收入动态的连续时间模型

T. Heimann, M. Trede
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引用次数: 4

摘要

大多数收入动态模型都是在一个任意选择的会计期间的离散时间框架中设置的。本文介绍了收入流的连续时间随机模型,而不需要定义会计期间。我们的模型可以使用任意间隔观测的不平衡面板数据进行估计。虽然我们的模型描述了收入流的随机特性,但估计是基于在可能不同长度的时间间隔内观察到的收入积累。我们的收入动态模型在精神上接近文献中流行的离散时间两阶段模型。我们施加了一个简约参数化的连续时间随机过程(可能包含一个单位根)来模拟与传统收益函数的偏差。我们通过使用1975年至1995年德国社会保障机构的微观经济数据估计一个简化模型来说明我们的方法。
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A Continuous-Time Model of Income Dynamics
Most models of income dynamics are set in a discrete-time framework with an arbitrarily chosen accounting period. This article introduces a continuous-time stochastic model of income flows, without the need to define an accounting period. Our model can be estimated using unbalanced panel data with arbitrarily spaced observations. Although our model describes the stochastic properties of income flows, estimation is based on observed incomes accruing during time intervals of possibly varying length. Our model of income dynamics is close in spirit to the discrete-time two-stage models prevalent in the literature. We impose a parsimoniously parameterized continuous-time stochastic process (possibly containing a unit root) to model the deviation from a traditional earnings function. We illustrate our approach by estimating a simplified model using microeconomic data from the German social security agency from 1975 to 1995.
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