Front Matter
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Front Matter Page

African Department

Authorized for distribution by Arend Kouwenaar

Contents

  • I. Motivation

  • II. The Data Structure and the Bias of the Estimator

  • III. The Bandwidth and Kernels Considered

  • IV. Monte Carlo Study

    • A. Theoretical Distributions

    • B. Summary Statistics, Density Estimates and Diagrams

    • C. Poverty Estimates

  • V. Country Studies

  • VI. Global Poverty

  • VII. Conclusions

  • References

  • Appendix

  • Appendix Figures

  • Figure 1. Distributions used in Monte Carlo analysis

  • Figure 2. Bias of KDE-based density (log-normal distribution)

  • Figure 3. Bias of estimated density (multimodal distribution)

  • Figure 4. Bias of estimated density (Dagum distribution)

  • Figure 5. Bias in the poverty headcount ratio versus location of poverty line

  • Figure 6. Survey-based and grouped data KDE-based density estimates

  • Appendix Tables

  • Table 1. Summary statistics from KDE-based sample

  • Table 2. Bias of poverty measures (Low and High Poverty Lines)

  • Table 3. Bias of poverty measures (Triweight kernel, Poverty line: 0.25 x median)

  • Table 4. Bias of poverty measures (Hybrid bandwidth, Poverty line: 0.5 x median)

  • Table 5. Bias of poverty measures (Epanechnikov kernel, Silverman bandwidth)

  • Table 6. Bias of poverty measures (Gaussian kernel, Poverty line: Capability)

  • Table 7. Global poverty rates (% poor)

  • Table 8. Global poverty counts (millions)

Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment
Author: Ms. Camelia Minoiu and Sanjay Reddy