Bayesian estimation of non-stationary Markov models combining micro and macro data
提出一种贝叶斯框架，利用微观样本的转移数据和宏观总体比例数据，估计非平稳马尔可夫模型的转移概率，并通过蒙特卡洛模拟和农业结构变化案例验证其有效性。
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.