MELE: MAXIMUM ENTROPY LEUVEN ESTIMATORS

Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls of their own. The ridge estimator is not generally accepted as a vital alternative to the ordinary least-squares (OLS) estimator because it depends upon unknown parameters. The generalized maximum entropy (GME) estimator of Golan, Judge and Miller depends upon subjective exogenous information that affects the estimated parameters in an unpredictable way. This paper presents novel maximum entropy estimators inspired by the theory of light that do not depend upon any additional information. Monte Carlo experiments show that they are not affected by any level of multicollinearity and dominate OLS uniformly. The Leuven estimators are consistent and asymptotically normal.


Issue Date:
2001
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/11991
Total Pages:
34
JEL Codes:
C2
Series Statement:
Working Paper 01-003




 Record created 2017-04-01, last modified 2017-08-23

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)