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Indirect inference for spatio-temporal autoregression models.
| Content Provider | CiteSeerX |
|---|---|
| Author | Luna, Xavier De Genton, Marc G. |
| Abstract | Introduction In this note we introduce a new inferential method for STAR (spatio-temporal autoregression) models. Due to the complexity of such models the maximum likelihood estimation is difficult to undertake when several nearest neighbours are included in the model, see Ali (1979). Moreover, only approximate likelihoods are available in practice because of the observations lying on the edges of the spatial domain. On the other hand, simpler estimation methods such as least squares and Yule-Walker are not generally consistent. With this background, we propose the use of an inferential method based on an auxiliary model whose parameters can be consistently estimated with Yule-Walker. From this estimation, consistent estimators for the parameters of the STAR model of interest are then retrieved with the help of simulated data. The method and its asymptotic theory are presented in Section 2. In Section 3 we illustrate its small sample properties with a limited Monte Carlo experime |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Indirect Inference Spatio-temporal Autoregression Model Inferential Method Approximate Likelihood Spatio-temporal Autoregression New Inferential Method Simpler Estimation Method Maximum Likelihood Estimation Spatial Domain Auxiliary Model Small Sample Property Consistent Estimator Limited Monte Carlo Experime Simulated Data Star Model Asymptotic Theory |
| Content Type | Text |
