Comparing Implementations of Estimation Methods for Spatial Econometrics

By Roger S. Bivand, Gianfranco Piras. 2015

931 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/29ac4f63d4c0b5f5dce3704240f47a781cb010d4

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In Economics and Econometrics

Citations in the corpus

Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction (2020, methodology): “It is readily available in the R packages spmoran (Murakami, 2018) and spatialreg (Bivand and Piras, 2015).”
Mountains, climate and niche heterogeneity explain global patterns of fern diversity (2021, methodology): “We initially conducted an ordinary least squares (OLS) regression and then to account for spatial autocorrelation we used a simultaneous autoregressive (SAR) lag model (Kissling & Carl, 2007) implemented using the ‘spdep’ package (Bivand & Piras, 2015).”
tmap: Thematic Maps in R (2018, methodology): “…multiples, htmlwidgets (Vaidyanathan, Xie, Allaire, Cheng, and Russell 2016) for further processing interactive maps, and htmltools ( RStudio Inc. 2017) for processing HTML. Finally, the package spdep (Bivand, Hauke, and Kossowski 2013; Bivand and Piras 2015) is used to ﬁnd neighboring polygons.”
Spatial modelling with R-INLA: A review (2018, methodology): “…not give a detailed introduction of R-INLA, which is found in Rue et al. (2017), or how to program spatial models in R-INLA, which is found in Lindgren and Rue (2015); Blangiardo and Cameletti (2015); Bivand et al. (2015); G´omez-Rubio et al. (2014); Krainski et al. (2017) and at www.r-inla.org .”
Population Bias in Geotagged Tweets (2015, methodology): “For management of spatial data and implementation of computation and estimation for spatial models, we used the R package spdep (Bivand and Piras 2015; Bivand, Hauke, and Kossowski 2013).”
About predictions in spatial autoregressive models: optimal and almost optimal strategies (2017, methodology): “All the prediction formulas cited in our paper are implemented in the releases later than 0.5–92 of the spdep R package (Bivand & Piras, 2015), following the GSoC 2015 project by M. Gubri.1 The second contribution is to present simulation experiment results as in Kato (2008, 2013), which yield a…”
Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments (2019, neutral): “All spatial models were estimated using R’s package spdep (Bivand and Piras 2015).”
Multi‐scale habitat modelling identifies spatial conservation priorities for mainland clouded leopards (Neofelis nebulosa) (2019, background): “We fur‐ ther tested for and found very low levels of spatial autocorrela‐ tion (Moran's ̄I = 0.02; Table 1; ‘spdep’ r package; Bivand & Piras, 2015), thereby validating the assumption of independence among samples.”
Spatial data analysis (2024, neutral): “For more details see Bivand & Piras (2015), LeSage & Pace (2009), and Sarrias (2023).”
On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models (2017, methodology): “The Columbus data are found in the spdep package (Bivand et al., 2013; Bivand and Piras, 2015) 327 for R (R Core Team, 2016).”