ArticleMethodsX2025
Spatial autoregressive quantile regression modeling of gross regional domestic product data in Java Island.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study analyzes the Gross Regional Domestic Product (GRDP) data of Java Island to understand the region's economic conditions and the key factors influencing GRDP. Java Island contributes the largest share to the national GRDP; however, the data show the presence of spatial dependence, spatial heterogeneity, and outliers. To address these issues, this study employs the Spatial Autoregressive Quantile Regression (SARQR) model. The findings show that the number of workers significantly influences GRDP across all quantiles, whereas Local Own-Source Revenue (LOSR), Regency/City Minimum Wage (RCMW), and the Human Development Index (HDI) only impact specific quantiles. The SARQR model demonstrates the best performance at the 0.6 quantile, with a Quantile Verification Skill Score (QVSS) of 0.9052, indicating strong explanatory power. • The model was estimated using the Instrumental Variable Quantile Regression (IVQR) method. • Variable significance at each quantile was tested using the Wald test. • The best model was selected based on the QVSS value.
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