Spatial Dependence in Regressors and its Effect on Estimator Performance

2011 ◽  
Author(s):  
R. Kelley Pace ◽  
James P. LeSage ◽  
Shuang Zhu
Keyword(s):  
Spatial Dependence

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

Spatial Dependence in Sovereign Wealth Funds' Investments

2016 ◽  
Author(s):  
Nicolas Debarsy ◽  
Jean-Yves Gnabo ◽  
Malik Kerkour
Keyword(s):  
Spatial Dependence ◽  
Sovereign Wealth Funds

Spatial Dependence and Space-Time Trends in Extreme Events

2020 ◽  
Author(s):  
John H. J. Einmahl ◽  
Ana Ferreira ◽  
Laurens de Haan ◽  
Claudia Neves ◽  
Chen Zhou
Keyword(s):  
Extreme Events ◽  
Spatial Dependence ◽  
Time Trends ◽  
Space Time

scholarly journals Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 282
Author(s):  
Mabel Morales-Otero ◽  
Vicente Núñez-Antón
Keyword(s):  
Infant Mortality ◽  
Count Data ◽  
Spatial Dependence ◽  
Poisson Model ◽  
Mortality Rates ◽  
Estimation Methods ◽  
Neighborhood Structure ◽  
Specific Context ◽  
Count Data Models ◽  
Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

scholarly journals Spatial Dependence Evaluation of Agricultural Technical Efficiency—Based on the Stochastic Frontier and Spatial Econometric Model

2021 ◽  
Vol 13 (5) ◽  
pp. 2708
Author(s):  
Ziqi Yin ◽  
Jianzhai Wu
Keyword(s):  
Spatial Distribution ◽  
Technical Efficiency ◽  
Spatial Dependence ◽  
Stochastic Frontier ◽  
Hebei Province ◽  
Agricultural Technology ◽  
Agricultural Product ◽  
Spatial Econometric ◽  
Frontier Analysis ◽  
The Impact

In recent years, through the implementation of a series of policies, such as the delimitation of major grain producing areas and the construction of advantageous and characteristic agricultural product areas, the spatial distribution of agriculture in China has changed significantly; however, research on the impact of such changes on the efficiency of agricultural technology is still lacking. Taking 11 cities in Hebei Province as the research object, this study examines the spatial dependence of regional agricultural technical efficiency using the stochastic frontier analysis and spatial econometric analysis. The results show that the improvement in agricultural technical efficiency is evident in all cities in Hebei Province from 2008 to 2017, but there is scope for further improvement. Industrial agglomeration has statistical significance in improving the efficiency of agricultural technology. Further, there is an obvious spatial correlation and difference in agricultural technical efficiency. Optimizing the spatial distribution of agricultural production, promoting the innovation, development, and application of agricultural technology, and promoting the expansion of regional elements can contribute to improving agricultural technical efficiency.

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