Department of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, Romania.
Department of Education, Training and Labour Market, National Scientific, Research Institute for Labour and Social Protection, 010643 Bucharest, Romania.
Int J Environ Res Public Health. 2021 Oct 24;18(21):11165. doi: 10.3390/ijerph182111165.
Economic crises cause significant shortages in disposable income and a sharp decline in the living conditions, affecting healthcare sector, hitting the profitability and sustainability of companies leading to raises in unemployment. At micro level, these sharp decreases in earnings associated with unemployment and furthermore with the lack of social protection will impact the quality of life and finally the health of individuals. In time of crisis, it becomes vital to support not only the critical sectors of the economy, the assets, technology, and infrastructure, but to protect jobs and workers. This health crisis has hit hard the jobs dynamics through unemployment and underemployment, the quality of work (through wages, or access to social protection), and through the effects on specific groups, with a higher degree of vulnerability to unfavorable labor market outcomes. In this context, providing forecasts as recent as possible for the unemployment rate, a core indicator of the Romanian labor market that could include the effects of the market shocks it becomes fundamental. Thus, the paper aims to offer valuable forecasts for the Romanian unemployment rate using univariate vs. multivariate time series models for the period 2021-2022, highlighting the main patterns of evolution. Based on the univariate time series models, the paper predict the future values of unemployment rate based on its own past using self-forecasting and implementing ARFIMA and SETAR models using monthly data for the period January 2000-April 2021. From the perspective of multivariate time series models, the paper uses VAR/VECM models, analyzing the temporal interdependencies between variables using quarterly data for the period 2000Q1-2020Q4. The empirical results pointed out that both SETAR and VECM provide very similar results in terms of accuracy replicating very well the pre-pandemic period, 2018Q2-2020Q1, reaching the value of 4.1% at the beginning of 2020, with a decreasing trend reaching the value of 3.9%, respectively, 3.6% at the end of 2022.
经济危机导致可支配收入大幅减少,生活水平急剧下降,影响医疗保健行业,冲击企业盈利能力和可持续性,导致失业率上升。在微观层面上,与失业相关的收入急剧下降,以及缺乏社会保护,将影响个人的生活质量,最终影响个人的健康。在危机时期,支持不仅是经济的关键部门、资产、技术和基础设施,而且保护就业和工人变得至关重要。这场卫生危机通过失业和就业不足、工作质量(通过工资或获得社会保护)以及对特定群体的影响,对劳动力市场产生了严重影响,这些群体更容易受到不利的劳动力市场结果的影响。在这种情况下,提供尽可能接近当前的失业率预测,作为罗马尼亚劳动力市场的核心指标,包括其受到市场冲击的影响,变得至关重要。因此,本文旨在使用 2021-2022 年的单变量和多变量时间序列模型,为罗马尼亚失业率提供有价值的预测,突出主要的演变模式。基于单变量时间序列模型,本文使用自回归整合移动平均(ARIMA)和门限自回归(SETAR)模型,根据 2000 年 1 月至 2021 年 4 月的月度数据,预测失业率的未来值。从多变量时间序列模型的角度来看,本文使用向量自回归(VAR)/向量误差修正模型(VECM),根据 2000 年第一季度至 2020 年第四季度的季度数据,分析变量之间的时间相关性。实证结果表明,SETAR 和 VECM 在准确性方面提供了非常相似的结果,很好地复制了大流行前时期(2018 年第二季度至 2020 年第一季度),在 2020 年初达到 4.1%的水平,呈下降趋势,分别在 2022 年底达到 3.9%和 3.6%。