TY - JOUR
T1 - Monte Carlo Simulation of The Multivariate Spatial Durbin Model for Complex Data Sets
AU - Atikah, Nur
AU - Widodo, Basuki
AU - Mardlijah,
AU - Rahardjo, Swasono
AU - Harini, Sri
AU - Dinnullah, Riski Nur Istiqomah
N1 - Publisher Copyright:
© (2025), (International Journal of Mathematics and Computer Science). All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Complex Data Sets
KW - Maximum Likelihood Estimation
KW - Maximum Likelihood Ratio Test
KW - Monte Carlo Simulation
KW - Multivariate Spatial Durbin Model
UR - http://www.scopus.com/inward/record.url?scp=85205017451&partnerID=8YFLogxK
U2 - 10.69793/ijmcs/01.2025/widodo
DO - 10.69793/ijmcs/01.2025/widodo
M3 - Article
AN - SCOPUS:85205017451
SN - 1814-0424
VL - 20
SP - 223
EP - 233
JO - International Journal of Mathematics and Computer Science
JF - International Journal of Mathematics and Computer Science
IS - 1
ER -