Estimation and inference of heterogeneous spatial autoregressive (HSAR) panel data models. These are models where the spatial lag coefficients are allowed to differ over the cross-section units, in addition to the fixed effects generally allowed for in the literature. The model also features weakly exogenous regressors, such as lagged values of the dependent variable and heteroskedastic error. The estimation is performed via quasi maximum-likelihood. See Aquaro, Bailey and Pesaran (2021) for technical details.
# install release version from CRAN
install.packages("hetsar")
# install development version from GitHub
devtools::install_github("maquaro/hetsar")library("hetsar")
data(hetsarDataDemo)
df_data <- hetsarDataDemo[["data"]]
m_C <- hetsarDataDemo[["network_matrix"]]
# row normalise
m_W <- m_C / rowSums(m_C)
# y = Wy + 1 + x + Wx + err
# other model specifications available, see `?hetsar`
out <- hetsar(
data = df_data,
W = m_W,
indices = c("id", "time"),
dependent = "y",
explanatory = "x",
space_lags = "x")
summary(out)
summary(out, MG = TRUE)Coming soon.
hetsar is also available in MATLAB (coming soon), Python, and Stata.
M. Aquaro, N. Bailey and M. H. Pesaran (2021). "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices". Journal of Applied Econometrics 36(1): 18-44. doi:https://doi.org/10.1002/jae.2792