Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand

An Empirical Study Of Productivity Growth In EU28 - Spatial Panel Analysis

This paper investigates the spatial process of productivity growth in the European Union on the foundations of the theory of New Economic Geography. The proposed model is based on the study of NUTS 2 regions and takes into consideration a spatial weights matrix in order to better describe the structure of spatial dependence between EU regions. Furthermore, our paper attempts to investigate the applicability of some new approaches to spatial modelling including parameterization of the spatial weights matrix. Our study presents an application of the spatial panel model with fixed effects to Fingleton’s theoretical framework. We suggest that the applied approach constitutes an innovation to spatial econometric studies providing additional information hence, a deeper analysis of the investigated problem

Structural Interactions in Spatial Panels

Until recently, much effort has been devoted to the estimation of panel data regression models without adequate attention being paid to the drivers of diffusion and interaction across cross section and spatial units. We discuss some new methodologies in this emerging area and demonstrate their use in measurement and inferences on cross section and spatial interactions. Specifically, we highlight the important dis?tinction between spatial dependence driven by unobserved common factors and those based on a spatial weights matrix. We argue that, purely factor driven models of spatial dependence may be somewhat inadequate because of their connection with the exchangeability as?sumption. Limitations and potential enhancements of the existing methods are discussed, and several directions for new research are highlighted.Cross Sectional and Spatial Dependence, Spatial Weights Matrix, Interactions and Diffusion, Monetary Policy Committee, Generalised Method of Moments.

Computing the Jacobian in spatial models: an applied survey.

Despite attempts to get around the Jacobian in fitting spatial econometric models by using GMM and other approximations, it remains a central problem for maximum likelihood estimation. In principle, and for smaller data sets, the use of the eigenvalues of the spatial weights matrix provides a very rapid and satisfactory resolution. For somewhat larger problems, including those induced in spatial panel and dyadic (network) problems, solving the eigenproblem is not as attractive, and a number of alternatives have been proposed. This paper will survey chosen alternatives, and comment on their relative usefulness.Spatial autoregression; Maximum likelihood estimation; Jacobian computation; Econometric software.

Spatial Interactions in Hedonic Pricing Models:The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales

Estimation of the spatial weighting matrix for regular lattice data -- An adaptive lasso approach with cross-sectional resampling

Spatial econometric research typically relies on the assumption that the spatial dependence structure is known in advance and is represented by a deterministic spatial weights matrix. Contrary to classical approaches, we investigate the estimation of sparse spatial dependence structures for regular lattice data. In particular, an adaptive least absolute shrinkage and selection operator (lasso) is used to select and estimate the individual connections of the spatial weights matrix. To recover the spatial dependence structure, we propose cross-sectional resampling, assuming that the random process is exchangeable. The estimation procedure is based on a two-step approach to circumvent simultaneity issues that typically arise from endogenous spatial autoregressive dependencies. The two-step adaptive lasso approach with cross-sectional resampling is verified using Monte Carlo simulations. Eventually, we apply the procedure to model nitrogen dioxide ($\mathrm{NO_2}$) concentrations and show that estimating the spatial dependence structure contrary to using prespecified weights matrices improves the prediction accuracy considerably

Spatial Shift-Share Analysis of the Leisure and Hospitality Sector on the Gulf Coast following Hurricane Katrina

Employment shifts in the Leisure and Hospitality sector along the Gulf coast following Hurricane Katrina were explored using spatial shift-share analysis. Using a spatial weights matrix that incorporated relative employment, and distance measures relative to the track of the storm we calculated classical and spatial shift-share components. Each of the spatial components then was regressed on net employment change, and the results were statistically significant, and similar to results obtained by Marquez and Ramajo (2005). These results suggest that spatial interaction between employment centers as well as with the storm track, was a relevant aspect of the employment shifts that occurred following Hurricane Katrina.Labor and Human Capital, Research Methods/ Statistical Methods, R11, R12, J21,