Asymptotic Properties of Quasi-Maximum Likelihood Estimators for Heterogeneous Spatial Autoregressive Models

Qiu, Feng; Ding, Hui-Sheng; Hu, Jianhua

doi:10.3390/sym14091894

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Therefore, the parameter estimation in this model followed the SAR parameter estimation method, employing the Maximum Likelihood Estimation (MLE) technique. The observed sources for this approach included Ord [36], Smirnov and Anselin [37], Robinson and Rossi [38], and Feng [39]. Equation ( 12) can be written as the following equation:…”

Section: Estimationmentioning

confidence: 99%

The Hybrid Modeling of Spatial Autoregressive Exogenous Using Casetti’s Model Approach for the Prediction of Rainfall

Falah,

Ruchjana,

Abdullah

et al. 2023

Mathematics

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Spatial Autoregressive (SAR) models are used to model the relationship between variables within a specific region or location, considering the influence of neighboring variables, and have received considerable attention in recent years. However, when the impact of exogenous variables becomes notably pronounced, an alternative approach is warranted. Spatial Expansion, coupled with the Casetti model approach, serves as an extension of the SAR model, accommodating the influence of these exogenous variables. This modeling technique finds application in the realm of rainfall prediction, where exogenous factors, such as air temperature, humidity, solar irradiation, wind speed, and surface pressure, play pivotal roles. Consequently, this research aimed to combine the SAR and Spatial Expansion models through the Casetti model approach, leading to the creation of the Spatial Autoregressive Exogenous (SAR-X) model. The SAR-X was employed to forecast the rainfall patterns in the West Java region, utilizing data obtained from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) dataset. The practical execution of this research capitalized on the computational capabilities of the RStudio software version 2022.12.0. Within the framework of this investigation, a comprehensive and integrated RStudio script, seamlessly incorporated into the RShiny web application, was developed so that it is easy to use.

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Section: Estimationmentioning

confidence: 99%

The Hybrid Modeling of Spatial Autoregressive Exogenous Using Casetti’s Model Approach for the Prediction of Rainfall

Falah,

Ruchjana,

Abdullah

et al. 2023

Mathematics

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Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Andriyana,

Falah,

Ruchjana

et al. 2024

Mathematics

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Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

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Asymptotic Properties of Quasi-Maximum Likelihood Estimators for Heterogeneous Spatial Autoregressive Models

Cited by 2 publications

References 41 publications

The Hybrid Modeling of Spatial Autoregressive Exogenous Using Casetti’s Model Approach for the Prediction of Rainfall

The Hybrid Modeling of Spatial Autoregressive Exogenous Using Casetti’s Model Approach for the Prediction of Rainfall

Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

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