Kernel-based spatial error model for analyzing spatial panel data

2020 ◽  
Vol 15 (3) ◽  
pp. 239-248
Author(s):  
Jooyong Shim ◽  
Sang Bum Lee ◽  
Daiwon Kim ◽  
Jung-Suk Yu ◽  
Chanha Hwang
Keyword(s):  
Panel Data ◽  
Error Model ◽  
Support Vector ◽  
Spatial Error Model ◽  
Varying Coefficient Model ◽  
Spatial Error ◽  
Spatial Panel ◽  
Varying Coefficient ◽  
Proposed Model ◽  
Spatial Panel Data

Spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies.

scholarly journals Inefficiencies in a healthcare system with a regulatory split of power: a spatial panel data analysis of avoidable hospitalisations in Austria

2019 ◽  
Vol 21 (1) ◽  
pp. 85-104
Author(s):  
Anna-Theresa Renner
Keyword(s):  
Panel Data ◽  
Bayesian Model Averaging ◽  
Panel Data Analysis ◽  
Model Averaging ◽  
Social Health Insurance ◽  
Population Characteristics ◽  
University Education ◽  
Spatial Error ◽  
Spatial Panel ◽  
Spatial Panel Data

Abstract Despite generous universal social health insurance with little formal restrictions of outpatient utilisation, Austria exhibits high rates of avoidable hospitalisations, which indicate the inefficient provision of primary healthcare and might be a consequence of the strict regulatory split between the Austrian inpatient and outpatient sector. This paper exploits the considerable regional variations in acute and chronic avoidable hospitalisations in Austria to investigate whether those inefficiencies in primary care are rather related to regional healthcare supply or to population characteristics. To explicitly account for inter-regional dependencies, spatial panel data methods are applied to a comprehensive administrative dataset of all hospitalisations from 2008 to 2013 in the 117 Austrian districts. The initial selection of relevant covariates is based on Bayesian model averaging. The results of the analysis show that supply-side variables, such as the number of general practitioners, are significantly associated with decreased chronic and acute avoidable hospitalisations, whereas characteristics of the regional population, such as the share of population with university education or long-term unemployed, are less relevant. Furthermore, the spatial error term indicates that there are significant spatial dependencies between unobserved characteristics, such as practice style or patients’ utilization behaviour. Not accounting for those would result in omitted variable bias.

scholarly journals Modeling Human Development Index of Bali with Spatial Panel Data Regression

2019 ◽  
Vol 4 (5) ◽  
pp. 132-137
Author(s):  
Mita Lasdiyanti ◽  
Eka N. Kencana ◽  
Putu Suciptawati
Keyword(s):  
Panel Data ◽  
Human Development ◽  
Human Development Index ◽  
Panel Data Analysis ◽  
Spatial Error ◽  
Development Index ◽  
Spatial Panel ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data

Human development index (HDI) is an index that represents the successfulness of human development in a region. For Bali, one of 34 provinces in Indonesia, the progress of HDI in the period 2010–2017 showed an increasing trend. In the year 2010, the Bali’s HDI is accounted for 70.10, gradually increase to 74.30 in the year 2017. However, in 2017 there are some regions with their HDIs are below of Bali’s HDI, namely Jembrana, Buleleng, Klungkung, Bangli, and Karangasem. The aim of this work is to model the HDI of 9 regencies of Bali so that the main determinant to increase the HDIs especially for the regencies with lower HDIs could be determined. The model consists of one dependent variable (HDI) with three indicators as the independent ones, there are (a) life expectancy, (b) education, and (b) standard of living. By applying spatial panel data analysis, five models were built i.e. CEM, FEM (individual), FEM (time), REM, and spatial error FEM to determine the effect of each indicator. The result shows the best model is spatial error FEM in which education has the biggest influence compare than the others.

scholarly journals A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

2018 ◽  
Vol 10 (8) ◽  
pp. 2800 ◽  
Author(s):  
Rui Jin ◽  
Jianya Gong ◽  
Min Deng ◽  
Yiliang Wan ◽  
Xuexi Yang
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Interaction Model ◽  
Spillover Effects ◽  
Spatiotemporal Pattern ◽  
Regional Economic ◽  
Spatial Panel ◽  
Economic Agglomeration ◽  
Spatial Panel Data

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

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