Description
Spatial Econometric Interaction Models
Description
Efficient estimation of spatial econometric models of origin-destination flows, which may exhibit spatial autocorrelation in the dependent variable, the explanatory variables or both. The model is the one proposed by LeSage and Pace (2008) <doi:10.1111/j.1467-9787.2008.00573.x>, who develop a matrix formulation that exploits the relational structure of flow data. The estimation procedures follow most closely those outlined by Dargel (2021) (preprint available at <https://www.tse-fr.eu/fr/publications/revisiting-estimation-methods-spatial-econometric-interaction-models>).
README.md
spflow
The spflow package allows to estimate spatial econometric interaction models. It is designed to exploit the relational structure of flow data, reducing the computational burden and memory requirements.
Installation
You can install development version from GitHub with:
# install.packages("devtools")
devtools::install_github("LukeCe/spflow")
Example
The package provides a new sp_multi_network-class that combines information on the origins, the destinations, and the origin-destination pairs. Once this object is created, we can estimate an interaction model with the spflow() function. The package vignette contains a more detailed example.
library("spflow")
spflow(y9 ~ . + G_(DISTANCE), multi_net_usa_ge)
#> --------------------------------------------------
#> Spatial interaction model estimated by: MLE
#> Autocorrelation structure: model_9 (SDM)
#> Observations: 256
#>
#> --------------------------------------------------
#> Coefficients:
#> est sd t.stat p.value
#> rho_d 0.48 0.03 15.75 0.02
#> rho_o 0.36 0.03 10.59 0.03
#> rho_w -0.25 0.04 -5.95 0.05
#> (Intercept) 10.00 1.92 5.21 0.06
#> (Intra) 11.32 3.07 3.69 0.08
#> DEST_X 0.94 0.06 14.97 0.02
#> DEST_X.lag1 0.62 0.11 5.80 0.05
#> ORIG_X -0.78 0.04 -21.20 0.02
#> ORIG_X.lag1 -0.32 0.08 -3.93 0.08
#> INTRA_X 1.95 0.08 23.98 0.01
#> INTRA_X.lag1 -0.34 0.19 -1.76 0.16
#> DISTANCE -2.80 0.34 -8.25 0.04
#>
#> --------------------------------------------------
#> R2_corr: 0.9927709