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通过一种新的强大的过方法估计潜在GDP

Éva Gyurkovics¹, Tibor Takács²

¹Mathematical Institute, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1521 Hungary.

Central European journal of operations research

|June 26, 2023

概括

一个新的强大的过器可以估计宏观经济指标,而不需要完全的模型稳定性. 应用于匈牙利潜在GDP,它揭示了亲周期经济政策和2012年后持续的积极GDP差距,包括在COVID-19危机期间.

科学领域:

计量经济学计量经济学计量经济学
宏观经济的建模.
时间序列分析时间序列分析.

背景情况:

估计不可观察的宏观经济指标对于经济政策至关重要.
像卡尔曼波器这样的传统方法依赖于严格的假设,这些假设在现实世界经济中可能不成立.
估计匈牙利经济的潜在GDP需要强大的方法,能够处理复杂的动态和不确定性.

研究的目的:

引入一种新的,强大的过方法,用于估计不可观察的宏观经济指标.
应用这种新方法来估计2000年至2021年间匈牙利潜在的国内生产总值 (GDP).
通过放松动态模型稳定性要求和适应一般的二次性约束来解决现有过器的局限性.

主要方法:

开发一个理论上新的强大的波器方法.
放松动态模型稳定性到一个部分条件.
包括时间依赖的不确定性和满足一般二次制约的非线性.
应用过器来估计匈牙利的潜在GDP,使用单元,双元和三元模型.

主要成果:

提出的强有力的过方法已成功应用于估计匈牙利2000-2021年潜在GDP.
通过单元,双元和三元模型获得一致的估计.
分析表明,匈牙利的经济政策在2012年之后显著表现为前周期性.
在COVID-19危机期间和之后观察到积极的GDP差距.

结论:

新型强大的过器为宏观经济指标估计提供了比传统过器更灵活,更少依赖假设的替代方案.
估计匈牙利的潜在GDP突出显著的政策影响,特别是关于近期危机的亲周期性和经济影响.
调查结果提供了有关匈牙利经济在关键全球事件期间表现的及时见解.

关键词:

线性矩阵不等式的不等式聚类和二次边界的不确定性.可能的国内生产总值 (GDP).强大的过器.趋势周期分解的分解没有观察到的组件模型.

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