text-only page produced automatically by LIFT Text
Transcoder Skip all navigation and go to page contentSkip top navigation and go to directorate navigationSkip top navigation and go to page navigation
National Science Foundation HomeNational Science Foundation - Directorate for Social, Behavioral & Economic Sciences (SBE)
Social, Behavioral & Economic Sciences
design element
SBE Home
About SBE
Funding Opportunities
Awards
News
Events
Discoveries
Publications
Advisory Committee
Career Opportunities
See Additional SBE Resources
View SBE Staff
SBE Organizations
SBE Office of Multidisciplinary Activities (SMA )
National Center for Science and Engineering Statistics (NCSE)
Division of Behavioral and Cognitive Sciences (BCS )
Division of Social and Economic Sciences (SES )
Proposals and Awards
Proposal and Award Policies and Procedures Guide
  Introduction
Proposal Preparation and Submission
bullet Grant Proposal Guide
  bullet Grants.gov Application Guide
Award and Administration
bullet Award and Administration Guide
Award Conditions
Other Types of Proposals
Merit Review
NSF Outreach
Policy Office Website
Additional SBE Resources
Exploring What Makes Us Human
Rebuilding the Mosaic Report
Bringing People Into Focus: How Social, Behavioral & Economic Research Addresses National Challenges
"Youth Violence: What We Need to Know" Report to NSF
Social, Behavioral and Economic Research in the Federal Context Report
Expedited Review of Social and Behavioral Research Activities Report
SBE Advisory Committee Web Site (for members only)


SBE 2020: Submission Detail

ID Number: 307
Title: Sensitivity Analysis through mixed Gini and OLS regressions Paper submitted to the NSF
Lead Author: Yitzhaki, Shlomo
Abstract: About thirty years ago Edward Leamer criticized the credibility of empirical research in economics. Since then there were huge improvements in research design, data collection and econometric methodology. On the other hand, the huge increase in computing power has increased the number of instruments available for the use of the over-zealous researcher who wants to prove his point. I suggest developing the mixed Gini and Ordinary Least Squares regression. It enables unraveling, tracing and testing the role of several whimsical assumptions imposed on the data in regression analysis. Among those assumptions are the linearity assumption, the use of monotonic increasing transformations, and the symmetry between distributions that is imposed by the Pearson correlation coefficient. My conjecture is that the new technique will reduce drastically the number of results that are claimed to be supported by empirical "proofs".
PDF: Yitzhaki_Shlomo_307.pdf

SBE 2020 Home

 

Print this page
Back to Top of page