Discounting long run average growth in stochastic dynamic programs
Discounting long run average growth in stochastic dynamic programs
复制标题
贴现随机动态计划中的长期平均增长
DOI：
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发表时间：
2003
期刊：
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通讯作者：
Jorge A. Duran
中科院分区：
文献类型：
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作者：
Jorge A. Duran
概括。寻找贝尔曼方程的解通常依赖于限制性有界假设。在本文中，我们开发了一种证明方法，可以免除回报受上面限制的假设。在应用中，我们的假设仅意味着长期平均（预期）增长被充分折扣，这与绝对限制增长或限制每个时期（而不是长期）最大（而不是平均）增长的经典假设形成鲜明对比。我们讨论与文献相关的工作并提供几个例子。
Summary. Finding solutions to the Bellman equation often relies on restrictive boundedness assumptions. In this paper we develop a method of proof that allows to dispense with the assumption that returns are bounded from above. In applications our assumptions only imply that long run average (expected) growth is sufficiently discounted, in sharp contrast with classical assumptions either absolutely bounding growth or bounding each period (instead of long run) maximum (instead of average) growth. We discuss our work in relation to the literature and provide several examples.