The Spatial Durbin Model and the Common Factor Tests

Mur, Jesús; Angulo, Ana

doi:10.1080/17421770601009841

Cited by 73 publications

(42 citation statements)

References 16 publications

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“…These problems may be due to various spatially related misspecifications common to most applied hedonic house price analyses and are an argument in favour of the SDM. To study further this issue of specification, a common factor constraints hypothesis test has been performed (Bivand, 1984;Mur and Angulo, 2006 This is a test of the null hypothesis that the coefficients of the OLS model and the corresponding spatial error model are equal. The null hypothesis has to be rejected and is an additional support of the SDM.…”

Section: Estimation Results and Spatial Spillover Effects From The Bmmentioning

confidence: 99%

Spatial Impacts, Local Labour Market Characteristics and Housing Prices

Osland¹,

Thorsen²

2013

Urban Studies

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This paper takes as a starting-point a model where spatial variation in housing prices is explained by urban attraction and labour market accessibility effects.Using data from a region in south-west Norway, estimation results are found to be encumbered, however, with significant spatial effects. The spatial Durbin model is used to account for this and to provide estimates of direct and indirect impacts. In addition, hypotheses are tested that some of the spatial variation in housing prices reflects local labour market characteristics. Some support is found for a hypothesis that a model specification should account for sub-centres located at some distance from the central parts of the region. The indirect impacts estimated in the spatial Durbin model suggest that spatially related misspecifications of implicit elasticities in the ordinary least squares model are mainly due to negative externalities close to areas of high labour market accessibility.

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Section: Estimation Results and Spatial Spillover Effects From The Bmmentioning

confidence: 99%

Spatial Impacts, Local Labour Market Characteristics and Housing Prices

Osland¹,

Thorsen²

2013

Urban Studies

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“…In the next section, we will present a list of the alternative model formulations to be estimated. Osland and Thorsen (, ) have tested Models 1 and 2 in the list of alternatives for spatial effects, by using robust Lagrange multiplier tests (Osland, ) and the common factor hypothesis test (Mur and Angulo, ). The results from these tests showed that there are still significant spatial effects in the residuals.…”

Section: The Modeling Frameworkmentioning

confidence: 99%

Accounting for Local Spatial Heterogeneities in Housing Market Studies

Osland¹,

Thorsen

Thorsen³

2016

Journal of Regional Science

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We adopt a novel method to deal with omitted spatial heterogeneities in hedonic house price analysis. A Gaussian variant of the conditional autoregressive (CAR) model is used to study the impact of spatial effects. In a general linear modeling framework, we include zone‐specific random effects that are allowed to interact spatially with neighboring zones. The results demonstrate that this estimator accounts for missing spatial information, producing more reliable results on estimated spatially related coefficients. The CAR model is benchmarked against a fixed effects model. Socioeconomic neighborhood characteristics are found to have only modest impact on spatial variation in housing prices.

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“…Second, we use spatial Durbin models (SDMs) to examine spatial lags on our dependent and explanatory variables (Mur and Angulo 2006). The SDMs capture feedback influences between variables-that is, the impacts passing through neighbouring subdistricts and back to a subdistrict itself (Elhorst 2010).…”

Section: Model Specifications With Spatial Dependencementioning

confidence: 99%

Agro-clusters and Rural Poverty: A Spatial Perspective for West Java

Wardhana

Ihle

Heijman

2017

Bulletin of Indonesian Economic Studies

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Neighbouring economies are likely to influence one another. The concentration of farming activities referred to as an 'agro-cluster' generates opportunities for income and employment in a given region and its surrounding area. We analyse the link between poverty rates and agro-clusters by accounting for spatial spillovers. To quantify agroclusters, we employ one input-oriented and one output-oriented measure. Our analysis applies six spatial econometric specifications and focuses on 545 subdistricts of West Java, where about 10% of the population live in poverty. We find that the concentration of agricultural employment substantially reduces poverty in a subdistrict as well as in neighbouring subdistricts. We also find that specialisation in crop outputs has positive impacts on poverty reduction and that localisation externalities are fundamental to agriculture's success. These findings imply that policy interventions may be applied in a spatially selective manner because they will generate spatial-spillover effects on poverty reduction in surrounding areas.

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The Spatial Durbin Model and the Common Factor Tests

Cited by 73 publications

References 16 publications

Spatial Impacts, Local Labour Market Characteristics and Housing Prices

Spatial Impacts, Local Labour Market Characteristics and Housing Prices

Accounting for Local Spatial Heterogeneities in Housing Market Studies

Agro-clusters and Rural Poverty: A Spatial Perspective for West Java

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