estimateW

This is the development repository of the `R` package `estimateW`.

Features

The package provides methods to estimate spatial weight matrices in spatial autoregressive type models.

Installation

Type into your `R` session:

``````if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github(
repo = "https://github.com/tkrisztin/estimateW")``````

Demonstration

``````# Load the package
library(estimateW)
require(dplyr)

tt = length(unique(covid\$date))
n = length(unique(covid\$ISO3))

# reorder by date and longitude
covid = covid %>%
arrange(date, LON) %>%
mutate(date = as.factor(date))

# Benchmark specification from Krisztin and Piribauer (2022) SEA
Y = as.matrix(covid\$infections_pc - covid\$infections_pc_lag)
X = model.matrix(~infections_pc_lag + stringency_2weekly +
precipProbability + temperatureMax + ISO3 + as.factor(date) + 0,data = covid)

# use a flat prior for W
flat_W_prior = W_priors(n = n,nr_neighbors_prior = rep(1/n,n))

# Estimate a Bayesian model using covid infections data
res = sarw(Y = Y,tt = tt,Z = X,niter = 200,nretain = 50,
W_prior = flat_W_prior)

# Plot the posterior of the spatial weight matrix
dimnames(res\$postw)[[2]] = dimnames(res\$postw)[[1]] = covid\$ISO3[1:n]
plot(res,font=3,cex.axis=0.75,las=2)``````

References

Tamás Krisztin & Philipp Piribauer (2022) A Bayesian approach for the estimation of weight matrices in spatial autoregressive models, Spatial Economic Analysis, DOI: `10.1080/17421772.2022.2095426`