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Mathematical Problems in Engineering
Volume 2015, Article ID 902602, 12 pages
Research Article

Detection of Outliers in Panel Data of Intervention Effects Model Based on Variance of Remainder Disturbance

School of Mathematics and Quantitative Economics, Dongbei University of Finance and Economics, Dalian 116025, China

Received 18 July 2014; Revised 19 September 2014; Accepted 24 September 2014

Academic Editor: Erol Egrioglu

Copyright © 2015 Yanfang Lyu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The presence of outliers can result in seriously biased parameter estimates. In order to detect outliers in panel data models, this paper presents a modeling method to assess the intervention effects based on the variance of remainder disturbance using an arbitrary strictly positive twice continuously differentiable function. This paper also provides a Lagrange Multiplier (LM) approach to detect and identify a general type of outlier. Furthermore, fixed effects models and random effects models are discussed to identify outliers and the corresponding LM test statistics are given. The LM test statistics for an individual-based model to detect outliers are given as a particular case. Finally, this paper performs an application using panel data and explains the advantages of the proposed method.