Bayesian estimation of non-stationary Markov models combining micro and macro data
AbstractIn 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.
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Bibliographic InfoPaper provided by Agricultural and Applied Economics Association in its series 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania with number 103645.
Date of creation: 24 Jul 2011
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Bayesian estimation; Markov transitions; prior information; multinomial logit; ordered multinomial logit; Agricultural and Food Policy; Research Methods/ Statistical Methods;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-24 (All new papers)
- NEP-ECM-2011-05-24 (Econometrics)
- NEP-ORE-2011-05-24 (Operations Research)
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- Storm, Hugo & Heckelei, Thomas, 2012. "Predicting agricultural structural change using census and sample data," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125185, Agricultural and Applied Economics Association.
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