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Specification Search and Levels of Significance in Econometric Models


  • Steven B. Caudill

    () (Economics Department, Auburn University)

  • Randall G. Holcombe

    () (Department of Economics, Florida State University)


This paper describes the problem specification searches pose for inference, presents the results of some simulations for purposes of illustration, and uses the bootstrapping procedure to give a better estimate of statistical significance than a standard t-test. The value of the illustrations of specification searches is that they help demonstrate the severity of the problem. The examples presented here illustrate that in most cases, a researcher can undertake specification search and report a statistically significant result regardless of whether the variables in a regression equation are actually related. The bootstrap procedure used to analyze the specification searches does provide another way to examine the true statistical significance of empirical results. Two different specification searches are examined: a "drop insignificant coefficients" search and a "biggest t-ratio" search. Both are shown to lead to larger than reported standard errors. In general, standard errors get larger if a specification search has taken place, but exactly how much larger must be determined on a case-by-case basis.

Suggested Citation

  • Steven B. Caudill & Randall G. Holcombe, 1999. "Specification Search and Levels of Significance in Econometric Models," Eastern Economic Journal, Eastern Economic Association, vol. 25(3), pages 289-300, Summer.
  • Handle: RePEc:eej:eeconj:v:25:y:1999:i:3:p:289-300

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    Cited by:

    1. Carson, Richard T. & DeShazo, J.R. & Schwabe, Kurt A. & Vincent, Jeffrey R. & Ahmad, Ismariah, 2015. "Incorporating local visitor valuation information into the design of new recreation sites in tropical forests," Ecological Economics, Elsevier, vol. 120(C), pages 338-349.
    2. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
    3. Hristos Doucouliagos & Martin Paldam, 2013. "The Robust Result in Meta-analysis of Aid Effectiveness: A Response to Mekasha and Tarp," Journal of Development Studies, Taylor & Francis Journals, vol. 49(4), pages 584-587, April.
    4. Thomas Apolte, 2002. "Jurisdictional competition for quality standards: Competition of laxity?," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 30(4), pages 389-402, December.
    5. Chris Doucouliagos & T.D. Stanley, 2013. "Are All Economic Facts Greatly Exaggerated? Theory Competition And Selectivity," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 316-339, April.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications


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