Advances in Spatial Econometrics: Parametric vs. Semiparametric Spatial Autoregressive Models

In this Chapter we provide a critical review of parametric and semiparametric spatial econometric approaches. We focus on the capability of each class of models to fit the main features of spatial data (such as strong and weak cross-sectional dependence, spatial heterogeneity, nonlinearities, and time persistence), leaving aside the technicalities related to the estimation methods. We also provide a brief discussion of the existent software developed to estimate most of the econometric models exposed in this Chapter.

[1]  Dongwoo Kang,et al.  Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach , 2016, J. Geogr. Syst..

[2]  Paul H. C. Eilers,et al.  Twenty years of P-splines , 2015 .

[3]  M. Hashem Pesaran,et al.  A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence , 2016 .

[4]  H. Kelejian,et al.  Estimation of Spatial Regression Models with Autoregressive Errors by Two-Stage Least Squares Procedures: A Serious Problem , 1997 .

[5]  J. Elhorst,et al.  A regional unemployment model simultaneously accounting for serial dynamics, spatial dependence and common factors , 2016 .

[6]  Giovanni Millo,et al.  Maximum likelihood estimation of spatially and serially correlated panels with random effects , 2014, Comput. Stat. Data Anal..

[7]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[8]  M. Durbán,et al.  Spatio-Temporal Autoregressive Semiparametric Model for the analysis of regional economic data , 2016 .

[9]  Gianfranco Piras,et al.  splm: Spatial Panel Data Models in R , 2012 .

[10]  Guido Rossum,et al.  Python Reference Manual , 2000 .

[11]  Antonio Musolesi,et al.  Weak and Strong cross-sectional dependence : a panel data analysis of international technology di ↵ usion , 2015 .

[12]  Lung-fei Lee,et al.  A SPATIAL DYNAMIC PANEL DATA MODEL WITH BOTH TIME AND INDIVIDUAL FIXED EFFECTS , 2009, Econometric Theory.

[13]  Elisa Tosetti,et al.  Large Panels with Common Factors and Spatial Correlations , 2007, SSRN Electronic Journal.

[14]  M. Durbán,et al.  Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities , 2014 .

[15]  Roland K. Roberts,et al.  Moderating urban sprawl: is there a balance between shared open space and housing parcel size? , 2010 .

[16]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[17]  Sergio J. Rey,et al.  PySAL: A Python Library of Spatial Analytical Methods , 2010 .

[18]  Giuseppe Arbia,et al.  A primer for Spatial Econometrics , 2014 .

[19]  Kazuaki Miyamoto,et al.  A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 2. Spatial Association and Model Specification Tests , 2002 .

[20]  Antonio Musolesi,et al.  Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion , 2017 .

[21]  Guangqing Chi,et al.  Applied Spatial Data Analysis with R , 2015 .

[22]  Madina Kukenova,et al.  Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation , 2008 .

[23]  Dae-Jin Lee,et al.  P-spline ANOVA-type interaction models for spatio-temporal smoothing , 2011 .

[24]  Youngihn Kho,et al.  GeoDa: An Introduction to Spatial Data Analysis , 2006 .

[25]  L. Fahrmeir,et al.  Software for Bayesian Inference in Structured Additive Regression Models Methodology Manual Acknowledgements Licensing Agreement Structured Additive Regression Based on Mcmc Simulation , 2022 .

[26]  L. Anselin Spatial Econometrics: Methods and Models , 1988 .

[27]  Roger Bivand,et al.  Comparing Implementations of Estimation Methods for Spatial Econometrics , 2015 .

[28]  M. Pesaran,et al.  Weak and Strong Cross-Section Dependence and Estimation of Large Panels , 2009, SSRN Electronic Journal.

[29]  D. McMillen,et al.  Perspectives on Spatial Econometrics: Linear Smoothing with Structured Models , 2012 .

[30]  Lung-fei Lee,et al.  An efficient GMM estimator of spatial autoregressive models , 2010 .

[31]  R. Bivand Spatial Dependence: Weighting Schemes, Statistics and Models , 2015 .

[32]  Lung-fei Lee,et al.  Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large , 2008 .

[33]  Tx Station Stata Statistical Software: Release 7. , 2001 .

[34]  Lung-fei Lee,et al.  Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models , 2004 .

[35]  J. LeSage Introduction to spatial econometrics , 2009 .

[36]  Martin Charlton,et al.  The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models , 2013, Geo spatial Inf. Sci..

[37]  R. Crescenzi,et al.  Econometric modelling of the regional knowledge production function in Europe , 2015 .

[38]  E. Pebesma,et al.  Classes and Methods for Spatial Data , 2015 .

[39]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[40]  Román Mínguez,et al.  SAR models with nonparametric spatial trends . A P-spline approach , 2012 .

[41]  Davide Martinetti,et al.  A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models , 2017, Regional Science and Urban Economics.

[42]  R. Blundell,et al.  Initial Conditions and Moment Restrictions in Dynamic Panel Data Models , 1998 .

[43]  Achim Zeileis,et al.  Structured Additive Regression Models: An R Interface to BayesX , 2015 .

[44]  M. Pesaran Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure , 2004, SSRN Electronic Journal.

[45]  J. LeSage,et al.  Interpreting dynamic space-time panel data models , 2012 .

[46]  David M. Kreps,et al.  Advances In Economics and Econometrics: Theory And Applications: Seventh World Congress , 1997 .

[47]  Harry H. Kelejian,et al.  On the asymptotic distribution of the Moran I test statistic with applications , 2001 .

[48]  Martin Charlton,et al.  GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models , 2013, 1306.0413.

[49]  Kunpeng Li,et al.  Spatial Panel Data Models with Common Shocks , 2013 .

[50]  Lung-fei Lee,et al.  Estimation of spatial autoregressive panel data models with fixed effects , 2010 .

[51]  Gerhard Tutz,et al.  Variable Selection and Model Choice in Geoadditive Regression Models , 2009, Biometrics.

[52]  A. Páez,et al.  A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity , 2002 .

[53]  T. Kneib,et al.  BayesX: Analyzing Bayesian Structural Additive Regression Models , 2005 .

[54]  Richard Blundell,et al.  Endogeneity in Nonparametric and Semiparametric Regression Models , 2022 .

[55]  J. Paul Elhorst,et al.  Matlab Software for Spatial Panels , 2014 .

[56]  Gianfranco Piras,et al.  sphet: Spatial Models with Heteroskedastic Innovations in R , 2010 .

[57]  Chris Brunsdon,et al.  An introduction to R for spatial analysis & mapping , 2015 .

[58]  Paul Elhorst,et al.  The Impact of Interaction Effects among Neighbouring Countries on Financial Liberalization and Reform: A Dynamic Spatial Panel Data Approach , 2013 .