AN APPLICATION AND EXTENSION OF THE MARKOV CHAIN MODEL TO CEREAL PRODUCTION
利用英国谷物市场的总量时间序列数据，估计了马尔可夫链的平稳转移概率，并检验了非平稳概率的可能性，最终引入外生变量构建了更灵活的模型。
Given aggregate time‐series data, conventional Markov Chain estimation of the stationary transition probabilities is performed for a model of the cereal market in Great Britain. The possibility of non‐stationary probabilities is examined in the light of these initial results, and the basic model developed to allow exogenous variables to enter via their influence on these parameters. The evidence from the paper would appear to suggest that, at least in the case considered, time‐varying transition probabilities are a most attractive alternative.