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List:       r-sig-geo
Subject:    [R-sig-Geo] Adjusting for HAC in splm models
From:       Denys Dukhovnov via R-sig-Geo <r-sig-geo () r-project ! org>
Date:       2023-11-21 21:55:06
Message-ID: 1993974628.6736544.1700603706230 () mail ! yahoo ! com
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Dear community,

I am trying to interpret and report the results of my spatial panel analysis, but I \
am running into issues while attempting to accommodate for spatial and serial \
autocorrelation and clustering in the errors.

What implemented solutions currently exist in R for heteroskedasticity and \
autocorrelation correction (HAC) and/or standard error clustering for spatial panel \
models? Beyond the robust that could be run with non-spatial plm, fixest, or lfe \
packages, I have not been able to find an analogous HAC procedure for spatial panel \
models. The typical lmtest::coeftest() with sandwich vcov argument do not work \
directly on splm objects.  

A typical error on application of lmtest:coeftest(splm.obj, .vcov = \
vcovHAC(splm.obj)) is this: Error in UseMethod("estfun") :  no applicable method for \
'estfun' applied to an object of class "c('splm_ML', 'splm', 'splm_ML')"

However, the splm documentation says that splm::vcov.splm() function is able to \
extract vcov matrix for interoperability with lmtest functions. How would I go about \
doing it?

I consulted a fairly recent paper on software for spatial panel analysis by Bivand, \
Millo & Piras (2021), but there is little in the way of discussion on how to adjust \
the standard errors for spatial and serial autocorrelation and clustering.

I am also aware of Conley spatial errors (Conley (1999)), implemented in fixest \
package for example, but these tend to require latitude and longitude as inputs \
(which is fine, if my inputs were points). But my dataset consists of polygons of \
various sizes where reducing them to a centroid to compute a distance matrix becomes \
theoretically untenable. I would prefer to stick with adjacency/contiguity weights.

I am running R version 4.2.2 (splm v.1.6.3). Below is a simple reproducible example \
with similar outcomes:

library(plm)
library(spdep)
library(splm)
library(sandwich)
library(lmtest)

# Read in the data and spatial weights
data("RiceFarms")
data("riceww")

# Convert into panel data frame and convert spatial weights into list form
RiceFarms <- pdata.frame(RiceFarms, index = c("id", "time"))
listw <- mat2listw(riceww)

# Pre-compute lag for variables for use in Durbin specifications
RiceFarms$slag.pesticide <- slag(RiceFarms$pesticide, listw = listw)
RiceFarms$slag.wage <- slag(RiceFarms$wage, listw = listw)

# PLM model
plm.mod <- plm(goutput ~ pesticide + wage,data = RiceFarms,  
                       model = "within",  
                       effect = "twoways",  
                       index = c("id", "time"))

# SDEM model
sdem.mod <- spml(goutput ~ pesticide + wage + slag.pesticide + slag.wage,
                          data = RiceFarms,  
                          listw = listw,
                          spatial.error = "b",
                          lag = FALSE,
                          index = c("id", "time"),  
                          model = "within",  
                          effect = "twoways")

# Apply robust SE corrections
lmtest::coeftest(plm.mod, vcov. = vcovHC(plm.mod, method = "arellano", type = "HC0"))

# Neither works with splm object
lmtest::coeftest(sdem.mod, vcov. = vcovHC(sdem.mod, method = "arellano", type = \
"HC0")) lmtest::coeftest(sdem.mod, vcov. = sandwich::vcovHAC(sdem.mod))


A follow-up question is how to deal with HAC standard error corrections for the \
impacts, given that spatial panel impacts function are not directly implemented in \
splm as of version 1.6.3, although the coefficients can be computed as per the \
previous posts on the subject:

https://stat.ethz.ch/pipermail/r-sig-geo/2016-April/024326.html
https://stat.ethz.ch/pipermail/r-sig-geo/2019-July/027513.html

Any help would be much appreciated.


Best,
Denys Dukhovnov

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