kinktest: Testing the kink effect

In woraphonyamaka/Kinkreg: Kink regression model of Hansen (2018)

Testing the kink effect

Description

Hypothesis testing for kink point in kink regression. The null hyphothesis is linear regression against the alternative hyphothesis of kink regression.

Usage

kinktest(y,x,level=0.90, boot=10,search=0.01, LB)

Arguments

y	the vector of dependent variable
x	the matrix of regime-dependent independent variable
level	Confidence interval
boot	number of boostrapping
search	range of grid search
LB	lower and upper bound restriction

Details

As shown by Hansen (1996), it is simple to simulate approximations using a multiplier bootstrap, and thus asymptotically valid p-values can be calculated. The following is his recommended algorithm. (Theorem 3 of Hansen (1996) shows that the algorithm produces asymptotically first-order correct p-values under the conditions of Theorem 1.

Value

Wald.test	Wald statistic
p-value	p-value

Author(s)

Woraphon Yamaka

References

Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica: Journal of the econometric society, 413-430.

Hansen, B. E. (2017). Regression kink with an unknown threshold. Journal of Business & Economic Statistics, 35(2), 228-240.

Examples

#### Example Simulation data
# Ho: Linear
# Ha: Kink
set.seed(111)                  # Set seed for reproducibility
k = 1                         #number of dependent variable
n = 500                       #number of observation
r1=1.5                        # Kink parameter
x = rnorm(n,r1,sd=1)
e = rnorm(n,0,sd=1)
x1 = cbind(neg.part(x-r1),pos.part(x-r1))
y=0.5+(0.5*x1[,1])-(1*x1[,2])+e


kinktest(y,x,level=0.90, boot=10,search=0.01,LB=0.01)

woraphonyamaka/Kinkreg documentation built on Aug. 15, 2022, 4:17 a.m.