• Sarimah Sarimah Department of Statistics, IPB University, Indonesia
  • Anik Djuraidah Department of Statistics, IPB University, Indonesia
  • Aji H Wigena Department of Statistics, IPB University, Indonesia
Keywords: bayesian model averaging, posterior probability, spatial autoregressive


Economic data always contains spatial effects. Gross Regional Domestic Product (GRDP) in Java is one of economic data that describes spatial dependence between adjacent districts/cities. The method that is suitable for modeling GDRP is spatial regression with spatial dependence on lags that is spatial autoregressive. GDRP prediction used the Bayesian Model Averaging (BMA) method. The ten autoregressive spatial model that have highest posterior probability was chosen to determined the BMA model by posterior probability. The explanatory variables used in this study were (1) mean years of schooling (2) life expectancy (3) income per capita (4) local revenue (5) number of workers (6) district minimum salary. The results showed that the number of workers was chosen as a predictor for the ten models. The model that have highest posterior probability probability is 0.54 which contains five explanatory variables that are mean years of schooling, income per capita, local revenue, number of workers and district minimum salary and the pseudo R2 of the model is 0.696.


Anselin, L. (1988). Spatial econometrics: methods and models. New York (US): Springer Science & Business Media.

Anselin, L. (1998). Spatial econometrics. Toledo (US): Departemen of Economics University of Toledo.

Bivand, R. S., Gómez-Rubio, V., & Rue, H. (2014). Approximate Bayesian inference for spatial econometrics models. Spatial Statistics, 9: 146–165.

[BPS] Badan Pusat Statistik. (2018). Produk domestik regional bruto kabupaten/kota di Indonesia 2013-2018. Jakarta (ID): Badan Pusat Statistik.

Glasson, J. (1997). Pengantar perencanaan regional. (penerjemah: Paul Sitohang). Jakarta (ID): Fakultas Ekonomi Universitas Indonesia.

Huang, J., Niu, C., Green, C. D., Yang, L., Mei, H., & Han, J.-D. J. (2013). Systematic prediction of pharmacodynamic drug-drug interactions through protein-protein-interaction network. PLoS Computational Biology, 9(3): e1002998.

LeSage, J. P. (1997). Bayesian estimation of spatial autoregressive models. International Regional Science Review, 20(1–2): 113–129.

Ord, K. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70(349): 120–126.

Putra, F. S. (2017). Analisis spasial produk domestik regional bruto (PDRB) kabupaten/kota di provinsi jawa tengah (Skripsi). Universitas Islam Indonesia, Yogyakarta (ID).

Rachmawati, R. N., Djuraidah, A., Fitrianto, A., & Sumertajaya, I. M. (2018). Spatio-temporal models using R-INLA with generalized extreme value distribution in hierarchical bayesian regression. IJRSET, 4(4): 2394–4099.

Zallner, A. (1971). An introduction to bayes inference in econometrics. New York (US): Wiley.