Traditional DEA-based ranking techniques have some pitfalls such as ignoring the inherent differences among decision making units(DMUs),or lacking a common weight-based ranking,etc.To overcome these obstacles,the pape...Traditional DEA-based ranking techniques have some pitfalls such as ignoring the inherent differences among decision making units(DMUs),or lacking a common weight-based ranking,etc.To overcome these obstacles,the paper first examines the possible differences among all DMUs such as the technical efficiency difference,the preference structure difference and the within-group position difference.Based upon the above differences this paper induces an integrated ranking measurement which helps to give a fair and full ranking for all DMUs under evaluation.Following the three types of differences,this approach behaves greatly elaborately,accurately and reasonably.Finally,the results from the Olympics achievement evaluation approve the acceptability of this approach.展开更多
We used simulated data to investigate both the small and large sample properties of the within-groups (WG) estimator and the first difference generalized method of moments (FD-GMM) estimator of a dynamic panel data (D...We used simulated data to investigate both the small and large sample properties of the within-groups (WG) estimator and the first difference generalized method of moments (FD-GMM) estimator of a dynamic panel data (DPD) model. The magnitude of WG and FD-GMM estimates are almost the same for square panels. WG estimator performs best for long panels such as those with time dimension as large as 50. The advantage of FD-GMM estimator however, is observed on panels that are long and wide, say with time dimension at least 25 and cross-section dimension size of at least 30. For small-sized panels, the two methods failed since their optimality was established in the context of asymptotic theory. We developed parametric bootstrap versions of WG and FD-GMM estimators. Simulation study indicates the advantages of the bootstrap methods under small sample cases on the assumption that variances of the individual effects and the disturbances are of similar magnitude. The boostrapped WG and FD-GMM estimators are optimal for small samples.展开更多
基金supported partly by the National Natural Science Fundation of China for Innovative Research Groups(T0821001)the National Natural Science Fundation of China(70801056)University of Science and Technology of China Science Funds for Young Scholars.
文摘Traditional DEA-based ranking techniques have some pitfalls such as ignoring the inherent differences among decision making units(DMUs),or lacking a common weight-based ranking,etc.To overcome these obstacles,the paper first examines the possible differences among all DMUs such as the technical efficiency difference,the preference structure difference and the within-group position difference.Based upon the above differences this paper induces an integrated ranking measurement which helps to give a fair and full ranking for all DMUs under evaluation.Following the three types of differences,this approach behaves greatly elaborately,accurately and reasonably.Finally,the results from the Olympics achievement evaluation approve the acceptability of this approach.
文摘We used simulated data to investigate both the small and large sample properties of the within-groups (WG) estimator and the first difference generalized method of moments (FD-GMM) estimator of a dynamic panel data (DPD) model. The magnitude of WG and FD-GMM estimates are almost the same for square panels. WG estimator performs best for long panels such as those with time dimension as large as 50. The advantage of FD-GMM estimator however, is observed on panels that are long and wide, say with time dimension at least 25 and cross-section dimension size of at least 30. For small-sized panels, the two methods failed since their optimality was established in the context of asymptotic theory. We developed parametric bootstrap versions of WG and FD-GMM estimators. Simulation study indicates the advantages of the bootstrap methods under small sample cases on the assumption that variances of the individual effects and the disturbances are of similar magnitude. The boostrapped WG and FD-GMM estimators are optimal for small samples.
