dq.test.lvsl_1: Sequential Test for Additional Breaks within a Range of...

In QR.break: Structural Breaks in Quantile Regression

Sequential Test for Additional Breaks within a Range of Quantiles

Description

This function performs a sequential test to determine whether the number of breaks in a quantile regression model should be increased from L to L+1 using multiple quantiles.

Usage

dq.test.lvsl_1(y, x, q.L, q.R, n.size = 1, vec.date)

Arguments

y	A numeric vector of dependent variables (NT \times 1).
x	A numeric matrix of regressors (NT \times p).
q.L	A numeric value specifying the lower bound of the quantile range.
q.R	A numeric value specifying the upper bound of the quantile range.
n.size	An integer specifying the size of cross-sections (N).
vec.date	A numeric vector (L \times 1) of estimated break dates under the null hypothesis.

Details

This procedure tests for the existence of L breaks against L+1 breaks based on multiple quantiles: H_0: L breaks vs. H_1: L+1 breaks.

Value

A numeric value representing the DQ test statistic.

References

Qu, Z. (2008). Testing for Structural Breaks in Regression Quantiles. Journal of Econometrics, 146(1), 170-184.

Examples

# Load data
data(gdp)
y = gdp$gdp
x = gdp[,c("lag1", "lag2")]

# Set quantile range (left and right limits)
q.L = 0.2
q.R = 0.8

# Set N parameter
n.size = 1

# Specify break date under H_0
vec.date = 146

# Run the test
result = dq.test.lvsl_1(y, x, q.L, q.R, n.size, vec.date)
print(result)

QR.break documentation built on June 8, 2025, 1:53 p.m.