Panel Quantile Autoregressive Distributed Lag Model for R
The xtpqardl package provides functions for estimating Panel Quantile ARDL
(PQARDL) models. It combines the panel ARDL methodology of Pesaran, Shin, and
Smith (1999) with quantile regression to allow for heterogeneous effects across
the conditional distribution of the response variable.
# Install from CRAN (when available)
install.packages("xtpqardl")
# Or install development version from GitHub
library(xtpqardl)
# Load example data
data(pqardl_sample)
# Estimate PQARDL model at multiple quantiles
fit <- xtpqardl(
formula = d_y ~ d_x1 + d_x2,
data = pqardl_sample,
id = "country",
time = "year",
lr = c("L_y", "x1", "x2"),
tau = c(0.25, 0.50, 0.75),
model = "pmg"
)
# View results
summary(fit)
# Test parameter equality across quantiles
wald_test(fit)
# Compute impulse response function
irf <- compute_irf(fit, horizon = 20)
print(irf)
Pesaran MH, Shin Y, Smith RP (1999). "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels." Journal of the American Statistical Association, 94(446), 621-634. doi:10.1080/01621459.1999.10474156
Cho JS, Kim TH, Shin Y (2015). "Quantile Cointegration in the Autoregressive Distributed-Lag Modeling Framework." Journal of Econometrics, 188(1), 281-300. doi:10.1016/j.jeconom.2015.05.003
Bildirici M, Kayikci F (2022). "Uncertainty, Renewable Energy, and CO2 Emissions in Top Renewable Energy Countries: A Panel Quantile Regression Approach." Energy, 247, 124303.
GPL-3