scholarly journals Spatial Spillover of House Prices: An Empirical Study of the Yangtze Delta Urban Agglomeration in China

2019 ◽  
Vol 11 (2) ◽  
pp. 544 ◽  
Author(s):  
Ling Zhang ◽  
He Wang ◽  
Yan Song ◽  
Haizhen Wen
Keyword(s):  
Indirect Effects ◽  
Spatial Dependence ◽  
Industrial Structure ◽  
House Prices ◽  
Urban Agglomeration ◽  
Spatial Effects ◽  
Yangtze Delta ◽  
Data Set ◽  
Section Data ◽  
Year 2000

This study investigates the spatial dependence of house prices in the Yangtze Delta Urban Agglomeration since the year 2000. According to Moran’s I index and the LISA scatter plot derived from a cross-section data set, the spatial dependence of house prices can be traced across the 25 cities in the agglomeration and became more evident after 2005. This study develops a spatial panel model with geographical distance and economic distance weight matrices. Spatial effects significantly influenced house prices in both cases but the intensity of the former was weaker than for the latter. Income, proportion of the tertiary industry, and amenity exhibited significant indirect effects on house prices in other cities in the inner region of the agglomeration, while competition of population between cities with economic proximity exerted negative indirect effects. Furthermore, urban industrial structure, innovation capability, and urbanization degree revealed differences in terms of spatial dependence among various city groups.

scholarly journals Partitioned and Non-Partitioned Regularized Additive Hazard Models with and Without Spatial Dependence

2021 ◽  
Vol 3 (2) ◽  
pp. 1-19
Author(s):  
Peter Enesi Omaku ◽  
Benjamin Agboola Oyejola
Keyword(s):  
Survival Data ◽  
Spatial Dependence ◽  
Information Criteria ◽  
Penalized Splines ◽  
Hazard Models ◽  
Time Interval ◽  
Spatial Effects ◽  
Sample Sizes ◽  
Data Set ◽  
Non Linear

Spatial effects are often simultaneously investigated with non-linear effects of continuous covariates and the usual linear effect. In this work the performance of models with and without spatial dependence in partitioned (PM) and non-partitioned models (NPM) for four (4) censoring percentages, three(3) levels of Weibull baseline variances (WBV), and sample sizes 100, 500 & 1000 were investigated. Hazard models were adapted to the generalized additive predictors and analyses were carried out via MCMC simulation technique. The performances of the models were again assessed when fitted to the diabetic data set. Results suggest that; partition models outperformed the non-partition ones. Models with spatial dependence perform better than models without spatial dependence in denser event times and when WBVs are low. The partition models perform better with spatial dependence than the Non-partitioned models. For the diabetic data set, it is seen that covariates Age and Blood Sugar level (BSL) violates the proportionality assumptions upon test. Further assessment from the graph of coefficient against time; suggest that Age be put to cut-points while BSL was estimated for models with and without Penalized splines for the sake of comparison, since the graph shows just a slight deviation from proportionality. Hazard rates for the time varying Age; indicate that as the time of study rolls by, the hazard of experiencing the event death from the disease increases steadily between intervals but constant within each time interval. A unit change in hazard rate for BSL indicates a decrease for PM implemented for with and without penalized splines. The model without penalized splines was however, seen to be better with smaller DIC (Deviance Information Criteria) value. Marriage is seen to be significant in the management of the disease in comparison to single patients. In addition patients are advised to visit their physicians on a regular basis to run a routine check to keep their BSL in good range. The study provides a means of moving out of non-linear ruts in survival data analysis. Intervals increase sample sizes (pseudoobservations), which in turn improves the modified Partitioned model when they are with or without 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.

Has tourism driven house prices in Germany? Time-varying evidence since 1870

2021 ◽  
pp. 135481662110088
Author(s):  
Sefa Awaworyi Churchill ◽  
John Inekwe ◽  
Kris Ivanovski
Keyword(s):  
World War Ii ◽  
Historical Data ◽  
House Prices ◽  
Housing Prices ◽  
Time Varying ◽  
Data Set ◽  
Time Series Modelling ◽  
The Impact ◽  
Tourism Flows ◽  
Non Parametric

Using a historical data set and recent advances in non-parametric time series modelling, we investigate the nexus between tourism flows and house prices in Germany over nearly 150 years. We use time-varying non-parametric techniques given that historical data tend to exhibit abrupt changes and other forms of non-linearities. Our findings show evidence of a time-varying effect of tourism flows on house prices, although with mixed effects. The pre-World War II time-varying estimates of tourism show both positive and negative effects on house prices. While changes in tourism flows contribute to increasing housing prices over the post-1950 period, this is short-lived, and the effect declines until the mid-1990s. However, we find a positive and significant relationship after 2000, where the impact of tourism on house prices becomes more pronounced in recent years.

Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods

2010 ◽  
Vol 32 (2) ◽  
pp. 139-160 ◽  
Author(s):  
Steven Bourassa ◽  
Eva Cantoni ◽  
Martin Hoesli
Keyword(s):  
Spatial Dependence ◽  
House Prices ◽  
Alternative Methods

scholarly journals A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

2019 ◽  
Vol 8 (11) ◽  
pp. 508
Author(s):  
Lan Hu ◽  
Yongwan Chun ◽  
Daniel A. Griffith
Keyword(s):  
Multilevel Model ◽  
House Prices ◽  
House Price ◽  
Spatial Filtering ◽  
Neighborhood Environment ◽  
Model Specification ◽  
Spatial Effects ◽  
Residential Areas ◽  
Fairfax County ◽  
Eigenvector Spatial Filtering

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

scholarly journals Global production networks and the evolution of industrial capabilities: does production sharing warp the product space?

2020 ◽  
Vol 72 (3) ◽  
pp. 731-747
Author(s):  
Russell Thomson ◽  
Prema-Chandra Athukorala
Keyword(s):  
Product Space ◽  
Industrial Structure ◽  
De Novo ◽  
Trade Openness ◽  
Production Networks ◽  
Global Production Networks ◽  
Industrial Upgrading ◽  
Data Set ◽  
Production Sharing ◽  
Space Approach

Abstract Do production capabilities of countries evolve from existing capabilities or emerge de novo? The Product Space approach developed by Hidalgo, Klinger, Barabási and Hausmann postulates that a country’s existing industrial structure largely determines its opportunities for industrial upgrading. However, this is difficult to reconcile with the export dynamism of many developing countries such as Thailand, Malaysia, Costa Rica and Vietnam that transformed from primary commodity dependence to exporters of dynamic manufactured products. In each of these cases, global production sharing facilitated industrial transition. In this article, we advance the Product Space approach to accommodate the role of global production sharing. Using a newly constructed multi-country data set of manufacturing exports that distinguishes between trade within global production networks and traditional horizontal trade, we find that that existing industrial structure has a smaller impact, but trade openness has a greater impact, on industrial upgrading within vertically integrated global industries.

scholarly journals The reasoned action approach applied to health behavior: Role of past behavior and test of some key moderators using meta-analytic structural equation modeling

2018 ◽  
Author(s):  
Martin S Hagger ◽  
Juho Polet ◽  
Taru Lintunen
Keyword(s):  
Structural Equation Modeling ◽  
Health Behavior ◽  
Indirect Effects ◽  
Structural Equation ◽  
Social Cognitive ◽  
Reasoned Action ◽  
Equation Modeling ◽  
Reasoned Action Approach ◽  
Data Set ◽  
Past Behavior

Rationale: The reasoned action approach (RAA) is a social cognitive model that outlines the determinants of intentional behavior. Primary and meta-analytic studies support RAA predictions in multiple health behaviors. However, including past behavior as a predictor in the RAA may attenuate model effects. Direct effects of past behavior on behavior may reflect non-conscious processes while indirect effects of past behavior through social cognitive variables may represent reasoned processes. Objective: The present study extended a previous meta-analysis of the RAA by including effects of past behavior. The analysis also tested effects of candidate moderators of model predictions: behavioral frequency, behavior type, and measurement lag.Method: We augmented a previous meta-analytic data set with correlations between model constructs and past behavior. We tested RAA models that included and excluded past behavior using meta-analytic structural equation modeling and compared the effects. Separate models were estimated in studies on high and low frequency behaviors, studies on different types of behavior, and studies with longer and shorter measurement lag.Results: Including past behavior attenuated model effects, particularly the direct effect of intentions on behavior, and indirect effects of experiential attitudes, descriptive norms, and capacity on behavior through intentions. Moderator analyses revealed larger intention-behavior and past behavior-behavior effects in high frequency studies, but the differences were not significant. No other notable moderator effects were observed.Conclusion: Findings indicate a prominent role for habitual processes in determining health behavior and inclusion of past behavior in RAA tests is important to yield precise estimates of model effects.

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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