Research on UK Unemployment Rate Forecast Based on ARIMA Model
- 1 Beijing University of Posts and Telecommunications
* Author to whom correspondence should be addressed.
Abstract
The objective of this study is to predict the unemployment rate in the UK and the Eurozone for the years 2014 to 2024 using the time series analysis model known as ARIMA. It is essential to be informed of changes to employment rates in order to address them and changes in the economic environment including historical occurrences such as Brexit, COVID-19, and shifts in the labor market. The ARIMA model was used because it fits non-stationary time series, which is a feature present in the employment rates of both time series. The monthly unemployment rates for each state were seasonally adjusted and the ARIMA parameters were estimated by the automatic method. The study observed that while the interior portion of the trading cycle was effectively predicted by the ARIMA model, it failed to do so for the short-term UK unemployment in the post-2023 period because of exogenous shocks. However, the model was equally successful in predicting a marginal contraction in unemployment rates in the Euro zone after 2023. By performing the Ljung-Box test, it was established that the residuals of the models were random and therefore, the models were fit. Accordingly, the results suggest that the estimates derived from the ARIMA model are beneficial in economic predictions, which helps governments to design better social security programs and other economic plans to address the changing employment landscape.
Keywords
UK Unemployment Rate, Eurozone Unemployment Rate, ARIMA, Economic Forecasting, Labor Market Trends
[1]. TIKHOMIROVA, T., & NECHETOVA, A. (2019). Models for the short-term and mid-term forecasting of the unemployment rate. Revista Espacios, 40 (30).
[2]. Md Hossain. (2023). Comparative Analysis of ARIMA, SARIMAX, and Random Forest Models for Forecasting Future GDP of the UK in Relation to Unemployment Rate. International Journal of Management, Accounting and Economics, 10(11), 924-937.
[3]. Mayhew, K., & Anand, P. (2020). COVID-19 and the UK labour market. Oxford Review of Economic Policy, 36, S215-S224.
[4]. Simionescu, M., Streimikiene, D., & Strielkowski, W. (2020). What does Google Trends tell us about the impact of Brexit on the unemployment rate in the UK?. Sustainability, 12(3), 1011.
[5]. Trading Economics. (2024). Unemployment rate in the United Kingdom. Retrieved from https://tradingeconomics.com/united-kingdom/unemployment-rate.
[6]. Costa Dias, M., Joyce, R., Postel‐Vinay, F., & Xu, X. (2020). The challenges for labour market policy during the Covid‐19 pandemic. Fiscal Studies, 41(2), 371-382.
[7]. Fernández-Reino, M., & Rienzo, C. (2022). Migrants in the UK labour market: An overview. Migration Observatory, 6.
[8]. Mogoș, R. I., Dinu, M., Constantinescu, V. G., & Istrate, B. (2022). Unemployment in European union during the COVID-19 pandemic. A cluster analysis. In 8th BASIQ international conference on new trends in sustainable business and consumption, 117-124.
[9]. Prohorovs, A. (2022). Russia’s war in Ukraine: Consequences for European countries’ businesses and economies. Journal of risk and financial management, 15(7), 295.
[10]. Wulfgramm, M. (2014). Life satisfaction effects of unemployment in Europe: The moderating influence of labour market policy. Journal of European Social Policy, 24(3), 258-272.
Cite this article
Yan,Y. (2024).Research on UK Unemployment Rate Forecast Based on ARIMA Model.Advances in Economics, Management and Political Sciences,123,26-35.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content
About volume
Volume title: Proceedings of ICEMGD 2024 Workshop: Policies to Enhance Sustainable Development through the Green Economy
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).