Comparing estimation methods for spatial econometrics techniques using R.

Recent advances in spatial econometrics model fitting techniques have made it more desirable to be able to compare results and timings. Results should correspond between implementations using different applications, while timings are more readily compared within a single application. A broad range of model fitting techniques are provided by the contributed R packages for spatial econometrics. These model fitting techniques are associated with methods for estimating impacts and some tests, which will also be presented and compared. This review constitutes an up-to-date demonstration of techniques now available in R, and mentions some that will shortly become more generally available.Spatial autoregression; Econometric software.

After “Raising the Bar”: applied maximum likelihood estimation of families of models in spatial econometrics.

Elhorst (2010) shows how the recent publication of LeSage and Pace (2009) in his expression “raises the bar” for our fitting of spatial econometrics models. By extending the family of models that deserve attention, Elhorst reveals the need to explore how they might be fitted, and discusses some alternatives. This paper attempts to take up this challenge with respect to implementation in the R spdep package for the maximum likelihood case, using a smaller data set to see whether earlier conclusions would be changed when newer techniques are used, and two larger data sets to examine model fitting issues.Models; Econometrics;

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.

Exploiting Parallelization in Spatial Statistics: an Applied Survey using R.

Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit this kind of division, and particularly when there is little or no need for communication between the nodes. Another approach is to parallelize bottom-up, by the substitution of multi-threaded low-level functions for single-threaded ones in otherwise unchanged user-level functions. This survey examines the timings of typical spatial data analysis tasks across a range of data sizes and hardware under different combinations of these two approaches. Conclusions are drawn concerning choices of alternatives for parallelization, and attention is drawn to factors conditioning those choices.Statistical software; Parallelization; Optimized linear algebra subroutines; Multicore processors; Spatial statistics.

Further explorations of interactions between agricultural policy and regional growth in Western Europe - approaches to nonstationarity in spatial econometrics

The work discussed in Bivand and Brunstad (2003) was an attempt to throw light on apparent variability in regional convergence in relation to agriculture as a sector subject to powerful political measures, in Western Europe, 1989 1999. We tried to explore the possibility that some of the observed specification issues in current results are rooted in neglecting agricultural policy interventions, within the limitations imposed by data available. We also attempted to use this as a case setting for evaluating the appropriateness of geographically weighted regression (GWR) as a technique for assessing coef- ficient variability, over and above for instance country dummies, but possibly reflecting missing variables or other specification problems. The present study takes up a number of points made in conclusion in that paper. Since it is possible that the non-stationarity found there is related to further missing variables, including the inadequacy of the way in which agricultural subsidies are represented, we attempt to replace the agriculture variables with better estimates of producer subsidy equivalents for the base year. We also look at ways of handling changes in agricultural policy regime occurring between years and T. This raises the further challenge of looking at both spatial and temporal dimensions at the same time, which we will discuss, but are not likely to resolve satisfactorily. On the technical side, the tests on GWR estimates also need to be more firmly established. The GWR results also need to be tested for spatial autocorrelation, and re-worked in an adaptive weighting framework, although GWR does already involve a spatial weighting of the observations themselves. The paper is therefore also an account of the development of software contributed to the R project (R Development Core Team, 2004) as packages, in particular the spdep package for spatial econometrics, and the spgwr package for GWR fitting. In particular, specific issues regarding the handling of the Jacobian in fitting spatial simultaneous autoregressive (SAR) models, and in interpreting GWR output will be discussed. These will be set in the context of on-going work on semi-parametric spatial filtering, which it is hoped to add to spdep following contributions by Michael Tiefelsdorf, so that the weaknesses and strengths of alternative approaches can be compared. Concentrating on implementations in R is justified by the preliminary nature of many of these methods requiring open source and replicable statistical research approaches, so that others can, if they wish, see how results were calculated. One such technical issue is the representation of neighbours in the various approaches, and of the impact of symmetry requirements in conditional autoregressive (CAR) models typically used in MCMC estimation using Open- BUGS and elsewhere. Indeed, in many SAR models, symmetry is also required, or at least underlying symmetry, with the weights matrix in the rowstandardised weighting scheme typically being similar to a symmetric matrix. Using the Western European regional growth data augmented with agricultural policy variables, we will try to explore how far some as-yet unresolved technical questions impede progress with substantive interpretation. We will also try to show how these questions may be handled in other software settings, and how data can be moved between software platforms for analysis. In conclusion, the paper has two threads, one focussing on the analysis of the relationships between regional growth and agricultural policy, generating models needing testing, while the other attempts to meet the software demands generated in the first thread, and to incorporate on-going research in spatial data-analytic methods to respond adequately to the potential importance of the substantive research question.

Dynamic externalities and regional manufacturing development: An exploration of the Polish experience before and after 1989

The impact of localization and urbanization economies on regional manufacturing development in Poland 1976-96 is assessed in terms of employment and the regional convergence or divergence of the economy. We examine current research on the role of dynamic production externalities in regional manufacturing development, starting with a review of recent literature on the nature of such externalities in manufacturing location, and how positive externalities may influence the spatial clustering of manufacturing industries. While much of the current literature is focussed on US experience, we analyse manufacturing employment data for Poland, in order to explore to what extent conclusions drawn from US experience may illuminate a regional economy in transition. The analysis also pays attention to the integration of a number of different methods from differing traditions, from economic geography, regional science, and new economic geography, including location quotients, Gini indices, shift-share, analysis of variance, Poisson regression, and Poisson regression for panel data. We find that radical changes have occurred in patterns of Polish regional manufacturing employment, both with regard to sectors and regions. Transition is refocussing the regional economy on strong regional centres, and on sectors regarded with little favour in the planned economy, such as food processing and wood products, including furniture.

Analytical environments

acceptedVersio

Geocomputation and open source software: components and software stacks.

Geocomputation, with its necessary focus on software development and methods innovation, has enjoyed a close relationship with free and open source software communities. These extend from communities providing the numerical infrastructure for computation, such as BLAS (Basic Linear Algebra Subprograms),through language communities around Python, Java and others, to communities supporting spatial data handling, especially the projects of the Open Source Geospatial Foundation. This chapter surveys the stack of software components available for geocomputation from these sources, looking in most detail at the R language and environment, and how OSGeo projects have been interfaced with it. In addition, attention will be paid to open development models and community participation in software development. Since free and open source geospatial software has also achieved a successively greater presence in proprietary software as computational platforms evolve, the chapter will close with some indications of future trends in software component stacks, using Terralib as an example.Geocomputation; Open source software

Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation

Integrated Nested Laplace Approximation provides a fast and effective method for marginal inference on Bayesian hierarchical models. This methodology has been implemented in the R-INLA package which permits INLA to be used from within R statistical software. Although INLA is implemented as a general methodology, its use in practice is limited to the models implemented in the R-INLA package. Spatial autoregressive models are widely used in spatial econometrics but have until now been missing from the R-INLA package. In this paper, we describe the implementation and application of a new class of latent models in INLA made available through R-INLA. This new latent class implements a standard spatial lag model, which is widely used and that can be used to build more complex models in spatial econometrics. The implementation of this latent model in R-INLA also means that all the other features of INLA can be used for model fitting, model selection and inference in spatial econometrics, as will be shown in this paper. Finally, we will illustrate the use of this new latent model and its applications with two datasets based on Gaussian and binary outcomes

Comparing Implementations of Estimation Methods for Spatial Econometrics

Recent advances in the implementation of spatial econometrics model estimation techniques have made it desirable to compare results, which should correspond between implementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up to date comparison of generalized method of moments (GMM) and maximum likelihood (ML) implementations now available. The comparison uses the cross sectional US county data set provided by Drukker, Prucha, and Raciborski (2011c, pp. 6-7). The comparisons will be cast in the context of alternatives using the MATLAB Spatial Econometrics toolbox, Stata, Python with PySAL (GMM) and R packages including sped, sphet and McSpatial