Pull Your Small Area Estimates Up by the Bootstraps

• Tác giả: Isabel Molina
• Ngôn ngữ: eng
• Ký hiệu phân loại: 338.64 Size of enterprises
• Thông tin xuất bản: Taylor and Francis, 2022
• Bộ sưu tập: Tài liệu truy cập mở
• ID: 297050
• Chủ đề: 1. ELL APPROACH; 2. EMPIRICAL BEST; 3. PARAMETRIC BOOTSTRAP; 4. POVERTY MAPPING; 5. SMALL AREA ESTIMATION

Tóm tắt

 This paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the institution: the traditional ELL approach and the Empirical Best (EB) addition introduced to imitate the original EB procedure of Molina and Rao [Small area estimation of poverty indicators. Canadian J Stat. 2010
 38(3):369-385], including heteroskedasticity and survey weights, but using a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased and noisier point estimates. The document presents an update to the World Bank's EB implementation by considering the original EB procedures for point and noise estimation, extended for complex designs and heteroscedasticity. Simulation experiments illustrate that the revised methods yield considerably less biased and more efficient estimators than those obtained from the clustered bootstrap approach.