Premium
PRACTITIONERS CORNER A POSITIVE SEMI‐DEFINITE COVARIANCE MATRIX FOR HAUSMAN SPECIFICATION TESTS OF CONDITIONAL AND MARGINAL DENSITIES
Author(s) -
Ai Chunrong
Publication year - 1995
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.1995.mp57002008.x
Subject(s) - mathematics , covariance matrix , hausman test , estimator , covariance , statistic , positive definite matrix , econometrics , test statistic , statistics , statistical hypothesis testing , panel data , fixed effects model , physics , eigenvalues and eigenvectors , quantum mechanics
When the joint density of data can be factorized into a conditional and marginal densities, Hausman test can be used for diagnosing misspecifications of these densities. However, since common covariance estimates of the difference of the two estimators used in Hausman test need not be positive semi‐definite in finite samples, the test statistic may be negative. This paper presents a simple and consistent covariance matrix which is positive semi‐definite in any finite sample.