Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Comparing Cross-Country Estimates of Lorenz Curves Using a Dirichlet Distribution Across Estimators and Datasets
Chotikapanich and Griffiths (2002) introduced the Dirichlet distribution to the estimation of Lorenz curves. This distribution naturally accommodates the proportional nature of income share data and the dependence structure between the shares. Chotikapanich and Griffiths (2002) fit a family of five Lorenz curves to one year of Swedish and Brazilian income share data using unconstrained maximum likelihood and unconstrained non-linear least squares. We attempt to replicate the authors' results and extend their analyses using both constrained estimation techniques and five additional years of data. We successfully replicate a majority of the authors' results and find that some of their main qualitative conclusions also hold using our constrained estimators and additional data.
Cite this item
Andrew C. Chang & Phillip Li & Shawn M. Martin, Comparing Cross-Country Estimates of Lorenz Curves Using a Dirichlet Distribution Across Estimators and Datasets, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2017-062, Jun 2017.
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
Keywords: Constrained Estimation; Dirichlet; Dirichlet Distribution; Gini Coefficient; Income Distribution; Lorenz Curve; Maximum Likelihood; Non-linear Least Squares; Replication; Share Data
This item with handle RePEc:fip:fedgfe:2017-62
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