Special Issue "Recent developments in Spatial Econometrics, associated with the 10th Annual Conference of the Spatial Econometrics Association, Rome 13-15 June 2016"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 January 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Giuseppe Arbia

Faculty of Economics, Department of Statistics and Institute of Hygiene, Catholic University of Sacro Cuore
Website | E-Mail
Interests: spatial econometrics; spatial statistics

Special Issue Information

Dear Colleagues,

The Spatial Econometrics Association was created in 2006 by the seven co-founders Anselin, Arbia, Baltagi, Keleijan Robinson, Paelinck, and Prucha, and currently counts more than 100 members in the five continents. The past Annual Meetings were hosted in prestigious venues, such as Rome, Cambridge, New York, Barcelona, Chicago, Toulouse, Salvador de Bahia, Washington DC, Zürich, and Miami, attracting an average of about 80 papers a year. From 13–15 June, 2016, in order to celebrate the 10th anniversary of the Association, the conference will be held again in Rome. It is a tradition that, at each conference, the best papers are published in Special Issues of scientific journals. In the past few years the papers were published in journals such as Empirical Economics, Papers in Regional Science, Economic Modeling, Regional Science & Urban Economics, International Regional Science Review, Spatial Economic Analysis, Geographical Analysis, Review of Regional Studies, and Annals in Regional Science. The focus of the 2016 SEA World Conference in Rome will be on methodological themes, such as discrete choice spatial modeling, spatial panel data, spatial concentration, spatial models of duration, explanatory spatial data analysis, spatial Heteroskedastic models, Bayesian methods, big spatial data and spatial data mining, spatial microeconometric methods, and spatial and social network effects. It will also plan to attract empirical contributions related to topics such as Economic Growth, Knowledge Diffusion, Labor Market and Migration, Health, Criminology, Environmental Economics and Energy and House market. Papers containing original empirical applications (but not mere case studies that make use of existing methods) will also be considered for publication in the Special Issue. The purpose of this Special Issue is to contribute to the development of new methods in spatial econometrics and to their rapid diffusion in the scientific community.

There are currently no specialized journals on spatial econometrics so contributions are scattered throughout different journals. Methodological papers are usually published in journals like the Journal of Applied Econometrics, Journal of Econometrics, Econometric Theory, Econometrics Journal, Spatial Economic Analysis, Regional Science and Urban Economics, while more applied papers are hosted in journal like Papers in Regional Science, Geographical Analysis, Geographical Systems, and International Regional Science Review. On average, more than 20 papers a year are published on the subject. This Special Issue can intercept an important share of these publications associated to the Annual Meeting of the Spatial Econometrics Association.

Prof. Dr. Giuseppe Arbia
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

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Keywords

  • spatial discrete choice models
  • spatial panel data models
  • big spatial data modeling
  • spatial and social network effects
  • Bayesian spatial econometric models
  • spatial duration models
  • spatial Microeconometrics

Published Papers (3 papers)

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Research

Open AccessArticle
A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade
Received: 23 September 2016 / Revised: 10 January 2018 / Accepted: 12 February 2018 / Published: 22 February 2018
Cited by 2 | PDF Full-text (287 KB) | HTML Full-text | XML Full-text
Abstract
Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each [...] Read more.
Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each other due to spatial and network autocorrelation, these methods may lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering (ESF) variants of the Poisson/negative binomial specifications have been proposed in the literature on gravity modelling of trade. However, no specific treatment has been developed for cases in which many zero flows are present. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters that is based on robust (sandwich) p-values and does not require likelihood-based indicators. In this respect, we develop an ad hoc backward stepwise function in R. Second, using this function, we select a reduced set of spatial filters that properly accounts for importer-side and exporter-side specific spatial effects, as well as network effects, both at the count and the logit processes of zero-inflated methods. Applying this estimation strategy to a cross-section of bilateral trade flows between a set of 64 countries for the year 2000, we find that our specification outperforms the benchmark models in terms of model fitting, both considering the AIC and in predicting zero (and small) flows. Full article
Open AccessArticle
The Turkish Spatial Wage Curve
Econometrics 2017, 5(3), 37; https://doi.org/10.3390/econometrics5030037
Received: 31 January 2017 / Revised: 25 July 2017 / Accepted: 17 August 2017 / Published: 29 August 2017
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Abstract
The wage curve for Turkey revisited considering the spatial spillovers of the regional unemployment rates using individual level data for a period of 2004–2013 at the 26 NUTS-2 level by employing FE-2SLS models. The unemployment elasticity of real wages is −0.07 without excluding [...] Read more.
The wage curve for Turkey revisited considering the spatial spillovers of the regional unemployment rates using individual level data for a period of 2004–2013 at the 26 NUTS-2 level by employing FE-2SLS models. The unemployment elasticity of real wages is −0.07 without excluding any group of workers unlike previous studies. There is strong evidence on spatial effects of unemployment rate of contiguous regions on wage level, which is larger, in absolute value, than the effect of own-regional unemployment rate, −0.087 and −0.056, respectively. Male workers are slightly more responsive to the own-region unemployment rate than female workers. However, female workers are more responsive to the neighboring regions’ unemployment rate. Furthermore, using group-specific unemployment rates in the estimation of the wage curve for various groups, we find that unemployment elasticity of pay for female workers has become smaller and lost its significance, whereas unemployment elasticity for male workers has changed slightly. However, introducing group-specific unemployment rate results in losing significance in estimates for female workers. The findings in this paper suggest that individual wages are more responsive to the unemployment rates of the proximate regions than that of an individual’s own region. Also, the wage curve estimates are sensitive to the group-specific unemployment rates. Full article
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Open AccessArticle
A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line
Econometrics 2017, 5(2), 24; https://doi.org/10.3390/econometrics5020024
Received: 31 January 2017 / Revised: 30 May 2017 / Accepted: 31 May 2017 / Published: 16 June 2017
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Abstract
The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health [...] Read more.
The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health services. This study aims to describe and evaluate the use of LS24. Since for LS24 data, the location attribute is an important source of information to describe its use, this study analyses the number of calls received, at a municipal level, under two different spatial econometric approaches. This analysis is important for future development of decision support indicators in a hospital context, based on the economic impact of the use of this health line. Considering the discrete nature of data, the number of calls to LS24 in each municipality is better modelled by a Poisson model, with some possible covariates: demographic, socio-economic information, characteristics of the Portuguese health system and development indicators. In order to explain model spatial variability, the data autocorrelation can be explained in a Bayesian setting through different hierarchical log-Poisson regression models. A different approach uses an autoregressive methodology, also for count data. A log-Poisson model with a spatial lag autocorrelation component is further considered, better framed under a Bayesian paradigm. With this empirical study we find strong evidence for a spatial structure in the data and obtain similar conclusions with both perspectives of the analysis. This supports the view that the addition of a spatial structure to the model improves estimation, even in the case where some relevant covariates have been included. Full article
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