This R package performs nonparametric tests of parametric specifications. Five heteroskedasticity-robust tests are available: Bierens (1982), Zheng (1996), Escanciano (2006), Lavergne and Patilea (2008), and Lavergne and Patilea (2012). Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap.
Hippolyte Boucher (Hippolyte.Boucher@outlook.com) is the author of SpeTestNP and Pascal Lavergne (lavergnetse@gmail.com) is a contributor. Both Hippolyte Boucher and Pascal Lavergne are maintainers and any question or bug should be reported to one of them.
To install SpeTestNP from CRAN simply run the following command:
install.packages("SpeTestNP")
To install SpeTestNP from Github the package devtools should be installed and the following commands should be run:
library(devtools)
install_github("HippolyteBoucher/SpeTestNP")
SpeTestNP requires the packages stats (already installed and loaded by default in Rstudio), foreach and doParallel (if parallel computing is used to derive p-values based on the bootstrap) to be installed.
library(SpeTestNP)
### Create artificial data, choose a parametric specification, and perform the test
# Example 1: The true model is linear and we estimate the model linearly
n<-500
x<-rnorm(n)
y<-x+rnorm(n)*0.7
eq<-lm(y~x+0)
# The test of Bierens (1982) is used with 300 wild bootstrap samples used
# to compute the p-value
SpeTest(eq=eq,type="icm",rejection="bootstrap",boot="wild",nboot=300)
# Example 2: The true model is nonlinear and we estimate the model linearly
n<-500
x<-rnorm(n)
y<-x^2+rnorm(n)*0.7
eq<-lm(y~x+0)
# The test of Lavergne and Patilea (2012) is used with a p-value based on the
# asymptotic distribution of the normalized test statistic under the null hypothesis
summary(SpeTest(eq=eq,type="sicm",rejection="asymptotics"))
H.J. Bierens (1982), "Consistent Model Specification Test", Journal of Econometrics, 20 (1), 105-134
J. C. Escanciano (2006), "A Consistent Diagnostic Test for Regression Models Using Projections", Econometric Theory, 22 (6), 1030-1051
P. Lavergne and V. Patilea (2008), "Breaking the Curse of Dimensionality in Nonparametric Testing", Journal of Econometrics, 143 (1), 103-122
P. Lavergne and V. Patilea (2012), "One for All and All for One: Regression Checks with Many Regressors", Journal of Business & Economic Statistics, 30 (1), 41-52
J.X. Zheng (1996), "A Consistent Test of Functional Form via Nonparametric Estimation Techniques", Journal of Econometrics, 75 (2), 263-289