scholarly journals Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 466
Author(s):  
Maryna Makeienko
Keyword(s):  
Spatial Dependence ◽  
Housing Prices ◽  
Spatial Models ◽  
Unit Roots ◽  
Symbolic Analysis ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Economic Relations ◽  
Modeling Strategy ◽  
Space Dependence

This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia, and Ruiz (2019), which does not rely on prior weight matrices assumptions. It is shown that the use of geographically restricted semiparametric spatial models is a promising modeling strategy for cross-sectional datasets that are compatible with some types of spatial dependence. The results state that models that merely incorporate space coordinates might be sufficient to capture space dependence. Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies.

scholarly journals Bias from network misspecification under spatial dependence

2019 ◽  
Author(s):  
Timm Betz ◽  
Scott J Cook ◽  
Florian M Hollenbach
Keyword(s):  
Spatial Analysis ◽  
Spatial Models ◽  
Limited Information ◽  
Network Ties ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Limit Condition ◽  
Spatial Weights Matrix ◽  
Cross Sectional Dependence ◽  
Variable Bias

The pre-specification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in non-spatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with non-differential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate non-spatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

Beyond Policy Diffusion: Spatial Econometric Models of Public Administration

2018 ◽  
Vol 29 (4) ◽  
pp. 591-608 ◽  
Author(s):  
Scott J Cook ◽  
Seung-Ho An ◽  
Nathan Favero
Keyword(s):  
Public Administration ◽  
Spatial Models ◽  
State Level ◽  
Cross Sectional Study ◽  
Econometric Models ◽  
Education Spending ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Econometric Methods ◽  
Spatial Econometric Models

Abstract Interdependence in the decision-making or behaviors of various organizations and administrators is often neglected in the study of public administration. Failing to account for such interdependence risks an incomplete understanding of the choices made by these actors and agencies. As such, we show how researchers analyzing cross-sectional or time-series-cross-sectional (TSCS) data can utilize spatial econometric methods to improve inference on existing questions and, more interestingly, engage a new set of theoretical questions. Specifically, we articulate several general mechanisms for spatial dependence that are likely to appear in research on public administration (isomorphism, competition, benchmarking, and common exposure). We then demonstrate how these mechanisms can be tested using spatial econometric models in two applications: first, a cross-sectional study of district-level bilingual education spending and, second, a TSCS analysis on state-level healthcare administration. In our presentation, we also briefly discuss many of the practical challenges confronted in estimating spatial models (e.g., weights specification, model selection, effects calculation) and offer some guidance on each.

scholarly journals Bias from Network Misspecification Under Spatial Dependence

2020 ◽  
pp. 1-7
Author(s):  
Timm Betz ◽  
Scott J. Cook ◽  
Florian M. Hollenbach
Keyword(s):  
Spatial Analysis ◽  
Spatial Models ◽  
Limited Information ◽  
Network Ties ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Limit Condition ◽  
Spatial Weights Matrix ◽  
Cross Sectional Dependence ◽  
Variable Bias

Abstract The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with nondifferential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate nonspatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

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.

Determinants of housing demand in the Erzurum province, Turkey

2014 ◽  
Vol 7 (4) ◽  
pp. 586-602 ◽  
Author(s):  
Erkan Oktay ◽  
Abdulkerim Karaaslan ◽  
Ömer Alkan ◽  
Ali Kemal Çelik
Keyword(s):  
Housing Prices ◽  
Binary Logistic Regression ◽  
Housing Demand ◽  
Outcome Variable ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Content Type ◽  
Comprehensive Survey ◽  
Income Expectations

Purpose – The main aim of this study is to determine the factors that influence the housing demand of households in Erzurum, northeastern Turkey. Housing demand is generally affected by several factors including housing prices, individuals’ income, expectations and choices and so on, as a means of its demographic and socio-psychological contexts. Design/methodology/approach – A questionnaire-based cross-sectional survey was carried out, in which the outcome variable had binary responses such as whether to invest in housing or not. A binary logistic regression analysis was performed to estimate the underlying data. Findings – The questionnaire was conducted in 2,927 households living in Erzurum city center, and 47 per cent of the respondents claimed that they would consider investing in housing in the future. The estimation results reveal that demographic or socio-economic factors that may possibly influence housing demand of the respondents are as follows: household head’s and spouse’s occupation, monthly income, the number of individuals in the family and car ownership. Originality/value – This paper involves the most comprehensive survey addressing the housing demand in the East Anatolian Region, Turkey. Additionally, this paper aims to contribute to the existing housing literature through establishing the statistical analysis of housing demand in an unstudied territory of the world.

scholarly journals A Review of Software for Spatial Econometrics in R

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1276
Author(s):  
Roger Bivand ◽  
Giovanni Millo ◽  
Gianfranco Piras
Keyword(s):  
Maximum Likelihood ◽  
Spatial Econometrics ◽  
Historical Perspective ◽  
Statistical Computing ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Panel Models ◽  
Econometric Software ◽  
Generalized Moments

The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in the examples. Estimators and tests for spatial cross-sectional and panel models based either on maximum likelihood or on generalized moments methods are presented. The paper is concluded reviewing some current active lines of research in spatial econometric software methods.

scholarly journals SPATIAL CONVERGENCE OF THE GROSS VALUE OF PRODUCTION OF FIREWOOD IN THE MESOREGIONS OF THE BRAZILIAN NORTHEAST

2018 ◽  
Vol 42 (2) ◽  
Author(s):  
Luiz Moreira Coelho Junior ◽  
Kalyne de Lourdes da Costa Martins ◽  
Magno Vamberto Batista da Silva
Keyword(s):  
Spatial Dependence ◽  
Classical Model ◽  
Spatial Models ◽  
Information Criterion ◽  
Spatial Error ◽  
Spatial Convergence ◽  
Spatial Autoregressive ◽  
Classical Linear Regression ◽  
Object Of Study ◽  
Brazilian Northeast

ABSTRACT This paper analyzed the process of convergence in the gross value of wood production in mesoregions of Northeast Brazil, in the period of 1994 and 2013. The object of study was the Gross Value of Production (GVP) of firewood per km2 of the mesoregions of the Northeast of Brazil. In the methodology the Absolute Convergence Model was applied and estimated through the classical model and spatial models. In the spatial approach we used the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). From the results obtained, the following conclusions were reached: The mesoregions of the Northeast of Brazil had an average fall of 3.94% a.a. of the GVP/km2 of native wood for the period 1994 to 2013. Considering the classical linear regression model, convergence was verified and also the presence of spatial dependence for GVP/km2 of firewood. In order to correct the spatial dependence, the SAR and SEM Models were adequate and according to Akaike's Information Criterion and used the rook matrix the SEM was configured the best model. This study showed the importance of the involvement of the spatial question in the models, either by the overlap of information of the GVP and in the development of public policies that positively affect the neighborhood.

scholarly journals Species richness and diversity in shrub savanna using ordinary kriging

2016 ◽  
Vol 51 (8) ◽  
pp. 958-966 ◽  
Author(s):  
Anderson Pedro Bernardina Batista ◽  
José Márcio de Mello ◽  
Marcel Régis Raimundo ◽  
Henrique Ferraço Scolforo ◽  
Aliny Aparecida dos Reis ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Species Richness ◽  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Diversity Index ◽  
Spatial Models ◽  
Shannon Diversity Index ◽  
Shannon Diversity ◽  
Species Richness And Diversity ◽  
Number Of Species

Abstract: The objective of this work was to analyze the spatial distribution and the behavior of species richness and diversity in a shrub savanna fragment, in 2003 and 2014, using ordinary kriging, in the state of Minas Gerais, Brazil. In both evaluation years, the measurements were performed in a fragment with 236.85 hectares, in which individual trees were measured and identified across 40 plots (1,000 m2). Species richness was determined by the total number of species in each plot, and diversity by the Shannon diversity index. For the variogram study, spatial models were fitted and selected. Then, ordinary kriging was applied and the spatial distribution of the assessed variables was described. A strong spatial dependence was observed between species richness and diversity by the Shannon diversity index (<25% spatial dependence degree). Areas of low and high species diversity and richness were found in the shrub savanna fragment. Spatial distribution behavior shows relative stability regarding the number of species and the Shannon diversity index in the evaluated years.

scholarly journals SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1

Irriga ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 1-15
Author(s):  
Iug Lopes ◽  
Abelardo A. A. Montenegro
Keyword(s):  
Electrical Conductivity ◽  
Soil Moisture ◽  
Precision Agriculture ◽  
Spatial Dependence ◽  
Spatial Information ◽  
Gravimetric Method ◽  
Research Unit ◽  
Soil Electrical Conductivity ◽  
Agricultural Engineering ◽  
Space Dependence

SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1     IUG LOPES2 E ABELARDO ANTONIO DE ASSUNÇÃO MONTENEGRO3   1Paper extracted from the doctoral thesis of the first author. 2Department of Agronomy, Instituto Federal de Educação, Ciência e Tecnologia Baiano, BR 349, Km 14 - Zona Rural, CEP: 47600-000, Bom Jesus da Lapa - BA, Brazil; [email protected] - ORCID: 0000-0003-0592-4774. 3Department of Agricultural Engineering, Universidade Federal Rural de Pernambuco, Rua Dom Manoel de Medeiros, Dois Irmão, CEP: 52171-900, Recife - PE, Brazil; [email protected] -ORCID: 0000-0002-5746-8574.     1 ABSTRACT   Spatial information on soil characteristics is essential to proper decision-making regarding to the environment and land use management. The objective of this work was the investigation of cross - variance between soil moisture and apparent soil electrical conductivity (CEa), under different land uses in an alluvial valley of Pernambuco. The study was developed at the Advanced Research Unit of Universidade Federal Rural de Pernambuco (UFRPE), located at  Brígida River Basin, municipality of Panamirim-PE. Soil samples were collected in a regular mesh of 20 x 10 m, for soil moisture by gravimetric method and, following a regular 10 x 10 m mesh, CEa measurements were performed using EM38® device. Cross-semivariograms were assessed and spatial dependence was verified by geostatistical procedures. It was verified in geostatistical procedures  low variation for soil moisture and intermediate variation for CEa. The use of geostatistics allowed identification of covariance between soil moisture and ECa, as well as spatial dependence for both variables, for agricultural areas. It was verified that soil moisture, even at levels close to residual, constitutes a relevant secondary component for increasing soil salinity maps precision, and hence to precision agriculture.   Keywords: geostatistics, semi-arid, precision agriculture     LOPES, I. E MONTENEGRO, A. A. DE A. DEPENDÊNCIA ESPACIAL DA UMIDADE DO SOLO E CONDUTIVIDADE ELÉTRICA EM REGIÃO ALUVIAL     2 RESUMO   Informações espaciais sobre as características do solo são essenciais para uma tomada de decisão adequada em relação ao meio ambiente e ao gerenciamento do uso do solo. O objetivo deste trabalho foi investigar a variância cruzada entre a umidade do solo e a condutividade elétrica aparente do solo (CEa), sob diferentes usos do solo em um vale aluvial de Pernambuco. O estudo foi desenvolvido na Unidade de Pesquisa Avançada da Universidade Federal Rural de Pernambuco (UFRPE), localizada na bacia do rio Brígida, município de Panamirim-PE. As amostras de solo foram coletadas em uma malha regular de 20 x 10 m, para a umidade do solo pelo método gravimétrico e, seguindo uma malha regular de 10 x 10 m, as medidas de CEa foram realizadas usando o dispositivo EM38®. Os semivariogramas cruzados foram avaliados e a dependência espacial foi verificada por procedimentos geoestatísticos. Verificou-se procedimentos geoestatísticos, uma baixa variação da umidade do solo e variação intermediária para CEa. O uso da geoestatística permitiu identificar a covariância entre a umidade do solo e o CEa, bem como a dependência espacial para ambas as variáveis, para as áreas agrícolas. Verificou-se que a umidade do solo, mesmo em níveis próximos ao residual, constitui um componente secundário relevante para o aumento da precisão do mapeamento da salinidade do solo e, consequentemente, para a agricultura de precisão.   Palavras-chave: geoestatística, semiárido, agricultura de precisão

Does digital proximity between countries impact entrepreneurship?

Info ◽  
2015 ◽  
Vol 17 (5) ◽  
pp. 46-65 ◽  
Author(s):  
Maria Veronica Alderete
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
Spatial Models ◽  
Negative Influence ◽  
Entrepreneurial Activity ◽  
Spatial Effects ◽  
Content Type ◽  
Spatial Spillovers ◽  
Information And Communication

Purpose – This paper aims to determine if there is a spatial dependence in the entrepreneurial activity among countries. The existence of a “digital proximity” could explain the spatial pattern of entrepreneurship. Design/methodology/approach – This question is empirically addressed by using a five-period, 2008-2012, panel data for 35 countries. A spatial fixed effects panel data model is estimated by using the total entrepreneurial activity published by the global entrepreneurship monitor as the dependent variable. Findings – A significant negative influence of the digital proximity on the entrepreneurial activity is observed. Mobile broadband (MB) direct effect is positive while the indirect effect (the spatial spillovers) is negative, leading to a negative total effect on the total entrepreneurial activity. This result is contrary to non-spatial models’ results. Besides, a higher MB penetration in a country would lead to a competitive advantage fostering its opportunities for entrepreneurship, but reducing those of its neighbours’. Originality/value – This paper examines the relationship between information and communication technology (ICT) and entrepreneurship, by introducing the spatial effects is the main contribution. This paper expands the scant literature on the ICT impact on entrepreneurship. Results obtained support policies towards enforcing innovation, education and reducing entry regulations for encouraging entrepreneurship. Meanwhile, MB policies could counteract the entrepreneurial policies’ results due to the spatial dependence.

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