The presence of outlying observations may adversely affect statistical testing procedures that result in unstable test statistics and unreliable inferences depending on the distortion in parameter estimates. In spite of the fact that the adverse effects of outliers in panel data models, there are only a few robust testing procedures available for model specification. In this paper, a new weighted likelihood based robust specification test is proposed to determine the appropriate approach in panel data including individual-specific components. The proposed test has been shown to have the same asymptotic distribution as that of most commonly used Hausman's specification test under null hypothesis of random effects specification. The finite sample properties of the robust testing procedure are illustrated by means of Monte Carlo simulations and an economic-growth data from the member countries of the Organisation for Economic Co-operation and Development. Our records reveal that the robust specification test exhibit improved performance in terms of size and power of the test in the presence of contamination.
翻译:尽管小组数据模型中外部线的不利效应,但只有几套严格的测试程序可供示范规格使用,本文件建议进行新的基于加权可能性的严格规格测试,以确定小组数据中的适当方法,包括具体成分; 已经证明拟议的测试与最常见的Hausman规格测试在随机效应说明的假设中具有相同的零星分布; 可靠的测试程序的有限样本特性通过Monte Carlo模拟和经济合作与发展组织成员国的经济成长数据加以说明; 我们的记录显示,稳妥规格测试显示,在存在污染的情况下,测试的规模和功率有所改善。