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

Hugo Storm, Thomas Heckelei and Ron Mittelhammer ()

European Review of Agricultural Economics, 2016, vol. 43, issue 2, 303-329

Abstract: We develop a Bayesian framework for estimating non-stationary Markov models in situations where macro population data are available only on the proportion of individuals residing in each state, but micro-level sample data are available on observed transitions between states. Posterior distributions on non-stationary transition probabilities (TPs) are derived combining micro and macrodata using potentially asynchronous data observations, providing a new method for inferring TPs that merges previously disparate approaches. Monte Carlo simulations demonstrate how observed micro transitions can improve the precision of posterior information. We provide an empirical illustration in the context of farm structural change.

Date: 2016
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Related works:
Working Paper: Bayesian Estimation of Non-Stationary Markov Models Combining Micro and Macro Data (2014)
Working Paper: Bayesian estimation of non-stationary Markov models combining micro and macro data (2011)
Working Paper: Bayesian estimation of non-stationary Markov models combining micro and macro data (2011)
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