Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach

Authors

  • Wigbertus Ngabu Brawijaya University
  • Henny Pramoedyo Brawijaya University
  • Rahma Fitriani Brawijaya University
  • Ani Budi Astuti Brawijaya University

DOI:

https://doi.org/10.21512/comtech.v14i1.8033

Keywords:

spatial modeling, fixed effect, random effect, Fast Double Bootstrap (FDB)

Abstract

The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.

Dimensions

Plum Analytics

Author Biographies

Wigbertus Ngabu, Brawijaya University

Brawijaya University

Henny Pramoedyo, Brawijaya University

Department of Statistics, Faculty of Mathematics and Natural Sciences

Rahma Fitriani, Brawijaya University

Department of Statistics, Faculty of Mathematics and Natural Sciences

Ani Budi Astuti, Brawijaya University

Department of Statistics, Faculty of Mathematics and Natural Sciences

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Published

2023-05-08

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