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经济复苏的形态重要吗？一种基于新的平滑转换模型的替代单位根检验。

Does the shape of economic recovery matter? An alternative unit root test with new smooth transition model.

作者信息

Özcan Mehmet

机构信息

Dr. Faculty of Economics and Administrative Sciences, Department of Economics, Karamanoglu Mehmetbey University, Karaman, Turkey.

出版信息

J Econ Asymmetries. 2022 Nov;26:e00256. doi: 10.1016/j.jeca.2022.e00256. Epub 2022 Jun 11.

DOI:10.1016/j.jeca.2022.e00256
PMID:35722247
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9188429/
Abstract

The subject of economic recovery after the Coronavirus pandemic has received much attention in the media and by academics in recent years. Pandemic experience creates a new transition between the pre-pandemic era trend and the post-pandemic era trend related to the major economic indicators' time series path. This paper offers a new smooth transition model and a unit root testing procedure to test null of non-stationary against the alternatives of stationary that allow for a pit shape smooth transition from the pre-pandemic trend to post-pandemic trend. The properties of the test statistics are investigated with several simulation studies. Also, the new model and unit root testing procedure are applied to industrial production index, consumer price index and the unemployment rates of Global 8 countries and results state the usefulness of these new tests.

摘要

近年来，新冠疫情后经济复苏的主题受到了媒体和学术界的广泛关注。疫情经历在与主要经济指标时间序列路径相关的疫情前时代趋势和疫情后时代趋势之间创造了一种新的转变。本文提供了一种新的平滑转换模型和单位根检验程序，以检验非平稳的原假设与平稳的备择假设，该程序允许从疫情前趋势到疫情后趋势呈坑状平滑转换。通过多项模拟研究考察了检验统计量的性质。此外，将新模型和单位根检验程序应用于全球8个国家的工业生产指数、消费者价格指数和失业率，结果表明了这些新检验的有用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/6742950c97d7/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/6e35fa66f6da/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/128a2c1021fa/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/6742950c97d7/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/6e35fa66f6da/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/128a2c1021fa/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fbbc/9188429/6742950c97d7/gr3_lrg.jpg

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本文引用的文献

1
Study of the trend pattern of COVID-19 using spline-based time series model: a Bayesian paradigm.使用基于样条的时间序列模型对2019冠状病毒病趋势模式的研究：贝叶斯范式
Jpn J Stat Data Sci. 2022;5(1):363-377. doi: 10.1007/s42081-021-00127-x. Epub 2021 Jun 7.
2
V-, U-, L- or W-shaped economic recovery after Covid-19: Insights from an Agent Based Model.Covid-19 后 V、U、L 或 W 型经济复苏：基于代理的模型分析。
PLoS One. 2021 Mar 2;16(3):e0247823. doi: 10.1371/journal.pone.0247823. eCollection 2021.
3
After the lockdown: simulating mobility, public health and economic recovery scenarios.
封锁后：模拟流动性、公共卫生和经济恢复情景。
Sci Rep. 2020 Oct 12;10(1):16950. doi: 10.1038/s41598-020-73949-6.