OptSig.Boot: Optimal Significance Level for the F-test using the bootstrap

In OptSig: Optimal Level of Significance for Regression and Other Statistical Tests

Optimal Significance Level for the F-test using the bootstrap

Description

The function calculates the optimal level of significance for the F-test

The bootstrap can be conducted using either iid resampling or wild bootstrap.

Usage

OptSig.Boot(y,x,Rmat,rvec,p=0.5,k=1,nboot=3000,wild=FALSE,Figure=TRUE)

Arguments

y	a matrix of dependent variable, T by 1
x	a matrix of K independent variable, T by K
Rmat	a matrix for J restrictions, J by (K+1)
rvec	a vector for restrictions, J by 1
p	prior probability for H0, default is p = 0.5
k	relative loss from Type I and II errors, k = L2/L1, default is k = 1
nboot	the number of bootstrap iterations, the default is 3000
wild	if TRUE, wild bootsrap is conducted; if FALSE (default), bootstrap is based on iid residual resampling
Figure	show graph if TRUE (default). No graph otherwise

Details

See Kim and Choi (2020)

Value

alpha.opt	Optimal level of significance
crit.opt	Critical value at the optimal level
beta.opt	Type II error probability at the optimal level

Note

Applicable to a linear regression model

The black curve in the figure plots the denity under H0; The blue curve in the figure plots the denity under H1.

Author(s)

Jae H. Kim

References

Kim and Choi, 2020, Choosing the Level of Significance: A Decision-theoretic Approach, Abacus, Wiley. <https://doi.org/10.1111/abac.12172>

See Also

Leamer, E. 1978, Specification Searches: Ad Hoc Inference with Nonexperimental Data, Wiley, New York.

Kim, JH and Ji, P. 2015, Significance Testing in Empirical Finance: A Critical Review and Assessment, Journal of Empirical Finance 34, 1-14. <DOI:http://dx.doi.org/10.1016/j.jempfin.2015.08.006>

Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician. <https://doi.org/10.1080/00031305.2020.1750484.>

Examples

data(data1)
# Define Y and X
y=data1$lnoutput; x=cbind(data1$lncapital,data1$lnlabor)

# Restriction matrices to test for constant returns to scale
Rmat=matrix(c(0,1,1),nrow=1); rvec=matrix(0.94,nrow=1)

OptSig.Boot(y,x,Rmat,rvec,p=0.5,k=1,nboot=1000,Figure=TRUE)

OptSig documentation built on July 3, 2022, 5:05 p.m.