Schneeweiß, Hans (30. June 2014): The linear GMM model with singular covariance matrix due to the elimination of a nuisance parameter. Department of Statistics: Technical Reports, No.165 

307kB 
Abstract
When in a linear GMM model nuisance parameters are eliminated by multiplying the moment conditions by a projection matrix, the covariance matrix of the model, the inverse of which is typically used to construct an efficient GMM estimator, turns out to be singular and thus cannot be inverted. However, one can show that the generalized inverse can be used instead to produce an efficient estimator. Various other matrices in place of the projection matrix do the same job, i.e., they eliminate the nuisance parameters. The relations between those matrices with respect to the efficiency of the resulting estimators are investigated.
Item Type:  Paper (Technical Report) 

Keywords:  Generalized method of moments, orthogonal projection, nuisance parameter, singular covariance matrix, weighting matrix, generalized inverse, panel data model 
Faculties:  Mathematics, Computer Science and Statistics Mathematics, Computer Science and Statistics > Statistics Mathematics, Computer Science and Statistics > Statistics > Technical Reports 
Subjects:  500 Science > 510 Mathematics 
URN:  urn:nbn:de:bvb:19epub210691 
Language:  English 
ID Code:  21069 
Deposited On:  30. Jun 2014 18:26 
Last Modified:  29. Apr 2016 09:18 
Repository Staff Only: item control page