View source: R/OptSig.Weight.R
OptSig.Weight | R Documentation |
The function calculates the weighted optimal level of significance for the F-test
The weights are obtained from a folded-normal distribution with mean m and staradrd deviation delta
OptSig.Weight(df1, df2, m, delta = 2, p = 0.5, k = 1, Figure = TRUE)
df1 |
the first degrees of freedom for the F-distribution |
df2 |
the second degrees of freedom for the F-distribution |
m |
a value of of the non-centality paramter, the mean of the folded-normal distribution |
delta |
standard deviation of the folded-normal distribution |
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 |
Figure |
show graph if TRUE (default); No graph if FALSE |
See Kim and Choi (2020)
alpha.opt |
Optimal level of significance |
crit.opt |
Critical value at the optimal level |
The figure shows the folded-normal distribution
Jae H. Kim
Kim and Choi, 2020, Choosing the Level of Significance: A Decision-theoretic Approach, Abacus, Wiley. <https://doi.org/10.1111/abac.12172>
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.>
data(data1) # Define Y and X y=data1$lnoutput; x=cbind(data1$lncapital,data1$lnlabor) # Restriction matrices to test for constant returns to scale Rmat=matrix(c(0,1,1),nrow=1); rvec=matrix(0.94,nrow=1) # Model Estimation and F-test M=R.OLS(y,x,Rmat,rvec) # Degrees of Freedom and estimate of non-centrality parameter K=ncol(x)+1; T=length(y) df1=nrow(Rmat);df2=T-K; NCP=M$ncp OptSig.Weight(df1,df2,m=NCP,delta=3,p=0.5,k=1,Figure=TRUE)
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