Non-linear dimension reduction in factor-augmented vector autoregressions

IF 1.9 3区 经济学 Q2 ECONOMICS
Karin Klieber
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引用次数: 0

Abstract

This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. I argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent times of the business cycle. In simulations, I show that non-linear dimension reduction techniques yield good forecasting performance, especially when data is highly volatile. In an empirical application, I identify a monetary policy as well as an uncertainty shock excluding and including observations of the COVID-19 pandemic. Those two applications suggest that the non-linear FAVAR approaches are capable of dealing with the large outliers caused by the COVID-19 pandemic and yield reliable results in both scenarios.

因子增强向量自回归中的非线性维度降低
本文在因子增强向量自回归中引入了非线性降维,以分析不同经济冲击的影响。我认为,在商业周期的动荡时期,控制大维度数据集与潜在因子之间的非线性尤其有用。在模拟中,我证明了非线性降维技术能产生良好的预测效果,尤其是在数据高度不稳定的情况下。在实证应用中,我确定了货币政策以及不确定性冲击,排除并包括 COVID-19 大流行病的观测数据。这两个应用表明,非线性 FAVAR 方法能够处理 COVID-19 大流行病造成的大离群值,并在两种情况下都能得出可靠的结果。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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