Multiple regression with inequality constraints: Pretesting bias, hypothesis testing and efficiency

Michael C. Lovell, Edward Prescott

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

This article analyzes, within the context of the standard multiple regression model, the problem of handling inequality constraints specifying the signs of certain regression coefficients. It is common econometric practice when regression coefficients are encountered with incorrect sign to delete the variables in question and reestimate the equation. This article shows that this procedure causes bias and can lead to inefficient parameter estimates. Furthermore, we show that grossly exaggerated statements concerning significance levels are likely to be made when other regression coefficients in the model are tested with the final regression obtained after deleting variables with incorrect sign.

Original languageEnglish (US)
Pages (from-to)913-925
Number of pages13
JournalJournal of the American Statistical Association
Volume65
Issue number330
DOIs
StatePublished - 1970
Externally publishedYes

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Multiple Regression
Regression Coefficient
Hypothesis Testing
Inequality Constraints
Significance level
Multiple Models
Econometrics
Regression Model
Regression
Likely
Estimate
Inequality constraints
Multiple regression
Hypothesis testing
Coefficients
Model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Multiple regression with inequality constraints : Pretesting bias, hypothesis testing and efficiency. / Lovell, Michael C.; Prescott, Edward.

In: Journal of the American Statistical Association, Vol. 65, No. 330, 1970, p. 913-925.

Research output: Contribution to journalArticle

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