Measuring chronic and transient components of poverty: a Bayesian approach
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DOI: 10.1007/s00181-006-0110-5
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References listed on IDEAS
- Ravallion, Martin, 1988. "Expected Poverty under Risk-Induced Welfare Variability," Economic Journal, Royal Economic Society, vol. 98(393), pages 1171-1182, December.
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"Bayesian inference in error-in-variables models,"
Journal of Multivariate Analysis, Elsevier, vol. 4(4), pages 419-452, December.
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Cited by:
- Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print halshs-04135764, HAL.
- Michel Lubrano & Zhou Xun, 2021.
"The Bayesian approach to poverty measurement,"
AMSE Working Papers
2133, Aix-Marseille School of Economics, France.
- Michel Lubrano & Zhou Xun, 2021. "The Bayesian approach to poverty measurement," Working Papers halshs-03234072, HAL.
- Shiva Raj Adhikari, Ph.D., 2016. "Poverty Dynamics in Nepal between 2004 and 2011: An Analysis of Hybrid Dataset," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 28(1), pages 27-40, April.
- Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print hal-04347292, HAL.
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More about this item
Keywords
Birth–death process; Foster Greer and Thorbecke (FGT) measure; Gibbs sampling; Markov chain Monte Carlo (MCMC); C11; C15; D63;All these keywords.
JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
Statistics
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