Forecasting Land Use from Estimated Markov Transitions

Timothy H. Savage
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引用次数: 3

Abstract

The use of Markov processes (or Markov chains) has become widespread in dynamic stochastic modeling. For example, its use is ubiquitous in macroeconomics (dynamic stochastic general equilibrium), finance (dynamic asset pricing), and areas of microeconomics (dynamic programming). As we discuss below, its application in dynamic land use has been more limited, but is, in principle, no less applicable. Using a multi-nominal logit (ML) specification together with serial data on agricultural land use from California, we estimate Markov transition probabilities conditional on number of exogenous factors. Applying so-called “first step” analysis, these transition probabilities are used to forecast the distribution of agricultural crops, which in turn can be used for policy making.
从估计的马尔可夫过渡预测土地利用
马尔可夫过程(或马尔可夫链)在动态随机建模中得到了广泛的应用。例如,它在宏观经济学(动态随机一般均衡)、金融(动态资产定价)和微观经济学(动态规划)领域中无处不在。正如我们下面讨论的那样,它在动态土地利用方面的应用比较有限,但原则上同样适用。使用多标称logit (ML)规范和加利福尼亚农业用地的序列数据,我们估计了马尔可夫转移概率的条件是外生因素的数量。应用所谓的“第一步”分析,这些过渡概率被用来预测农作物的分布,进而可用于政策制定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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