Type of Document Master's Thesis Author Belinga-Hill, Nelly E. Author's Email Address matnebx@langate.gsu.edu URN etd-11272007-201143 Title EMPIRICAL LIKELIHOOD CONFIDENCE INTERVALS FOR GENERALIZED LORENZ CURVE Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title Dr. Gengsheng (Jeff) Qin Committee Chair Dr. Xu Zhang Committee Member Dr. Pulak Ghosh Committee Member Dr. Yu-Sheng Hsu Committee Member Keywords
- Income data.
- Normal Approximation
- Empirical Likelihood
- Confidence Intervals
- Lorenz Ordinate
- Generalized Lorenz curve
- Lorenz curve
Date of Defense 2007-11-26 Availability unrestricted Abstract Lorenz curves are extensively used in economics to analyze income inequality metrics. In this thesis, we discuss confidence interval estimation methods for generalized Lorenz curve. We first obtain normal approximation (NA) and empirical likelihood (EL) based confidence intervals for generalized Lorenz curves. Then we perform simulation studies to compare coverage probabilities and lengths of the proposed EL-based confidence interval with the NA-based confidence interval for generalized Lorenz curve. Simulation results show that the EL-based confidence intervals have better coverage probabilities and shorter lengths than the NA-based intervals at 100p-th percentiles when p is greater than 0.50. Finally, two real examples on income are used to evaluate the applicability of these methods: the first example is the 2001 income data from the Panel Study of Income Dynamics (PSID) and the second example makes use of households’ median income for the USA by counties for the years 1999 and 2006Files
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