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Shi L.,Yunnan University of Finance and Economics | Shi L.,Yunnan TongChuang Scientific Computing and Data Mining Center | Lu J.,Yunnan University of Finance and Economics | Zhao J.,Yunnan University of Finance and Economics | Chen G.,University of Calgary
Computational Statistics and Data Analysis | Year: 2016

Generalized method of moment (GMM) is an important estimation method for econometric models. However, it is highly sensitive to the outliers and influential observations. This paper studies the detection of influential observations using GMM estimation and establishes some useful diagnostic tools, such as residual and leverage measures. The case deletion technique is employed to derive diagnostic measures. Under linear moment conditions, an exact deletion formula is derived, and under nonlinear moment condition an approximate formula is suggested. The results are applied to efficient instrumental variable estimation and dynamic panel data models. In addition, generalized residuals and leverage measure for GMM estimator are defined and discussed. Two real data sets are used for illustration and a simulation study is conducted to confirm the usefulness of the proposed methodology. © 2015 Elsevier B.V. Source


Zhao J.,Yunnan University of Finance and Economics | Zhao J.,Yunnan TongChuang Scientific Computing and Data Mining Center | Jin L.,Renmin University of China | Shi L.,Yunnan University of Finance and Economics | Shi L.,Yunnan TongChuang Scientific Computing and Data Mining Center
Computational Statistics and Data Analysis | Year: 2015

Abstract The Bayesian information criterion (BIC) is one of the most popular criteria for model selection in finite mixture models. However, it implausibly penalizes the complexity of each component using the whole sample size and completely ignores the clustered structure inherent in the data, resulting in over-penalization. To overcome this problem, a novel criterion called hierarchical BIC (HBIC) is proposed which penalizes the component complexity only using its local sample size and matches the clustered data structure well. Theoretically, HBIC is an approximation of the variational Bayesian (VB) lower bound when sample size is large and the widely used BIC is a less accurate approximation. An empirical study is conducted to verify this theoretical result and a series of experiments is performed on simulated and real data sets to compare HBIC and BIC. The results show that HBIC outperforms BIC substantially and BIC suffers from underestimation. © 2015 Elsevier B.V. All rights reserved. Source


Yu D.,Yunnan University of Finance and Economics | Yu D.,Yunnan TongChuang Scientific Computing and Data Mining Center | Bai P.,Yunnan University of Finance and Economics | Bai P.,Yunnan TongChuang Scientific Computing and Data Mining Center | And 2 more authors.
Computational Statistics and Data Analysis | Year: 2015

Under flexible distributional assumptions, the adjusted quasi-maximum likelihood (adqml) estimator for mixed regressive, spatial autoregressive model is studied in this paper. The proposed estimation method accommodates the extra uncertainty introduced by the unknown regression coefficients. Moreover, the explicit expressions of theoretical/feasible second-order-bias of the adqml estimator are derived and the difference between them is investigated. The feasible second-order-bias corrected adqml estimator is then designed accordingly for small sample setting. Extensive simulation studies are conducted under both normal and non-normal situations, showing that the quasi-maximum likelihood (qml) estimator suffers from large bias when the sample size is relatively small in comparison to the number of regression coefficients and such bias can be effectively eliminated by the proposed adqml estimation method. The use of the method is then demonstrated in the analysis of the Neighborhood Crimes Data. © 2015 Elsevier B.V. All rights reserved. Source

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