Tackling the omitted variables problem without the strong assumptions of proxies

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Omitted variables that interact with included independent variables change the vertical placement of observations. Thus, by projecting the data to an output oriented VRS DEA frontier, the influence of omitted variables can be eliminated. After this is done once, the efficient observations can be eliminated and the process repeated. Each subsequent iteration shows the relationship between the dependant and known independent variable for progressively less favorable omitted variables. Building on these ideas, we introduce a new analytical technique named "Reiterative Truncated Projected Least Squares" (RTPLS). We provide both a theoretical argument and simulation evidence that RTPLS produces less bias than ordinary least squares (OLS) when there are omitted variables that interact with the included variables. By way of example, we show how omitted variables have affected the relationship between the monetary base (MB) and the money supply (M2 + CDs) for Japan using monthly data from January 1970 to April 2003.

Original languageEnglish (US)
Pages (from-to)819-840
Number of pages22
JournalEuropean Journal of Operational Research
Volume178
Issue number3
DOIs
StatePublished - May 1 2007

Keywords

  • Data envelopment analysis
  • Monetary policy
  • Omitted variables

ASJC Scopus subject areas

  • Information Systems and Management
  • General Computer Science
  • Industrial and Manufacturing Engineering
  • Modeling and Simulation
  • Management Science and Operations Research

Fingerprint

Dive into the research topics of 'Tackling the omitted variables problem without the strong assumptions of proxies'. Together they form a unique fingerprint.

Cite this