Large mixed-frequency VARs with a parsimonious time-varying parameter structure

Goetz Thomas B.; Hauzenberger Klemens

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Large mixed-frequency VARs with a parsimonious time-varying parameter structure

机译：大型混合频率差，具有解析的时变参数结构

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In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

机译：为了同时考虑混合频率时间序列，它们的联合动力学和可能的结构变化，我们引入了一个时变的参数混合频率向量自动转移（VAR）。时间变化以解析的方式进入：仅截取和错误方差中的常见因素可以变化。因此，计算复杂性仍然在仍然允许我们在合理的时间内估计中等大的变量的范围。这使我们的模型是任何预测模型套件的吸引力。对于11，美国变量，与常用的恒定系数混合频率var和其他相关模型类相比，我们展示了竞争力。我们的模型也准确地在Covid-19大流行期间准确地捕获了国内生产总值下降。

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来源
《The econometrics journal》 |2021年第3期|442-461|共20页
作者
Goetz Thomas B.; Hauzenberger Klemens;
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Deutsch Bundesbank Macroecon Anal & Project Div Wilhelm Epstein Str 14 D-60431 Frankfurt Germany;

Deutsch Bundesbank Macroecon Anal & Project Div Wilhelm Epstein Str 14 D-60431 Frankfurt Germany;

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正文语种 eng
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Bayesian methods; time-varying intercepts; common stochastic volatility; forecasting; real-time data; COVID-19 case study;

机译：贝叶斯方法;时变截距;常见的随机波动性;预测;实时数据;Covid-19案例研究;

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Large mixed-frequency VARs with a parsimonious time-varying parameter structure

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