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Estimating Inequality.pdf

GLO-DP-1138.pdf
Preview of Estimating Inequality
🔗 Source: econstor.eu
📊 Size: 666 KB
👤 Author: Brunori, Paolo; Salas-Rojo, Pedro; Verme, Paolo
⬇️ Downloads: 438

Summary

Paolo Brunori, Pedro Salas-Rojo, and Paolo Verme's paper "Estimating Inequality with Missing Incomes" conducts an experiment to test how different correction methods address measurement biases of inequality due to item non-response. The study finds that sample reweighting based on probabilities of non-response produces inequality estimates close to true values in most simulated missing data patterns, while replacing with means is the worst-performing method. Other effective methods include Matching, Pareto corrections, and Predictive Means Matching, although their performance varies depending on the missing data pattern. Machine Learning methods, such as LASSO, single tree, and random forest, are less effective.

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Description

Paolo Brunori, Pedro Salas-Rojo, and Paolo Verme's paper "Estimating Inequality with Missing Incomes" conducts an experiment to test how different correction...

Technical Information

  • File Format: PDF
  • File Size: 666 KB
  • Pages: 45
  • Language: EN
  • Author: Brunori, Paolo; Salas-Rojo, Pedro; Verme, Paolo
  • Total Downloads: 438
  • Last Updated: 6 hours ago

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This PDF document about Estimating Inequality provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Estimating Inequality.

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