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Roger Bivand - One of the best experts on this subject based on the ideXlab platform.
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Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation
arXiv: Computation, 2017Co-Authors: Virgilio Gómez-rubio, Roger BivandAbstract: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.
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comparing implementations of estimation methods for Spatial Econometrics
Journal of Statistical Software, 2015Co-Authors: Roger Bivand, Gianfranco PirasAbstract: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 and maximum likelihood implementations now available. The comparison uses the cross-sectional US county data set provided by Drukker, Prucha, and Raciborski (2013d). The comparisons will be cast in the context of alternatives using the MATLAB Spatial Econometrics toolbox, Stata's user-written sppack commands, Python with PySAL and R packages including spdep, sphet and McSpatial.
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a new latent class to fit Spatial Econometrics models with integrated nested laplace approximations
Procedia environmental sciences, 2015Co-Authors: Virgilio Gomezrubio, Roger BivandAbstract:Abstract The new slm latent model for estimating Spatial Econometrics models using INLA has recently been introduced. It will be described briefly and its use will be demonstrated in the accompanying poster.
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approximate bayesian inference for Spatial Econometrics models
spatial statistics, 2014Co-Authors: Roger Bivand, Virgilio GomezrubioAbstract:Abstract In this paper we explore the use of the Integrated Laplace Approximation (INLA) for Bayesian inference in some widely used models in Spatial Econometrics. Bayesian inference often relies on computationally intensive simulation methods, such as Markov Chain Monte Carlo. When only marginal inference is needed, INLA provides a fast and accurate estimate of the posterior marginals of the parameters in the model. Furthermore, we have compared the results provided by these models to those obtained with a more general class of Generalised Linear Models with random effects. In these models, Spatial autocorrelation is modelled by means of correlated Gaussian random effects. We also discuss a procedure to extend the class of models that the R-INLA software can fit. This approach is based on conditioning on one or more parameters so that the resulting models can be fitted with R-INLA across sets of values of the fixed parameters. The posterior marginals of these parameters of interest are then obtained by combining the marginal likelihoods (which are conditioned on the values of the parameters fixed) of the fitted models and a prior on these parameters. This approach can also be used to fit even more general models. Finally, we discuss the use of all these models on two datasets based on median housing prices for census tracts in Boston and the probability of business re-opening in New Orleans in the aftermath of hurricane Katrina.
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Comparing estimation methods for Spatial Econometrics
2012Co-Authors: Roger Bivand, Gianfranco PirasAbstract:Investments in infrastructure are often seen as preferred policies for promoting regional growth. It is clear that good infrastructure projects, based on cost-benefit analysis, should show a countercyclical pattern. Being long-term investments, the benefits should be independent of the business cycle. However, the social costs of each project would be lower in cyclical troughs, when there is surplus capacity. The paper hopes to explore whether these relationships can be demonstrated. JEL: C21, R11, R23, R42 Keywords: Regional growth, transport investments, Spatial Econometrics
Bernard Fingleton - One of the best experts on this subject based on the ideXlab platform.
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contemporary developments in Spatial Econometrics modelling the 14th international workshop on Spatial Econometrics and statistics paris 2015
Spatial Economic Analysis, 2017Co-Authors: Bernard Fingleton, Alain PirotteAbstract:Contemporary developments in Spatial Econometrics modelling: the 14th International Workshop on Spatial Econometrics and Statistics, Paris 2015. Spatial Economic Analysis. This Spatial Economic Analysis special double issue brings together some of the contributions to the 14th International Workshop on Spatial Econometrics and Statistics held in Paris, France, in 2015. The papers contain some significant contributions to econometric methodology with applications to real-world problems. Methodologies range from dynamic Spatial panel models to point pattern analysis, prediction and Monte Carlo simulation. Among other topics, applications include the analysis of land-use patterns, manufacturing firm location and regional labour force participation across the European Union.
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where is the economics in Spatial Econometrics
LSE Research Online Documents on Economics, 2011Co-Authors: Luisa Corrado, Bernard FingletonAbstract:Spatial Econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous Spatial lags, and to Spatial error processes, and which are almost the sine qua non of Spatial Econometrics. Moreover it has been suggested that the significance of a Spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted Spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and Spatial externalities as embodied in Spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.
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bootstrap inference in Spatial Econometrics the j test
Spatial Economic Analysis, 2010Co-Authors: Peter Burridge, Bernard FingletonAbstract:Abstract Kelejian (2008) introduces a J-type test for the situation in which a null linear regression model, Model0, is to be tested against one or more rival non-nested alternatives, Model1, …, Model g , where typically the competing models possess endogenous Spatial lags and Spatially autoregressive error processes. Concentrating on the case g=1, in this paper we examine the finite sample properties of a Spatial J statistic that is asymptotically under the null, and an alternative version that is conjectured to be approximately , both introduced by Kelejian. We demonstrate numerically that the tests are excessively liberal in some leading cases and conservative in others using the relevant chi-square asymptotic approximations, and explore how far this may be corrected using a simple bootstrap resampling method. Inference ‘bootstrap’ dans l'econometrie Spatiale: le test ‘J’ Resume Kelejian (2008) presente un test de type J pour la situation dans laquelle on doit tester un modele a regression lineaire nul...
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New Spatial econometric techniques and applications in regional science
Papers in Regional Science, 2008Co-Authors: Giuseppe Arbia, Bernard FingletonAbstract:The papers appearing in this special issue of Papers in Regional Science, which is devoted to Spatial Econometrics, come from the First International Conference of the Spatial Econometrics Association held in Cambridge (UK) 12-14 July 2008. This conference was the first official meeting of the new association, which was established in May 2006 in Rome and which has already attracted more than 150 members from around the world. At the Cambridge conference there were close to 120 delegates and more than 100 papers were presented. With regard to the eight papers appearing in this special issue, we would particularly like to thank the authors and the referees for their contribution to what we believe is an interesting and lively selection. Recent years have seen a real explosion in the application of Spatial statistical models in all branches of social sciences and in particular in economics. Spatial Econometrics models have been used to analyse different topics (see for example Anselin et al. 2004 for a review) and as a matter of fact Spatial regression techniques are now becoming an established component in the applied Econometrics toolbox, as witnessed by the increasing attention given to this topic in standard Econometrics textbooks (Maddala 2001; Woolridge 2002; Gujarati 2003; Kennedy 2003; Baltagi 2008).
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theoretical economic geography and Spatial Econometrics bridging the gap between theory and reality
2004Co-Authors: Bernard FingletonAbstract:This chapter looks at Theoretical economic geography, Spatial Econometrics bridging the gap between theory and reality
L W Hepple - One of the best experts on this subject based on the ideXlab platform.
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bayesian model choice in Spatial Econometrics
2004Co-Authors: L W HeppleAbstract:Within Spatial Econometrics a whole family of different Spatial specifications has been developed, with associated estimators and tests. This lead to issues of model comparison and model choice, measuring the relative merits of alternative specifications and then using appropriate criteria to choose the “best” model or relative model probabilities. Bayesian theory provides a comprehensive and coherent framework for such model choice, including both nested and non-nested models within the choice set. The paper reviews the potential application of this Bayesian theory to Spatial econometric models, examining the conditions and assumptions under which application is possible. Problems of prior distributions are outlined, and Bayes factors and marginal likelihoods are derived for a particular subset of Spatial econometric specifications. These are then applied to two well-known Spatial data-sets to illustrate the methods. Future possibilities, and comparisons with other approaches to both Bayesian and non-Bayesian model choice are discussed.
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bayesian techniques in Spatial and network Econometrics 2 computational methods and algorithms
Environment and Planning A, 1995Co-Authors: L W HeppleAbstract:Bayesian theory has been seen as having considerable potential and attractiveness for model estimation and analysis in Spatial and network Econometrics. However, analytical and computational problems have also been seen as a great barrier. In this paper the analytical simplifications available are developed and the algorithms required are examined. The author argues that, for a broad class of models in Spatial Econometrics, Bayesian analysis is quite practicable and can be implemented without great cost. The Spatial specifications are mapped into the various forms of Bayesian computation available and detailed examples are provided. Recent developments on the frontier of Bayesian computation have potential to expand further the practical applicability of the Bayesian approach to Spatial Econometrics.
Giuseppe Arbia - One of the best experts on this subject based on the ideXlab platform.
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Further Topics in Spatial Econometrics
A Primer for Spatial Econometrics, 2020Co-Authors: Giuseppe ArbiaAbstract:This chapter discusses some advanced special topics in Spatial Econometrics that have recently been introduced in the literature. The primary purpose is to make the reader knowledgeable on a set of techniques that represent the evolution of the methods presented in Chapter 3 and that constitute an essential part of the skill set currently required by Spatial econometricians. These methods have the potential to make a tremendous impact in the analysis of real world problems in many scientific fields. In particular, section 4.1 discusses the case of non-constant innovation variances (heteroscedastic models), section 4.2 refers to the case where the dependent variable assumes a discrete (in particular, a binary) form, section 4.3 contains some of the modeling strategies in the field of diachronic Spatial econometric models estimated on panel data and, finally, section 4.4 discusses regression models that are non-stationary in the geographical space. Following the introductory nature of the current presentation, we will discuss the various methods with less analytical detail compared to the rest of the book, while referring the interested reader to the current literature for more detail.
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Spatial Econometrics: A Broad View
2016Co-Authors: Giuseppe ArbiaAbstract:Spatial Econometrics can be defined in a narrow and in a broader sense. In a narrow sense it refers to methods and techniques for the analysis of regression models using data observed within discrete portions of space such as countries or regions. In a broader sense it is inclusive of the models and theoretical instruments of Spatial statistics and Spatial data analysis to analyze various economic effects such as externalities, interactions, Spatial concentration and many others. Indeed, the reference methodology for Spatial Econometrics lies on the advances in Spatial statistics where it is customary to distinguish between different typologies of data that can be encountered in empirical cases and that require different modelling strategies. A first distinction is between continuous Spatial data and data observed on a discrete space. Continuous Spatial data are very common in many scientific disciplines (such as physics and environmental sciences), but are still not currently considered in the Spatial Econometrics literature. Discrete Spatial data can take the form of points, lines and polygons. Point data refer to the position of the single economic agent observed at an individual level. Lines in space take the form of interactions between two Spatial locations such as flows of goods, individuals and information. Finally data observed within polygons can take the form of predefined irregular portions of space, usually administrative partitions such as countries, regions or counties within one country.
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Spatial Econometrics a rapidly evolving discipline
Econometrics, 2016Co-Authors: Giuseppe ArbiaAbstract:Spatial Econometrics has a relatively short history in the scenario of the scientific thought. Indeed, the term “Spatial Econometrics” was introduced only forty years ago during the general address delivered by Jean Paelinck to the annual meeting of the Dutch Statistical Association in May 1974 (see [1]). [...]
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dirty Spatial Econometrics
Annals of Regional Science, 2016Co-Authors: Giuseppe Arbia, Giuseppe Espa, Diego GiulianiAbstract:Abstract Spatial data are often contaminated with a series of imperfections that reduce their quality and can dramatically distort the inferential conclusions based on Spatial econometric modeling. A “clean” ideal situation considered in standard Spatial Econometrics textbooks is when we fit Cliff-Ord-type models to data where the Spatial units constitute the full population, there are no missing data, and there is no uncertainty on the Spatial observations that are free from measurement and locational errors. Unfortunately in practical cases the reality is often very different and the datasets contain all sorts of imperfections: They are often based on a sample drawn from the whole population, some data are missing and they almost invariably contain both attribute and locational errors. This is a situation of “dirty” Spatial econometric modeling. Through a series of Monte Carlo experiments, this paper considers the effects on Spatial econometric model estimation and hypothesis testing of two specific sources of dirt, namely missing data and locational errors.
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Spatial econometric interaction modelling where Spatial Econometrics and Spatial interaction modelling meet
2016Co-Authors: Roberto Patuelli, Giuseppe ArbiaAbstract:The present book is concerned with Spatial interaction modelling. In particular, it aims to illustrate, through a collection of methodological and empirical studies, how estimation approaches in this field recently developed, by including the tools typical of Spatial statistics and Spatial Econometrics (Anselin 1988; Cressie 1993; Arbia 2006, 2014), into what LeSage and Pace (2009) deemed as ‘Spatial econometric interaction models’.
Jean H. P. Paelinck - One of the best experts on this subject based on the ideXlab platform.
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Spatial Econometrics: a Personal Overview
2020Co-Authors: Jean H. P. PaelinckAbstract:The paper is written on the basis of my lecture given at the Academy of Economics, University of Katowice, June 2012. Spatial Econometrics is a fast-growing field in the series of quantitative disciplines, auxiliaries of economics and related social sciences. The subject matter is all the more complex as it has a twin brother, Spatial statistics, which in many aspects is complementary of today’s subject. We will essentially treat Spatial Econometrics as we have experienced it, and are still practicing, referring to other materials to cover Spatial statistics proper; a recent paper with references is Griffith and Paelinck (2004), a condensed version appearing in Griffith and Paelinck (2011). Next section is devoted to an obscure period of our discipline, when essentially “Spatial Econometrics without space” was practiced. Then, progressively, that space emerged, and fragments of real Spatialized exercises could be excavated from dispersed articles and books, as section 3 testifies. In 1979, with our colleague Leo Klaassen, we defined a number of principles that, we hoped, would guide future work along lines that tried to identify the specificity of Spatial Econometrics, totally respecting the teachings of general Econometrics; the same applies to theoretical Spatial economics, which integrates all the principles of general theoretical economics. Looking ahead is a more dangerous undertaking, for which we will rely on our recent work; statements resulting from this exercise will probably be controversial, but we hope they will stimulate a fruitful discussion on some delicate points of the discipline. Conclusions and references follow, as usual.
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On Diffusion of Ideas in the Academic World: the Case of Spatial Econometrics
2020Co-Authors: Nikias Sarafoglou, Jean H. P. PaelinckAbstract:Spatial Econometrics is a fast-growing field in the series of quantitative disciplines, auxiliaries of economics and related social sciences. Space, friction, interdependence, spatiotemporal components, externalities and many other aspects interact and should be treated adequately in this field. The publication of the Paelinck and Klaassen book in the late 1970s generated virtually the field Spatial Econometrics This article studies the diffusion of Spatial Econometrics, through experienced history on the one hand, on the other through bibliometric methods. Although this field was an “Invisible College” up to 2006 (absence of any organization in form of association, conference, journal, etc.), the databases depict a fast diffusion in the past and strong prospects for the future.
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General Conclusions About Spatial Econometrics
Advanced Studies in Theoretical and Applied Econometrics, 2018Co-Authors: Daniel A. Griffith, Jean H. P. PaelinckAbstract:This chapter summarizes general findings gleaned from the ten chapters constituting the Spatial Econometrics part of this book. It highlights that many nonstandard data analytic tools exist that are available to a Spatial analyst, ones needing to be tried on ever improving collections of data. It also alludes to future developments in Spatial Econometrics.
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introduction to part ii Spatial Econometrics
2018Co-Authors: Daniel A. Griffith, Jean H. P. PaelinckAbstract:This chapter provides an introductory overview of the second part of this book, which treats Spatial Econometrics topics. It describes some of the source materials for this part of the book and interlaces selected topics with Spatial statistics topics treated in the first part of this book.
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non standard Spatial statistics and Spatial Econometrics
2011Co-Authors: Daniel A. Griffith, Jean H. P. PaelinckAbstract:Part 1. Non-standard Spatial statistics.- 1. Introduction: Spatial statistics, - 2. Individual versus ecological analyses.- 3. Statistical models for Spatial data: some linkages and communalities.- 4. Frequency distributions for simulated Spatially autorcorrelated random variable.- 5. Understanding correlations among Spatial random variables.- 6. Spatially structured random effects: a comparison of three popular specifications.- 7. Spatial filter versus conventional Spatial model specifications: some comparisons.- 8. The role of Spatial of autocorrelation in prioritizing sites within a geographic landscape.- 9. General Spatial statistics conclusions.- 10. References: Spatial statistics (Part 1) Part 2. Non-standard Spatial Econometrics.- 11. Introduction: Spatial Econometrics.- 12. Mixed linear-logarithmetic specification for Lotka-Volterra models with endogenously generated SDLS-variables.- 13. Selecting Spatial regimes by threshold analysis.- 14. Finite automata.- 15 Learning from residuals.- 16. Verhulst and Poisson distributions.- 17. QUARLIREG: qualitative regression and its application to Spatial data.- 18. Filtering complexity for observational errors and Spatial bias.- 19. General Spatial Econometrics conclusions.- 20. References: Spatial Econometrics (Part 2).
