TY  - JOUR
TT  - Comparison of the r- (k, d) class estimator with some estimators for multicollinearity under the Mahalanobis loss function
AU  - Chandra, Shalini
AU  - Sarkar, Nityananda
PY  - 2015
DA  - June
DO  - 10.33818/ier.278037
JF  - International Econometric Review
JO  - IER
PB  - Econometric Research Association
WT  - DergiPark
SN  - 1308-8793
SP  - 1
EP  - 12
VL  - 7
IS  - 1
KW  - r-(k
KW  - d) class estimator
KW  - Principal component estimator
KW  - Two-parameter class estimator
KW  - Mahalanobis loss function
KW  - Risk criterion
N2  - In the case of ill-conditioned design matrix in linear regression model, the r - (k, d) class estimator was proposed, including the ordinary least squares (OLS) estimator, the principal   component regression (PCR) estimator, and the two-parameter class estimator. In this paper, we opted to evaluate the performance of the r - (k, d) class estimator in comparison to    others under the weighted quadratic loss function where the weights are inverse of the variance-covariance matrix of the estimator, also known as the Mahalanobis loss function using    the criterion of average loss. Tests verifying the conditions for superiority of the r - (k, d) class estimator have also been proposed. Finally, a simulation study and also an     empirical illustration have been done to study the performance of the tests and hence verify the conditions of dominance of the r - (k, d) class estimator over the others under the     Mahalanobis loss function in artificially generated data sets and as well as for a real data. To the best of our knowledge, this study provides stronger evidence of superiority of     the r - (k, d) class estimator over the other competing estimators through tests for verifying the conditions of dominance, available in literature on multicollinearity.
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UR  - https://doi.org/10.33818/ier.278037
L1  - https://dergipark.org.tr/en/download/article-file/259402
ER  -