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Bayesian estimation of non-stationary Markov models combining micro and macro data

Hugo Storm and Thomas Heckelei

No 103645, 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania from Agricultural and Applied Economics Association

Abstract: 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.

Keywords: Agricultural and Food Policy; Research Methods/ Statistical Methods (search for similar items in EconPapers)
Pages: 2
Date: 2011-07-24
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (6) Track citations by RSS feed

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https://ageconsearch.umn.edu/record/103645/files/12782.pdf (application/pdf)

Related works:
Journal Article: Bayesian estimation of non-stationary Markov models combining micro and macro data (2016) Downloads
Working Paper: Bayesian Estimation of Non-Stationary Markov Models Combining Micro and Macro Data (2014) Downloads
Working Paper: Bayesian estimation of non-stationary Markov models combining micro and macro data (2011) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea11:103645

DOI: 10.22004/ag.econ.103645

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