On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables

DOI10.1007/s00180-017-0776-5zbMath1417.62005OpenAlexW2768920092MaRDI QIDQ1643009

Lijia Mo, Krishna P. Pokharel, Jason S. Bergtold, Allen M. Featherstone

Publication date: 18 June 2018

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-017-0776-5

zbMATH Keywords

maximum likelihood estimationaccuracylogistic regressionbenchmark datasetseconometric software

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Software, source code, etc. for problems pertaining to statistics (62-04) Generalized linear models (logistic models) (62J12)

Uses Software

• Mathematica
• R
• LIMDEP
• Stata
• SAS
• Excel
• aplore3
• SAS/STAT
• NLOGIT

Cites Work

• Unnamed Item
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• A Simulation Study to Investigate the Behavior of the Log-Density Ratio Under Normality
• Applied Logistic Regression
• Microeconometrics
• Nonlinear Programming
• The accuracy of \textit{Mathematica 4} as a statistical package

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