Precautionary Saving in a Dynamic Model of Consumption and Labor Supply

大多数经济学家都认为，过去20年美国储蓄率的下降令人担忧。但对于美国储蓄率如此之低的原因，有相互矛盾的证据。这些证据很大程度上是基于完全确定情况下的储蓄模型，因为不确定情况下的现实跨期储蓄模型是难以处理的。这个项目的贡献来自于在一台超级计算机上开发用于矢量计算的软件，该软件满足消费和劳动力供应的跨期模型的大量计算和编程要求，该模型具有重叠的不确定性来源和实际的大量周期。将使用数字方法来估计预防性储蓄的规模，即因未来事件的不确定性而导致的储蓄。预防性储蓄的规模在很大程度上取决于现有的私人和公共保险计划减少个人不确定性的程度。现有的和潜在的未来政府保险计划，如社会保障、失业保险、医疗补助、医疗保险和巨灾健康保险，产生了高度非线性或扭曲的预算约束，这些约束不可能在标准分析框架中建模。该项目开发的数值解技术将使这些非线性保险和税收计划的影响能够被准确测量。

Most economists agree that the decline in the savings rate in the United States over the past two decades is a cause for concern. But there is conflicting evidence as to the reasons U.S. saving are so low. Much of this evidence is based on models of savings under perfect certainty because realistic intertemporal models of saving under uncertainty are intractable. The contribution of this project comes from developing software for vector calculations on a supercomputer that meet the substantial computing and programming requirements of an intertemporal model of consumption and labor supply with overlapping sources of uncertainty and a realistically large number of periods in the model. Numerical methods will be used to estimate the magnitude of precautionary saving, that is, saving caused by uncertainty about future events. The magnitude of precautionary saving depends in an important way on the extent that existing private and public insurance programs reduce individual uncertainty. Existing and potential future government insurance programs, such as Social Security, unemployment insurance, Medicaid, Medicare, and catastrophic health insurance, create highly nonlinear or kinked budget constraints which are impossible to model in the standard analytic framework. The numerical solution techniques developed by this project will permit the effects of these nonlinear insurance and tax programs to be measured accurately.