Modeling Spatial Spillovers of Divorce in Senegal Using Spatial Durbin Model: A Maximum Likelihood Estimation Approach
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 1, January 2019, Pages: 1-6
Received: Dec. 16, 2018;
Accepted: Jan. 5, 2019;
Published: Jan. 24, 2019
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Alassane Aw, Laboratory of Mathematics and Applications, Assane Seck University of Ziguinchor, Ziguinchor, Senegal
Emmanuel Nicolas Cabral, Laboratory of Mathematics and Applications, Assane Seck University of Ziguinchor, Ziguinchor, Senegal
Spatial Durbin Model (SDM) is one of the family of spatial autoregressive models. In this paper, we use the SDM model to determine the spatial spillovers of divorce in Senegal. The variable of interest is the rate of divorce and the explanatory variables are the rate of illiteracy and the average age at marriage in Senegal. The model parameters are estimated by the maximum likelihood technique. The estimation of the autoregressive parameter is performed using numerical optimization of the concentrated log-likelihood of the SDM model. The results obtained showed that the rate of illiteracy and the average age at marriage have a real impact on the rate of divorce in Senegal. We also note that the departments of the country that are closed are more similar than the distant departments in relation to the divorce data. Direct and indirect effects were used to measure changes in the rate of divorce as a result of changes in the rate of illiteracy and the average age at marriage.
Emmanuel Nicolas Cabral,
Modeling Spatial Spillovers of Divorce in Senegal Using Spatial Durbin Model: A Maximum Likelihood Estimation Approach, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 1,
2019, pp. 1-6.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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LeSage, J. P., and Pace, R. K. (2017). Spatial econometrics Monte Carlo studies: raising the bar. Empirical Economics, 55(1), 17–34.
Doran, J. and Fingleton, B. (2018). US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation, Environment and Planning A: Economy and Space, 50(1), pp. 111-132.
Elhorst, J. P (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer Briefs in Regional Science.
LeSage, J. P. (2014). What Regional Scientists Need to Know about Spatial Econometrics. The Review of Regional Studies, 44(1).
Golgher, A. B., and Voss, P. R. (2016). How to interpret the coefficients of spatial models: Spillovers, direct and indirect effects. Spatial Demography, 4(3), 175-205.
Koroglu, M., and Sun, Y. (2016). Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth. Econometrics, 4(1), 6.
Feng, Z., & Chen, W. (2018). Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model. Sustainability, 10(1), 223.
Lee, L., and Yu, J. (2015). Identification of Spatial Durbin Panel Models. Journal of Applied Econometrics, 31(1), 133–162.
Bekti R. D., Rahayu A. and Sutikno (2013). Maximum likelihood estimation for spatial Durbin model. Journal of Mathematics and Statistics 9 (3): 169-174, 2013.
Fingleton B. and LeGallo J. (2012). Endogéneité et autocorrélation spatiale: quelle utilité pour le modèle de Durbin? Revue d’économie régionale et urbaine, 2012/1 (février), pp. 3-17.
Seya, H., Tsutsumi M. and Yamagata Y. (2012). Income convergence in Japan: A Bayesian spatial Durbin model approach. Econ. Model., 29: 60-71.
LeSage, J. P. and Pace R. K. (2009). Introduction to Spatial Econometrics. CRC Press Taylor & Francis Group, Boca Raton.
Anselin, L. (1988). Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Dorddrecht.
Andrienko N. and Andrienko G. (2006). Exploratory analysis of spatial and temporal data, a systematic approach. Springer-Verlag, Heidelberg, Germany.
LeSage, J. P. and Pace R. K. (2014). The biggest myth in spatial econometrics. Econometrics, 2, pp. 217-249.
Ord, J. K. (1975). Estimation Methods for Models of Spatial Interaction. Journal of the American Statistical Association, Volume 70, pp. 120-126.
Barry, R. and Pace R. K. (1999). A Monte Carlo Estimator of the Log Determinant of Large Sparse Matrices. Linear Algebra and its Applications, Volume 289, pp. 41-54.
Bivand R. S., Pebesma E., Gomez-Rubio V. (2013). Applied spatial data analysis with R, Second edition. Springer, NY.