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Methods for detecting structural breaks, determining the number of breaks, and estimating break locations in linear quantile regression. The package implements approaches based on Qu (2008) and Oka and Qu (2011), supporting both single and multiple quantiles analysis for time series and repeated cross-sectional data.
You can install the development version from GitHub with:
install.packages("QR.break")This basic example shows how to detect structural breaks in a quantile regression model:
library(QR.break)
# Example 1
# Time series example, using US GDP data
# Data
data(gdp)
y = gdp[,"gdp"]
x = gdp[, c("lag1", "lag2")]
# Quantiles
vec.tau = seq(0.20, 0.80, by = 0.150)
N = 1
trim.e = 0.15
vec.time = gdp[,"yq"]
m.max = 3
v.a = 2
v.b = 2
options(warn=-1) #sometimes fit is non-unique
## Structural breaks in quantile regression
result = rq.break(y, x, vec.tau, N, trim.e, vec.time, m.max, v.a, v.b)
print(result)
## Example 2
## Repeated cross-section example, using youth driving data
data(driver)
Driving_data<-driver
y <- Driving_data[,"bac"]
x <- Driving_data[, c("age", "gender", "winter")]
vec.tau = seq(0.70, 0.85, 0.05)
N <- 108
trim.e <- 0.05
vec.time <- unique(Driving_data[,"yq"])
m.max <- 3
v.a <-2
v.b <-2
options(warn=-1) #sometimes fit is non-unique
result <- rq.break(y, x, vec.tau, N, trim.e, vec.time, m.max, v.a, v.b)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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