Revisiting the collinear data problem: An assessment of estimator 'Ill-Conditioning' in linear regression

Practical Assessment, Research and Evaluation

Volumn 13, Issue 5, 2008, Pages

  Callaghan, Karen ^a   Chen, Jie ^b  

^a TEXAS SOUTHERN UNIVERSITY   (United States)

^b UNIVERSITY OF MASSACHUSETTS BOSTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78751636688     PISSN: None     EISSN: 15317714     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. Belsley, D. A (1991a). A guide to using the collinearity diagnostics. Computer Science in Economics and Management, 4, 33-50.
2. Belsley, D. A. (1991b). Collinearity diagnostics: Collinearity and weak data in regression. New York: John Wiley & Sons.
3. Belsley, D. A., Kuh, E. & Welsch, R. H. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley & Sons.
4. Coombs, W. T. (1995). What are eigenvalues? Disseminations of the International Statistical Applications Iinstitute,1, 90-91.
5. Farrar, D. & Glauber, R. R. (1967). Multicollinearity in regression Analysis: The problem revisited. Review of Economics and Statistics, 49, 92-107.
6. Fox, J. (1997). Applied regression analysis, linear models, and related methods. Thousand Oaks, CA: Sage Publications.
7. Gujarati, D. N. (2002). Basic econometrics. New York: McGraw-Hill.
8. Kendall, M. G. (1957). A course in multivariate analysis. London: Griffin.
9. Maddalla, G.S. (1992). Introduction to Economics. New York: Macmillan.
10. Weisberg, S. (2005). Applied linear regression. New York: John Wiley & Sons.