherits the properties of the auxiliary model estimator. For instance, robustness is achieved with GM estimators. An essential feature of the introduced estimator, compared to existing robust estimators for ARMA models, is its theoretical tracta... |
| Starting Page | 370 |
| Ending Page | 387 |
| Page Count | 18 |
| File Format | PDF HTM / HTML |
| DOI | 10.1198/10618600152628347 |
| Volume Number | 10 |
| Alternate Webpage(s) | https://stsda.kaust.edu.sa/Documents/2001.dLG.JCGS.pdf |
| Alternate Webpage(s) | https://doi.org/10.1198/10618600152628347 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
