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Controlling the Overall Significance Level of a Battery of Least Squares Diagnostic Tests

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  • Leslie G. Godfrey

Abstract

Double bootstrap methods are used to control the overall significance level of a battery of diagnostic tests applied to a regression model estimated by ordinary least squares. Monte Carlo evidence on the finite sample performance of the bootstrap methods is reported and discussed. Copyright 2005 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.

Volume (Year): 67 (2005)
Issue (Month): 2 (04)
Pages: 263-279

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Handle: RePEc:bla:obuest:v:67:y:2005:i:2:p:263-279

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Cited by:
  1. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity in sample selection models," Economics Working Paper Series 1145, University of St. Gallen, School of Economics and Political Science.
  2. Christopher J. Bennett, 2009. "p-Value Adjustments for Asymptotic Control of the Generalized Familywise Error Rate," Vanderbilt University Department of Economics Working Papers 0905, Vanderbilt University Department of Economics.
  3. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics.
  4. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity for LATE identification based on inequality moment constraints," Economics Working Paper Series 1143, University of St. Gallen, School of Economics and Political Science.

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