Special Issue "Spatial Econometrics"

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

Deadline for manuscript submissions: closed (30 June 2015)

Special Issue Editor

Guest Editor
Prof. Dr. Giuseppe Arbia (Website)

Faculty of Economics, Department of Statistics and Institute of Hygiene, Catholic University of Sacro Cuore

Special Issue Information

Special Issue Description

We are delighted to announce a new special issue on the subject of Spatial Econometrics, guest edited by Professor Giuseppe Arbia from the Catholic University of Sacro Cuore, Italy and Professor Lung-Fei Lee, Ohio State University, USA. This special issue is open for submissions as of 1 December 2013 until 31 March 2014. All submitted articles will undergo rigorous peer review, and in the event of acceptance, are ensured rapid publication and high visibility.

Note by Guest Editors

This special issue within the open access journal Econometrics will cover a broad range of topics in relation to Spatial Econometrics, including, but not limited to:

  • Spatial discrete choice models;
  • heteroskedastic spatial models;
  • static and dynamic spatial panel data models;
  • computation issues in big spatial datasets;
  • non stationary spatial models;
  • resampling issues spatial models;
  • bayesian spatial econometric models;
  • spatial limited dependent variable models;
  • spatial duration models;
  • spatial models with factor structures;
  • strong and weak dependence in spatial models;
  • endogenous spatial weights matrix in spatial models;
  • spatial models with expectation;
  • nonlinear spatial models.


Submissions can be made to this special issue as of 1 December 2013, and can be directly submitted online by registering at www.mdpi.com.

Prof. Dr. Giuseppe Arbia
Guest Editors

 

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a 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.

Published Papers (9 papers)

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Editorial

Jump to: Research

Open AccessEditorial Spatial Econometrics: A Rapidly Evolving Discipline
Econometrics 2016, 4(1), 18; doi:10.3390/econometrics4010018
Received: 28 January 2016 / Revised: 29 February 2016 / Accepted: 2 March 2016 / Published: 7 March 2016
Cited by 2 | PDF Full-text (150 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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]). [...] Full article
(This article belongs to the Special Issue Spatial Econometrics)

Research

Jump to: Editorial

Open AccessArticle Testing in a Random Effects Panel Data Model with Spatially Correlated Error Components and Spatially Lagged Dependent Variables
Econometrics 2015, 3(4), 761-796; doi:10.3390/econometrics3040761
Received: 6 August 2015 / Revised: 18 October 2015 / Accepted: 26 October 2015 / Published: 9 November 2015
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Abstract
We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM) test statistics for a variety of hypotheses within this model. We first [...] Read more.
We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM) test statistics for a variety of hypotheses within this model. We first construct the joint LM test for both the individual random effects and the two spatial effects (spatial error correlation and spatial lag dependence). We then provide LM tests for the individual random effects and for the two spatial effects separately. In addition, in order to guard against local model misspecification, we derive locally adjusted (robust) LM tests based on the Bera and Yoon principle (Bera and Yoon, 1993). We conduct a small Monte Carlo simulation to show the good finite sample performances of these LM test statistics and revisit the cigarette demand example in Baltagi and Levin (1992) to illustrate our testing procedures. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
Econometrics 2015, 3(4), 709-718; doi:10.3390/econometrics3040709
Received: 25 August 2015 / Revised: 16 October 2015 / Accepted: 20 October 2015 / Published: 29 October 2015
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Abstract
In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor [...] Read more.
In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle Strategic Interaction Model with Censored Strategies
Econometrics 2015, 3(2), 412-442; doi:10.3390/econometrics3020412
Received: 24 March 2015 / Revised: 5 May 2015 / Accepted: 21 May 2015 / Published: 1 June 2015
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Abstract
In this paper, we develop a new model of a static game of incomplete information with a large number of players. The model has two key distinguishing features. First, the strategies are subject to threshold effects, and can be interpreted as dependent [...] Read more.
In this paper, we develop a new model of a static game of incomplete information with a large number of players. The model has two key distinguishing features. First, the strategies are subject to threshold effects, and can be interpreted as dependent censored random variables. Second, in contrast to most of the existing literature, our inferential theory relies on a large number of players, rather than a large number of independent repetitions of the same game. We establish existence and uniqueness of the pure strategy equilibrium, and prove that the censored equilibrium strategies satisfy a near-epoch dependence property. We then show that the normal maximum likelihood and least squares estimators of this censored model are consistent and asymptotically normal. Our model can be useful in a wide variety of settings, including investment, R&D, labor supply, and social interaction applications. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model
Econometrics 2015, 3(2), 376-411; doi:10.3390/econometrics3020376
Received: 3 March 2015 / Revised: 12 May 2015 / Accepted: 14 May 2015 / Published: 21 May 2015
Cited by 2 | PDF Full-text (417 KB) | HTML Full-text | XML Full-text
Abstract
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence [...] Read more.
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as it leads potentially to much improved inferences for the regression coefficients. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe, and the proposed bias correction procedure is very effective. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle The SAR Model for Very Large Datasets: A Reduced Rank Approach
Econometrics 2015, 3(2), 317-338; doi:10.3390/econometrics3020317
Received: 12 January 2015 / Revised: 17 April 2015 / Accepted: 29 April 2015 / Published: 11 May 2015
Cited by 2 | PDF Full-text (7847 KB) | HTML Full-text | XML Full-text
Abstract
The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random [...] Read more.
The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random effects (SRE) model, and in this article, we calibrate it to the SAR model of interest using a generalisation of the Moran operator that allows for heteroskedasticity and an asymmetric SAR spatial dependence matrix. In general, spatial data have a measurement-error component, which we model, and we use restricted maximum likelihood to estimate the SRE model covariance parameters; its required computational time is only the order of the size of the dataset. Our implementation is demonstrated using mean usual weekly income data from the 2011 Australian Census. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle Two-Step Lasso Estimation of the Spatial Weights Matrix
Econometrics 2015, 3(1), 128-155; doi:10.3390/econometrics3010128
Received: 31 October 2014 / Accepted: 11 February 2015 / Published: 9 March 2015
Cited by 3 | PDF Full-text (434 KB) | HTML Full-text | XML Full-text
Abstract
The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial [...] Read more.
The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term
Econometrics 2015, 3(1), 101-127; doi:10.3390/econometrics3010101
Received: 14 October 2014 / Accepted: 27 January 2015 / Published: 26 February 2015
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Abstract
In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average [...] Read more.
In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of parameters of exogenous variables is inconsistent and determine its asymptotic bias. I provide simulation results to evaluate the performance of the MLE. The simulation results indicate that the MLE imposes a substantial amount of bias on both autoregressive and moving average parameters. Full article
(This article belongs to the Special Issue Spatial Econometrics)
Open AccessArticle The Biggest Myth in Spatial Econometrics
Econometrics 2014, 2(4), 217-249; doi:10.3390/econometrics2040217
Received: 29 September 2014 / Revised: 18 November 2014 / Accepted: 8 December 2014 / Published: 23 December 2014
Cited by 14 | PDF Full-text (403 KB) | HTML Full-text | XML Full-text
Abstract
There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are [...] Read more.
There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of misspecified models. Full article
(This article belongs to the Special Issue Spatial Econometrics)

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