OptSig (version 1.0)

Power.F: Function to calculate the power of an F-test

Description

This function calculates the power of an F-test, given the value of non-centrality parameter

Usage

Power.F(df1, df2, ncp, alpha, 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

alpha

the level of significance

Figure

show graph if TRUE (default); No graph if FALSE

Value

Power

Power of the test

Crit.val

Critical value at alpha level of signifcance

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

Power.F(df1,df2,ncp=NCP,alpha=0.20747,Figure=TRUE)
# }