Abstract
The present volume showcased a series of papers related to some of the most recent developments in the field of spatial econometric methods applied to spatial interaction modelling. In particular, this book was motivated by the need to testify, through a collection of methodological and empirical studies, how the various approaches that have been present in this field in the last decades have recently developed, by including tools that are typical of spatial statistics and spatial econometrics, giving birth to a somewhat novel discipline characterized by a body of methods and techniques known under the heading of spatial econometric interaction models (LeSage and Pace 2009).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arbia G (2014a) Pairwise likelihood inference for spatial regressions estimated on very large datasets. Spat Stat 7:21–39
Arbia G (2014b) A primer for spatial econometrics: with applications in R. Palgrave Macmillan, New York
Arbia G, Lafratta G (2005) Exploring nonlinear spatial dependence in the tails. Geogr Anal 37(4):423–437
Arbia G, Bee M, Espa G, Santi F (2015) Fitting spatial econometric models through the unilateral approximation. DEM Discussion Papers 2014/08, University of Trento, Trento
Burger M, van Oort F, Linders GJ (2009) On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation. Spat Econ Anal 4(2):167–190
Griffith DA (2010) The Moran coefficient for non-normal data. J Stat Plann Inference 140(11):2980–2990
Isard W (1960) Methods of regional analysis. MIT Press, Cambridge
Jacqmin-Gadda H, Commenges D, Nejjari C, Dartigues JF (1997) Tests of geographical correlation with adjustment for explanatory variables: an application to dyspnoea in the elderly. Stat Med 16(11):1283–1297
Kelejian HH, Prucha IR (2007) HAC estimation in a spatial framework. J Econom 140(1):131–154
Kelejian HH, Prucha IR (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. J Econom 157(1):53–67
LeSage JP, Kelley Pace R (2007) A matrix exponential spatial specification. J Econom 140(1):190–214
LeSage JP, Pace RK (2009) Introduction to spatial econometrics. CRC Press, Boca Raton
Lin G, Zhang T (2007) Loglinear residual tests of Moran’s I autocorrelation and their applications to Kentucky breast cancer data. Geogr Anal 39(3):293–310
Metulini R, Patuelli R, Griffith DA (2015) Estimating a spatial filtering gravity model for bilateral trade: functional specifications and estimation challenges. Paper presented at the European Trade Study Group 2015, Paris
Polasek W, Llano C, Sellner R (2012) Bayesian methods for completing data in spatial models. Rev Econ Anal 2(2):192–214
Tinbergen J (1962) Shaping the world economy: suggestions for an international economic policy. Twentieth Century Fund, New York
Trang HTT, Arbia G, Miyata Y (2016) The analysis of commodity flows in San-En-Nanshin region (Japan) with heteroskedastic spatial interaction models. Mimeo
Wilson AG (1970) Entropy in urban and regional modelling. Pion, London
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Arbia, G., Patuelli, R. (2016). Conclusions: The Future of Spatial Interaction Modelling. In: Patuelli, R., Arbia, G. (eds) Spatial Econometric Interaction Modelling. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-30196-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-30196-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30194-5
Online ISBN: 978-3-319-30196-9
eBook Packages: Economics and FinanceEconomics and Finance (R0)