Spatial Econometrics Based on Kronecker Multiplication
Keywords:
Spatial econometrics, Kronecker Multiplication, adjacency, spatial dependency, spatial heterogeneityAbstract
Objectives: Countries and cities, as places of residence, are endowed with various dimensions, including economic and social ones. The interactions between these components manifest in flows that affect the quality of the human-made environment on one hand and the quality of human life on the other. Prior Work: One of the new methods for achieving higher accuracy in analyzing space-affected relationships is spatially weighted regression, introduced by Anselin and Griffith (1988). In his book "Spatial Econometrics: Methods and Models," he presented a comprehensive picture of spatial econometric realities for the first time, claiming this technique had better capabilities and applications than conventional econometrics in regional and spatial studies. Approach: The current research is mainly done by a review method. Results: The result of this article is the introduction of spatial econometrics based on Kronecker's multiplication, which is used in many researches, especially researches related to sustainable development. Implications: This article aims to familiarize the reader with spatial econometrics and some features of the Kronecker product, as well as some of its applications in the context of spatial dependency, spatial heterogeneity, the nature of adjacency in spatial econometrics, spatial positioning, spatial lags, autoregressive models, mixed regression models, spatial autoregression, and the Kronecker product, discussed briefly in this paper. Value: As far as we know, this article is one of the first articles that introduced, described and explained the spatial econometric method based on Kronecker's multiplication, so it is innovative.
References
Afroogh, Emad (1998). Space and Social Inequality, A Model for Spatial Segregation and its Consequence. Tehran: Tarbiat
Modares University.
Afshari, Arjomand, Hamideh & Golpar, Raboki, Hamideh (2018). Kronecker Product and its Applications. Scientific-
Promotional Journal of Mathematics and Society. University of Isfahan, pp. 55-67.
Akbari, Nematollah (2001), Spatial Analysis of Social Demand for Higher Education in Iran, PhD Thesis, Tehran, Tarbiat
Modares University, pp. 1-186.
Anselin, L. & Griffith, D. A. (1988). Do spatial effecfs really matter in regression analysis? Papers in Regional Science, 65(1),
pp. 11-34.
Asgari, Ali & Akbari, Nematollah (2001). Methodology of Spatial Econometrics, Theory and Applications. Human Sciences
Research Journal. University of Isfahan, pp. 93-112.
Brunsdon, C.; Fotheringham, A. S. & Charlton, M. E. (2002). Geographically weighted regression: a method for exploring
spatial nonstationarity. Geographical Analysis, 28(4), pp. 281-298.
Lesage, J. (1999). Spatial Econometrics. Department of Economics University of Toledo.
Samadi, Ali, Hossein, Dehghani, Shabani, Zahra & Moradi, Koochaki, Atefeh (2015), Spatial Analysis of the Impact of Income
Distribution Inequality on Economic Growth in Iran. Scientific Research Quarterly of Growth and Development Research, pp.
-72.
Soltani, Ali; Dehghani Shabani, Zahra & Moradi, Koochaki, Atefeh (2014). Application of Spatial Regression Model in
Examining the Relationships between Spatial Variables in an Urban Area. Case Study: District 7 of Tehran Municipality, pp.
-110.
Stankovic, R. S. & Falkowski, B. J. (2003). The Haar wavelet transform: its status and achievements. Computers & Electrical
Engineering, 29(1), pp. 25-44.
Steeb, W. H. (2008). Nonlinear Workbook, The: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms,
Fuzzy Logic With C++, Java, Symbolicc++ And Reduce Programs. World Scientific Publishing Company.
Zhu, C.; Su, Y.; Fan, R.; Qin, M. & Fu, H. (2024). Exploring provincial carbon-pollutant emission efficiency in China: An
integrated approach with social network analysis and spatial econometrics. Ecological Indicators, 159, p. 111662.
Ziari, Hossein & Shafiei-Kakhki, Maryam (2016). Investigating the Spillover Effects of Investment in Innovation on the
Exports of Industrial Firms with a Regional Economics Approach, Application of Spatial Regression Model. Master’s Thesis.
Iran, Babol: Islamic Azad University, pp. 1-53.
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