Document Type : Original Research


Business School, University of Huddersfield, Huddersfield, United Kingdom


Accurate forecasting of Gross Domestic Product (GDP) is crucial for policymakers, businesses, and investors. This research explores the use of SARIMAX, ARIMA, and Random Forest models to forecast GDP in the UK. The study investigates the relationship between GDP and the unemployment rate, considering historical GDP and unemployment data collected from the Office of National Statistics (ONS). Both SARIMAX and ARIMA models indicate a negative relationship between GDP and the unemployment rate, although the coefficients are not statistically significant. On the other hand, the Random Forest model has shown its supremacy when it comes to the accuracy of prediction. The results suggest that other factors may have a stronger influence on GDP fluctuations based on the empirical findings. Future research should consider additional variables and advanced modelling techniques to further explore the relationship between GDP and the unemployment rate, contributing to a deeper understanding of the UK economy and informing effective economic management.


Main Subjects


©2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

Aisen, A., & Veiga, F. J. (2011). How Does Political Instability Affect Economic Growth? SSRN Electronic Journal.
Al-kasasbeh, O. (2022). The relationship between unemployment and economic growth: is Okun’s Law valid for the Jordan case? International Journal of Economics Development Research (IJEDR), 3(3), 217–226.
Alharbi, F. R., & Csala, D. (2022). A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach. Inventions, 7(4), 94.
Andrei, D. B., Vasile, D., & Adrian, E. (2009). The correlation between unemployment and real GDP growth. A study case on Romania. Annals of Faculty of Economics, 2(1), 317-322.
Anggraeni, W., Andri, K. B., Sumaryanto, & Mahananto, F. (2017). The Performance of ARIMAX Model and Vector Autoregressive (VAR) Model in Forecasting Strategic Commodity Price in Indonesia. Procedia Computer Science, 124, 189–196.
Anthony, U. and Emediong, U. (2021). Multivariate time series modelling of nigerian gross domestic product (gdp) and some macroeconomic variables. African Journal of Mathematics and Statistics Studies, 4(3), 12–31.
Azmi, F. B. (2013). An Empirical Analysis of the Relationship between GDP and Unemployment, Interest Rate and Government Spending. SSRN Electronic Journal.
Bouznad, I. E., Guastaldi, E., Zirulia, A., Brancale, M., Barbagli, A., & Bengusmia, D. (2020). Trend analysis and spatiotemporal prediction of precipitation, temperature, and evapotranspiration values using the ARIMA models: case of the Algerian Highlands. Arabian Journal of Geosciences, 13(24).
Cashin, D., Lenney, J., Lutz, B., & Peterman, W. (2018). Fiscal policy and aggregate demand in the USA before, during, and following the Great Recession. International Tax and Public Finance, 25(6), 1519–1558.
Chu, B. M., & Qureshi, S. (2022). Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth. Computational Economics.
Divya, K. H. and Devi, V. R. (2014). A study on predictors of gdp: Early signals. Procedia Economics and Finance, 11(14), 375–382.
Ghosh, S., & Ranjan, A. (2023). A machine learning approach to gdp nowcasting: an emerging market experience. Buletin Ekonomi Moneter Dan Perbankan, 26, 33–54.
Gonzalez, L. M., Llanto, C., & Manapat, C. (2022). The Relationship between Philippine Population, Remittances, Foreign Direct Investment, and Trade Openness on its Gross Domestic Product. Journal of Economics, Finance and Accounting Studies, 4(4), 168–201.
Hjazeen, H., Seraj, M., & Ozdeser, H. (2021). The nexus between the economic growth and unemployment in Jordan. Future Business Journal, 7(1).
House of Common Library. (2023, January 30). Economic update: Short recession looming and concern over US climate policies. UK Parliament .
Hua, S. (2022). Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis. Application of Neural Network in Mobile Edge Computing, 2022, 1–9.
Hussain, L., Ghufran, B., & Ditta, A. (2022). Forecasting Inflation, Exchange Rate, and GDP using ANN and ARIMA Models: Evidence from Pakistan. Sustainable Business and Society in Emerging Economies, 4(1), 25–32.
Juanda, R., Risky, M., & Ilham, R. N. (2023). The Influence Of Growth Of Micro Small And Medium Enterprises (Umkm) And Unemployment On Growth Indonesian Economy. International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS), 3(1), 188–202.
Khairani, F., Kurnia, A., Aidi, M. N., & Pramana, S. (2022). Predictions of Indonesia Economic Phenomena Based on Online News Using Random Forest. Jurnal Dan Penelitian Teknik Informatika, 7(2), 532–540.
Li, M., & Xu, T. (2023). Short and Long Term Tourism Demand Forecasting Based on Baidu Search Engine Data. Journal of Humanities, Arts and Social Science, 7(3), 529–539.
Maccarrone, G., Morelli, G., Spadaccini, S., & Spadaccini, S. (2021). GDP Forecasting: Machine Learning, Linear or Autoregression? Frontiers in Artificial Intelligence, 4.
  Mathers , M. (2023, January 31). Why the UK economy is worse off than Russia and other EU countries. The Independent.
Mohamed, A. O. (2022). Modeling and Forecasting Somali Economic Growth Using ARIMA Models. Forecasting, 4(4), 1038–1050.
Muma, B., & Karoki, A. (2022). Modeling GDP Using Autoregressive Integrated Moving Average (ARIMA) Model: A Systematic Review. Open Access Library Journal, 9(4), 1-8.
Olalekan, J. S., & Kamoru, J. (2020). Effect of Selected Macroeconomic Variables on the Nigeria Economy.  International Journal of Advanced Research, 8(8), 1236–1242.
Saâdaoui, F., & Khalfi, M. (2022). Revisiting Islamic banking efficiency using multivariate adaptive regression splines. Annals of Operations Research.
Shahriar, S. A., Kayes, I., Hasan, K., Hasan, M., Islam, R., Awang, N. R., Hamzah, Z., Rak, A. E., & Salam, M. A. (2021). Potential of ARIMA-ANN, ARIMA-SVM, DT and CatBoost for Atmospheric PM2.5 Forecasting in Bangladesh. Atmosphere, 12(1), 100.
Sharma, S. P., R, J., & Deepa, K. (2022). Forecasting India S&P BSE SENSEX and USA S&P-500 Benchmark Indices Using SARIMAX and Facebook Prophet Library. IEEE, 1523–1530.
Smith, E. (2023, February 10). UK narrowly avoids recession in back half of 2022 despite December slump. CNBC.
Stockhammer, E., Hein, E., & Grafl, L. (2011). Globalization and the effects of changes in functional income distribution on aggregate demand in Germany. International Review of Applied Economics, 25(1), 1–23.
Thiede, B., & Monnat, S. (2016). The Great Recession and America’s geography of unemployment. Demographic Research, 35, 891–928.
Uddin, I., & Rahman, K. U. (2022). Impact of corruption, unemployment and inflation on economic growth evidence from developing countries. Quality & Quantity, 57(2).
Velidi, G. (2022). GDP prediction for countries using machine learning models. Journal of Emerging Strategies in New Economics, 1(1), 41–49.
Voßemer, J., Gebel, M., Täht, K., Unt, M., Högberg, B., & Strandh, M. (2017a). The Effects of Unemployment and Insecure Jobs on Well-Being and Health: The Moderating Role of Labor Market Policies. Social Indicators Research, 138(3), 1229–1257.
Wang, D., Gryshova, I., Kyzym, M., Salashenko, T., Khaustova, V., & Shcherbata, M. (2022). Electricity Price Instability over Time: Time Series Analysis and Forecasting. Sustainability, 14(15), 9081.
Xue, T. (2022). Digital Infrastructure and Regional Economic Growth: An Empirical Study based on Random Forest Regression. 2022 2nd International Conference on World Trade and Economic Development (WTED 2022).
Zhang, Y., Yang, H., Cui, H., & Chen, Q. (2019). Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China. Natural Resources Research, 29(2), 1447–1464.