On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables
Jason Bergtold (),
Krishna P. Pokharel (),
Allen Featherstone () and
Lijia Mo ()
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Krishna P. Pokharel: Kansas State University
Lijia Mo: Kansas State University
Computational Statistics, 2018, vol. 33, issue 2, No 9, 757-786
Abstract The numerical reliability of statistical software packages was examined for logistic regression models, including SAS 9.4, MATLAB R2015b, R 3.3.1., Stata/IC 14, and LIMDEP 10. Thirty unique benchmark datasets were created by simulating alternative conditional binary choice processes examining rare events, near-multicollinearity, quasi-separation and nonlinear transformation of variables. Certified benchmark estimates for parameters and standard errors of associated datasets were obtained following standards set-out by the National Institute of Standards and Technology. The logarithm of relative error was used as a measure of accuracy for numerical reliability. The paper finds that choice of software package and procedure for estimating logistic regressions will impact accuracy and use of default settings in these packages may significantly reduce reliability of results in different situations.
Keywords: Accuracy; Benchmark datasets; Logistic regression; Maximum likelihood estimation; Econometric software (search for similar items in EconPapers)
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