OptSig (version 1.0)

Opt.SigBoot: 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

Opt.SigBoot(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

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

Details

See Kim and Choi (2017)

References

Kim and Choi, 2017, Choosing the Level of Significance: A Decision-theoretic Approach: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2652773

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>

Examples

# NOT RUN {
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)

Opt.SigBoot(y,x,Rmat,rvec,p=0.5,k=1,nboot=2000,Figure=TRUE)

# }