Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

Evaluating the Temporal and the Spatial Heterogeneity of the European Convergence Process, 1980-1999

In this paper, we suggest a general framework that allows testing simultaneously for temporal heterogeneity, spatial heterogeneity and spatial autocorrelation in b-convergence models. Based on a sample of 145 European regions over the 1980-1999 period, we estimate a Seemingly Unrelated Regression Model with spatial regimes and spatial autocorrelation for two sub-periods: 1980-1989 and 1989-1999. The assumption of temporal independence between the two-periods is rejected and the estimation results point to the presence of spatial error autocorrelation in both sub-periods and spatial instability in the second sub-period, indicating the formation of a convergence club between the peripheral regions of the European Union.b-convergence models, spatial autocorrelation, convergence clubs, temporal instability

More on the F-test under nonspherical disturbances

We show that the F-test can be both liberal and conservative in the context of a particular type of nonspherical behaviour induced by spatial autocorrelation, and that the conservative variant is more likely to occur for extreme values of the spatial autocorrelation parameter. In particular, it will wipe out the progressive one as the sample size increases. --F-test,spatial autocorrelation

Spatial Autocorrelation and Verdoorn Law in the Portuguese NUTs III

This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker), at economic sectors level of the NUTs III of mainland Portugal, from 1995 to 1999 and from 2000 to 2005 (taking in count the data availability and the Portuguese and European context), considering the Verdoorn relationship. From the analyses of the data, by using Moran I statistics, it is stated that productivity is subject to a positive spatial autocorrelation (productivity of each of the regions develops in a similar manner to each of the neighbouring regions), above all in services. The total sectors of all regional economy present, also, indicators of being subject to positive autocorrelation in productivity. Bearing in mind the results of estimations, it can been that the effects of spatial spillovers, spatial lags (measuring spatial autocorrelation through the spatially lagged dependent variable) and spatial error (measuring spatial autocorrelation through the spatially lagged error terms), influence the Verdoorn relationship when it is applied to the economic sectors of Portuguese regions. The results obtained for the two periods are different, as expected, and are better in second period, because, essentially, the European and national public supports (Martinho, 2011)

The Importance of Spatial Autocorrelation for Regional Employment Growth in Germany

In analyzing the disparities of the regional developments in the volume of employment in Germany, in the recent empirical literature so called shift-share-regression-models are frequently applied. However, these models usually neglect spatial interdependencies, even though such interdependencies are likely to occur on a regional level. Therefore, this paper focuses on the importance of spatial dependencies using spatial autocorrelation in order to analyze regional employment development. Spatial dependency in the form of spatial lag, spatial error and cross regressive model are compared. The results indicate that the exogenous variables’ spatial lag sufficiently explains the spatial autocorrelation of regional employment growth.spatial interdependency, spatial autocorrelation, shift-share-regression, regional employment growth

Spatial Autocorrelation in a Retail Context

This paper describes and applies the weighted least squares (WLS) technique that corrects for spatial autocorrelation in the residuals of hedonic regressions. Most empirical studies to date have focused on spatial autocorrelation in the housing market, i.e., single family home valuation. This study focuses on mall stores within shopping centers, with an emphasis on retail site selection within the mall.Spatial autocorrelation, hedonic modeling, bid rent, retail rents

Spatial effects and convergence theory in the Portuguese situation

This study analyses, through cross-section estimation methods, the influence of spatial effects and human capital in the conditional productivity convergence (product per worker) in the economic sectors of NUTs III of mainland Portugal between 1995 and 2002. To analyse the data, Moran’s I statistics is considered, and it is stated that productivity is subject to positive spatial autocorrelation (productivity develops in a similar manner to productivity in neighbouring regions), above all, in agriculture and services. Industry and the total of all sectors present indications that they are subject to positive spatial autocorrelation in productivity. On the other hand, it is stated that the indications of convergence, specifically bearing in mind the concept of absolute convergence, are greater in industry. Taking into account the estimation results, it is stated once again that the indications of convergence are greater in industry, and it can be seen that spatial spillover effects, spatial lag (capturing spatial autocorrelation through a spatially redundant dependent variable) and spatial error (capturing spatial autocorrelation through a spatially redundant error term), as well as human capital, condition the convergence of productivity in the various economic sectors of Portuguese region in the period under consideration.Spatial Econometric; Growth Endogenous Theory; Portuguese Regions