Monte Carlo Simulation of The Multivariate Spatial Durbin Model for Complex Data Sets

Nur Atikah, Basuki Widodo, Mardlijah, Swasono Rahardjo, Sri Harini, Riski Nur Istiqomah Dinnullah

Research output: Contribution to journalArticlepeer-review

Abstract

The Multivariate Spatial Durbin Model (MSDM) is a significant advance in spatial econometrics, very relevant in the context of research problems. This model extends spatial analysis by capturing the complexity and dynamism of interactions between variables in a spatial context that is often ignored by classical spatial models. Furthermore, this article aims to estimate the parameters of MSDM model applied to large and complex data sets through Monte Carlo simulations. This model was then estimated using Maximum Likelihood Estimation (MLE), and to test the accuracy of the model using the Maximum Likelihood Ratio Test (MLRT) with a computational approach. The research results show that the MSDM model parameter estimates are accurate as indicated by an accuracy value that is smaller than the 5% significance level. The model becomes more efficient as the sample size increases.

Original languageEnglish
Pages (from-to)223-233
Number of pages11
JournalInternational Journal of Mathematics and Computer Science
Volume20
Issue number1
DOIs
Publication statusPublished - 2025

Keywords

  • Complex Data Sets
  • Maximum Likelihood Estimation
  • Maximum Likelihood Ratio Test
  • Monte Carlo Simulation
  • Multivariate Spatial Durbin Model

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