Abstract
In this chapter, we introduce the concept of fractional integration for spatial autoregressive models. We show that the range of the dependence can be spatially extended or diminished by introducing a further fractional integration parameter to spatial autoregressive moving average models (SARMA). This new model is called spatial autoregressive fractionally integrated moving average model, briefly spARFIMA. We show the relation to time-series ARFIMA models and also to (higher-order) spatial autoregressive models. Moreover, an estimation procedure based on the maximum-likelihood principle is introduced and analysed in a series of simulation studies. Eventually, the use of the model is illustrated by an empirical example of atmospheric fine particles, so-called aerosol optical thickness, which is important in weather, climate, and environmental science.
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Otto, P., Sibbertsen, P. (2024). Spatial Autoregressive Fractionally Integrated Moving Average Model. In: Knoth, S., Okhrin, Y., Otto, P. (eds) Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science. Springer, Cham. https://doi.org/10.1007/978-3-031-69111-9_22
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DOI: https://doi.org/10.1007/978-3-031-69111-9_22
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