On Joint Modelling and Testing for Local and Global Spatial Externalities

This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and global externalities in both modelled and unmodelled effects. The proposed model encompasses all the models discussed in Anselin (2003). Robust methods of estimation and testing are developed based on Gaussian quasi-likelihood. Large and small sample properties of the proposed methods are investigated.Asymptotic property, Finite sample property, Quasi-likelihood, Spatial regression models, Robustness, Tests of spatial externalities

On Joint Modelling and Testing for Local and Global Spatial Externalities

This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and global externalities in both modelled and unmodelled effects. The proposed model encompasses all the models discussed in Anselin (2003). Robust methods of estimation and testing are developed based on Gaussian quasi-likelihood. Large and small sample properties of the proposed methods are investigated.Asymptotic property, Finite sample property, Quasi-likelihood, Spatial regression models, Robustness, Tests of spatial externalities.

A Robust LM Test for Spatial Error Components

This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaching to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant even when sample size is large. As a result, there can be severe size distortions of the Anselin’s LM test, a phenomenon revealed by the Monte Carlo results of Anselin and Moreno (2003) and further confirmed by the Monte Carlo results presented in this paper. Our Monte Carlo results also show that the proposed test performs well in general.Distributional misspecification; Robustness, Spatial layouts; Spatial error components; LM tests

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)

Knowledge, Spillovers and Firms’ International Growth. An Analysis at the Italian NUTS 3 Level

In the framework of analyses on the relationship between geography and technological innovation, the role of universities has received considerable attention. Both theoretical and empirical literature has shown that university research positively influences the capacity for innovation of the surrounding firms (Jaffe, 1989; Feldman, 1994; Acs et al, 2002). Universities play a central role in innovation processes both as the main responsible for basic research and also as forgers of human capital’s skills. Empirical work has highlighted that such effects radiate from major university centres crossing borders and administrative boundaries (Anselin et al., 1997). This paper focuses on the relationship between universities and the innovative capacity at the territorial level. Specifically, our empirical analysis investigates whether university research spillovers are highly localised or they rather flow across borders. Empirical literature has widely investigated intensity and directions of such spillovers, mainly within the theoretical framework of Griliches-Jaffe. However, we extend the empirical evidence exploring whether intensity and directions of spillovers depend on universities’ specificities (e.g. size, fields of specialization, fund rising capacity) and on the local absorptive capacity. The analysis is developed at the Italian NUTS3 level, using an explicit spatial econometric approach applied to a knowledge production function. References Acs, Z., Anselin, L., and Varga, A. (2002): “Patents and innovation counts as measures of regional production of new knowledgeâ€, Research Policy 31, pp. 1069-1085. Anselin, L., Varga, A., and Acs, Z. (1997): “Local geographic spillovers between University research and high technology innovationsâ€, Journal of Urban Economics 42, pp. 422-448. Feldman, M. (1994): The Geography of innovation, Kluwer Academic Publishers. Dordrecht. Jaffe, A. (1989): “Real effects of academic researchâ€, The American Economic Review, vol 79, n. 5, pp. 957-970.

A Robust LM Test for Spatial Error Components

This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaching to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant even when sample size is large. As a result, there can be severe size distortions of the Anselins LM test, a phenomenon revealed by the Monte Carlo results of Anselin and Moreno (2003) and further confirmed by the Monte Carlo results presented in this paper. Our Monte Carlo results also show that the proposed test performs well in general.Distributional misspecification, Robustness, Spatial layouts, Spatial error components, LM tests

Spatial Effects and Verdoorn Law in the Portuguese Context

The consideration of spatial effects at a regional level is becoming increasingly frequent and the work of Anselin (1988), among others, has contributed to this. This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker) in the NUTs III economic sectors of mainland Portugal from 1995 to 1999 and from 2000 to 2005 (taking in count the availability of data), considering the Verdoorn relationship. To analyse 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 of all sectors 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 (Martinho, 2011)

On proximity and hierarchy : exploring and modelling space using multilevel modelling and spatial econometrics

Spatial econometrics and also multilevel modelling techniques are increasingly part of the regional scientists‟ toolbox. Both approaches are used to model spatial autocorrelation in a wide variety of applications. However, it is not always clear on which basis researchers make a choice between spatial econometrics and spatial multilevel modelling. Therefore it is useful to compare both techniques. Spatial econometrics incorporates neighbouring areas into the model design; and thus interprets spatial proximity as defined in Tobler‟s first law of geography. On the other hand, multilevel modelling using geographical units takes a more hierarchical approach. In this case the first law of geography can be rephrased as „everything is related to everything else, but things in the same region are more related than things in different regions‟. The hierarchy (multilevel) and the proximity (spatial econometrics) approach are illustrated using Belgian mobility data and productivity data of European regions. One of the advantages of a multilevel model is that it can incorporate more than two levels (spatial scales). Another advantage is that a multilevel structure can easily reflect an administrative structure with different government levels. Spatial econometrics on the other hand works with a unique set of neighbours which has the advantage that there still is a relation between neighbouring municipalities separated by a regional boundary. The concept of distance can also more easily be incorporated in a spatial econometrics setting. Both spatial econometrics and spatial multilevel modelling proved to be valuable techniques in spatial research but more attention should go to the rationale why one of the two approaches is chosen. We conclude with some comments on models which make a combination of both techniques