impacts.stsls_sphet: Generate impacts for objects of class lag_gmm created in...

In gpiras/sphet: Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations

Generate impacts for objects of class lag_gmm created in sphet

Description

Generate impacts for objects of class lag_gmm created in sphet

Usage

## S3 method for class 'stsls_sphet'
impacts(
  obj,
  ...,
  tr = NULL,
  R = NULL,
  listw = NULL,
  evalues = NULL,
  tol = 1e-06,
  empirical = FALSE,
  Q = NULL,
  KPformula = FALSE,
  prt = TRUE
)

Arguments

obj	A spreg spatial regression object created by spreg with model ="lag"
...	Arguments passed through to methods in the coda package
tr	A vector of traces of powers of the spatial weights matrix created using trW, for approximate impact measures; if not given, listw must be given for exact measures (for small to moderate spatial weights matrices); the traces must be for the same spatial weights as were used in fitting the spatial regression
R	If given, simulations are used to compute distributions for the impact measures, returned as mcmc objects
listw	a listw object
evalues	vector of eigenvalues of spatial weights matrix for impacts calculations
tol	Argument passed to mvrnorm: tolerance (relative to largest variance) for numerical lack of positive-definiteness in the coefficient covariance matrix
empirical	Argument passed to mvrnorm (default FALSE): if true, the coefficients and their covariance matrix specify the empirical not population mean and covariance matrix
Q	default NULL, else an integer number of cumulative power series impacts to calculate if tr is given
KPformula	default FALSE, else inference of the impacts based on Kelejian and Piras (2020)
prt	prints the KP summary of the VC matrix

Value

Estimate of the Average Total, Average Direct, and Average Indirect Effects

References

Roger Bivand, Gianfranco Piras (2015). Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software, 63(18), 1-36. https://www.jstatsoft.org/v63/i18/. Harry Kelejian, Gianfranco Piras (2020). Spillover effects in spatial models: Generalization and extensions. Journal of Regional Science, 60(3), 425-442. Gianfranco Piras, Paolo Postiglione (2022). A deeper look at impacts in spatial Durbin model with sphet. Geographical Analysis, 54(3), 664-684.

Examples

require("sf", quietly=TRUE)
library(coda)
columbus <- st_read(system.file("shapes/columbus.gpkg", package="spData")[1], quiet=TRUE)
col.gal.nb <- spdep::read.gal(system.file("weights/columbus.gal", package="spData")[1])
listw <- spdep::nb2listw(col.gal.nb)
ev <- spatialreg::eigenw(listw)
W <- as(listw, "CsparseMatrix")
trMatc <- spatialreg::trW(W, type="mult")
trMC <- spatialreg::trW(W, type="MC")
#LAG
lobj_gm <- spreg(CRIME ~ INC + HOVAL, columbus, listw,
                model = "lag")
summary(lobj_gm)
lobj_gmh <- spreg(CRIME ~ INC + HOVAL, columbus, listw,
                 model = "lag", het = TRUE)
summary(lobj_gmh)
set.seed(1)
impacts(lobj_gm, listw=listw)
impacts(lobj_gm, tr=trMatc)
impacts(lobj_gm, tr=trMC)
impacts(lobj_gm, evalues=ev)
impacts(lobj_gmh, listw=listw)
impacts(lobj_gmh, tr=trMatc)
impacts(lobj_gmh, tr=trMC)
impacts(lobj_gmh, evalues=ev)
#same impacts but different SD
summary(impacts(lobj_gm, evalues = ev, R = 1000))
summary(impacts(lobj_gmh, evalues = ev, R = 1000))
lobjIQ5_gm <- impacts(lobj_gm, tr=trMatc, R=1000, Q=5)
summary(lobjIQ5_gm, zstats=TRUE, short=TRUE)
summary(lobjIQ5_gm, zstats=TRUE, short=TRUE, reportQ=TRUE)
# LAG durbin TRUE
mobj_gm <- spreg(CRIME ~ INC + HOVAL, columbus, listw, Durbin=TRUE,
                model = "lag")
summary(mobj_gm)
mobj_gmh <- spreg(CRIME ~ INC + HOVAL, columbus, listw, Durbin=TRUE,
                 model = "lag", het = TRUE)
mobj_gm2 <- spreg(CRIME ~ INC, columbus, listw, Durbin=TRUE,
                model = "lag")
summary(mobj_gmh)
impacts(mobj_gm, KPformula = TRUE)
impacts(mobj_gm2, KPformula = TRUE)
summary(impacts(mobj_gm2, evalues=ev, R=1000), short=TRUE, zstats=TRUE)
impacts(mobj_gm, listw=listw)
impacts(mobj_gm, tr=trMatc)
impacts(mobj_gm, tr=trMC)
impacts(mobj_gm, evalues=ev)
summary(impacts(mobj_gm, evalues=ev, R=1000), short=TRUE, zstats=TRUE)
impacts(mobj_gmh, listw=listw)
impacts(mobj_gmh, tr=trMatc)
impacts(mobj_gmh, tr=trMC)
impacts(mobj_gmh, evalues=ev)
summary(impacts(mobj_gmh, tr=trMatc, R=1000), short=TRUE, zstats=TRUE)
#lag durbin = ~formula
mobj1_gm <- spreg(CRIME ~ INC + HOVAL, columbus, listw, Durbin= ~ INC,
                 model = "lag")
mobj1_gmh <- spreg(CRIME ~ INC + HOVAL, columbus, listw, Durbin= ~ INC,
                  model = "lag", het = TRUE)
impacts(mobj1_gm, tr=trMatc)
impacts(mobj1_gm, listw=listw)
impacts(mobj1_gm, KPformula = TRUE)
summary(impacts(mobj_gm, evalues=ev, R=200), short=TRUE, zstats=TRUE)
summary(impacts(mobj1_gm, tr=trMatc, R=200), short=TRUE, zstats=TRUE)
mobj1_gm <- spreg(CRIME ~ HOVAL, columbus, listw, Durbin= ~ INC,
                 model = "lag")
summary(impacts(mobj1_gm, evalues=ev, R=200), short=TRUE, zstats=TRUE)

gpiras/sphet documentation built on June 11, 2025, 10:37 a.m.