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Forecasting the Romanian Unemployment Rate in Time of Health Crisis—A Univariate vs. Multivariate Time Series Approach

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  • Adriana AnaMaria Davidescu

    (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)

  • Simona-Andreea Apostu

    (Department of Statistics and Econometrics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
    Institute of National Economy, 050711 Bucharest, Romania)

  • Aurel Marin

    (Department of Physical Education and Sports, National University of Physical Education and Sports, 060057 Bucharest, Romania)

Abstract

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.

Suggested Citation

  • Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Aurel Marin, 2021. "Forecasting the Romanian Unemployment Rate in Time of Health Crisis—A Univariate vs. Multivariate Time Series Approach," IJERPH, MDPI, vol. 18(21), pages 1-31, October.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11165-:d:663573
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    2. Catalin Popescu & Simona Andreea Apostu & Irina Gabriela Rădulescu & Jianu Daniel Mureșan & Alina Gabriela Brezoi, 2024. "Energizing the Now: Navigating the Critical Landscape of Today’s Energy Challenges—An In-Depth Review," Energies, MDPI, vol. 17(3), pages 1-26, January.

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