Dynamic measurement of poverty: modeling and estimation

DOI10.1007/s13571-018-0153-6zbMath1405.91406OpenAlexW2790872062MaRDI QIDQ1711617

Guglielmo D'Amico, Philippe Regnault

Publication date: 18 January 2019

Published in: Sankhyā. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13571-018-0153-6

zbMATH Keywords

Markov processnonparametric estimationpopulation dynamicmicro-data

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70) Continuous-time Markov processes on general state spaces (60J25) Economic growth models (91B62) Statistical methods; economic indices and measures (91B82)

Related Items (4)

Confidence sets for dynamic poverty indexes ⋮ Analysis of fair fee in guaranteed lifelong withdrawal and Markovian health benefits ⋮ Perturbation analysis for dynamic poverty indexes ⋮ A Copula-based Markov Reward Approach to the Credit Spread in the European Union

Cites Work

• Unnamed Item
• Estimating stochastic dynamical systems driven by a continuous-time jump Markov process
• Mobility measurement, transition matrices and statistical inference
• Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization
• Poverty, Income Inequality, and Their Measures: Professor Sen's Axiomatic Approach Reconsidered
• A semi-Markov model for a multigrade population with Poisson recruitment
• On the poisson approximation to the multinomial distribution
• Non-homogeneous semi-Markov systems and maintainability of the state sizes
• Poverty: An Ordinal Approach to Measurement
• Asymptotic Behavior of the Multinomial Dstribution
• Revisiting the Sen Poverty Index
• A multidimensional view on poverty in the European Union by partial order theory
• Statistical Inference for Discretely Observed Markov Jump Processes
• On the Existence and Uniqueness of the Real Logarithm of a Matrix

This page was built for publication: Dynamic measurement of poverty: modeling and estimation