The response of household debt to COVID-19 using a neural networks VAR in OECD.
The response of household debt to COVID-19 using a neural networks VAR in OECD.
复制标题
DOI：
10.1007/s00181-022-02325-2
复制
发表时间：
2022-11-16
影响因子：
3.2
通讯作者：
Tsionas, Mike G
中科院分区：
文献类型：
--
作者：
Mamatzakis, Emmanuel C;Ongena, Steven;Tsionas, Mike G
本文在经合组织国家的一个神经网络面板VAR中调查了家庭债务对新冠肺炎相关数据的反应，如确诊病例和确诊死亡人数。我们的模型还包括过多的非药物干预和药物干预。我们选择了一种全球神经网络面板VAR(GVAR)方法，该方法嵌套了样本中的所有OECD国家。由于线性因素模型无法捕捉我们数据集中的变异性，因此使用人工神经网络(ANN)方法可以捕捉这种变异性。因子个数和中间层个数由边际似然准则确定，并用MCMC技术估计GVAR。我们还报告了δ-捕捉网络中每个国家的主导地位的值。就占主导地位的国家而言，英国、美国和日本在网络内的互联互通方面占主导地位，但比利时、荷兰和巴西等国家也占主导地位。结果显示，家庭债务对新冠肺炎感染和死亡有正向反应。居家建议和关闭学校等封锁措施都对家庭债务产生了积极影响，尽管这些措施是暂时的。然而，疫苗接种和检测似乎对家庭债务产生了负面影响。
This paper investigates responses of household debt to COVID-19-related data like confirmed cases and confirmed deaths within a neural networks panel VAR for OECD countries. Our model also includes a plethora of non-pharmaceutical and pharmaceutical interventions. We opt for a global neural networks panel VAR (GVAR) methodology that nests all OECD countries in the sample. Because linear factor models are unable to capture the variability in our data set, the use of an artificial neural network (ANN) method permits to capture this variability. The number of factors, as well as the number of intermediate layers, is determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. We also report δ-values that capture the dominance of each individual country in the network. In terms of dominant countries, the UK, the USA, and Japan dominate interconnections within the network, but also countries like Belgium, Netherlands, and Brazil. Results reveal that household debt positively responds to COVID-19 infections and deaths. Lockdown measures such as stay-at-home advice, and closing schools, all have a positive impact on household debt, though they are of transitory nature. However, vaccinations and testing appear to negatively affect household debt.