| OptSig.BootWeight {OptSig} | R Documentation |
Weighted Optimal Significance Level for the F-test based on the bootstrap
Description
The function calculates the weighted optimal level of significance for the F-test
The weights are obtained from the bootstrap distribution of the non-centrality parameter estimates
Usage
OptSig.BootWeight(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 (default); if FALSE, bootstrap is based on iid resampling |
Figure |
show graph if TRUE . No graph if FALSE (default) |
Details
The bootstrap can be conducted using either iid resampling or wild bootstrap.
Value
alpha.opt |
Optimal level of significance |
crit.opt |
Critical value at the optimal level |
Note
Applicable to a linear regression model
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)