Multicollinearity in regression

Review and examples

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

When building regression models for forecasting, analysts often encounter the problem of multicollinearity or illconditioning in their data sets. In such cases, large variances and covariances can make subset selection and parameter estimation difficult to impossible. In this paper, we suggest several approaches for extending estimation results to forecasting and review theoretical results useful for forecasting with multicollinearity. Several examples are provided.

Original languageEnglish (US)
Pages (from-to)281-292
Number of pages12
JournalJournal of Forecasting
Volume1
Issue number3
DOIs
StatePublished - 1982
Externally publishedYes

Fingerprint

Multicollinearity
Forecasting
Regression
Subset Selection
Set theory
Parameter estimation
Parameter Estimation
Regression Model
Review

Keywords

  • Estimation
  • Forecasting
  • Mean square error
  • Multicollinearity
  • Regression

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

Cite this

Multicollinearity in regression : Review and examples. / Askin, Ronald.

In: Journal of Forecasting, Vol. 1, No. 3, 1982, p. 281-292.

Research output: Contribution to journalArticle

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