Data/hora: 29/05/2024 05:51:27

Provedor de dados: | Research in Agricultural & Applied Economics (AgEcon Searh)
País: | United States
Título: | Bayesian estimation of non-stationary Markov models combining micro and macro data
Autores: | Storm,   Hugo; Heckelei,   Thomas.
Data: | 2011-05-03
Ano: | 2011
Palavras-chave: | Bayesian estimation; Markov transitions; Prior information; Multinomial logit; Ordered multinomial logit; Agricultural and Food Policy; Research Methods/ Statistical Methods.
Resumo: | In this poster a Bayesian estimation framework for a non-stationary Markov model is developed for situations where sample data with observed transition between classes (micro data) and aggregate population shares (macro data) are available. Posterior distributions on transition probabilities are derived based on a micro based prior and a macro based Likelihood function thereby consistently combining previously separated approaches. Monte Carlo simulations for ordered and unordered Markov states show how observed micro transitions improve precision of posterior knowledge as the sample size increases.
Tipo: | Conference Paper or Presentation
Idioma: | Inglês
Identificador: | http://purl.umn.edu/103645

Relação:

Agricultural and Applied Economics Association&gt;2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania

Poster

ID12782

Formato: | 2