摘要：During the health crisis, it is vital to protect not only the critical sectors of the economy, the assets, technology, and infrastructure, but first and foremost, it is fundamental to protect jobs and workers. The current COVID-19 pandemic has had a strong impact on the labor market from three main perspectives: number of jobs (through unemployment and underemployment), 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. The measures aiming to reduce economic activity and social contacts lead to a reduction of labor demand and implicitly to the increase of the unemployment rate. In this context, it becomes even more relevant to be able to monitor the unemployment rate, providing relevant forecasts that include the effects of market shocks. Thus, our paper aims to forecast the unemployment rate for the period 2020–2023 using the Box-Jenkins methodology based on ARIMA models, exploring also the uncertainty based on fan charts. Although the baseline forecast offers valuable information, a good understanding of risks and uncertainties related to this forecast is equally important. The empirical results highlighted an ascending trend for unemployment rate during 2020, followed by a slow and continuous decrease until the end of 2023 with a high probability for the forecast to be above the central projection.
