OptSig (version 1.0)

Opt.Sig: Optimal Significance Level for an F-test

Description

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

Usage

Opt.Sig(df1, df2, ncp, p = 0.5, k = 1, Figure = TRUE)

Arguments

df1

the first degrees of freedom for the F-distribution

df2

the second degrees of freedom for the F-distribution

ncp

a value of of the non-centality paramter

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

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)
# 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

# Optimal level of Significance: Under Normality
Opt.Sig(df1,df2,ncp=NCP,p=0.5,k=1, Figure=TRUE)
# }

Run the code above in your browser using DataCamp Workspace