OptSig2-package: Optimal Level of Significance for Regression and Other...

In OptSig: Optimal Level of Significance for Regression and Other Statistical Tests

Optimal Level of Significance for Regression and Other Statistical Tests

Description

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Details

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The package accompanies the paper: Kim and Choi, 2020, Choosing the Level of Significance: A Decision-theoretic Approach. Abacus. Wiley.

It oprovides functions for the optimal level of significance for the test for linear restiction in a regeression model.

Other basic statistical tests, including those for population mean and proportion, are also covered using the functions from the pwr package.

Author(s)

Jae H. Kim <[e-mail]>

Maintainer: Jae H. Kim <[e-mail]>

References

Kim and Choi, 2020, Choosing the Level of Significance: A Decision-theoretic Approach: Abacus: a Journal of Accounting, Finance and Business Studies. Wiley. <https://doi.org/10.1111/abac.12172>

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Stephane Champely (2017). pwr: Basic Functions for Power Analysis. R package version 1.2-1. https://CRAN.R-project.org/package=pwr

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)
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
OptSig.F(df1,df2,ncp=NCP,p=0.5,k=1, Figure=TRUE)

OptSig documentation built on July 3, 2022, 5:05 p.m.