2021/04/15 by Beste Hamiye Beyaztaş, Beste Hamiye Beyaztas, Beyaztas, Beste Hamiye +4
Economics, Econometrics and Finance · Mathematics · #Advanced Statistical Methods and Models #Computer science #Econometrics #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Fixed effects model #Global trade and economics #Hausman test #Mathematics #Methodology (stat.ME) #Monte Carlo method #Null hypothesis #Outlier #Panel data #Spatial and Panel Data Analysis #Specification #Statistical hypothesis testing #Statistics #econ.EM #stat.ME
paper · pdf · doi:10.48550/arxiv.2104.07723
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/04/15 · openalex publication_date 2021/04/15 · arxiv updated 2021/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.