Using Neural Network Auto-Regression to Forecast the Palestinian Unemployment Rate
DOI:
https://doi.org/10.1285/i20705948v17n3p586Keywords:
unemployment rate, NNAR, Forecasting, PalestineAbstract
The Palestinian labor market faces prolonged and significant challenges includinghigher rates of unemployment, which has major economic and socialimplications. This study aims to forecast the behavior of the unemploymentrate in Palestine utilizing quarterly unemployment data over the period from2001Q1 to 2023Q2. The data is divided into training (in-sample) and testing(out-of-sample) datasets. The study applies a neural network auto-regressionmodel (NNAR) to provide future predictions of the unemployment rate forthe next ten quarters (2023Q3 – 2025Q4). The forecasting performance wasassessed on both in-sample and out-of-sample datasets. The findings suggestthat the optimal model to predict the Palestinian unemployment ratewas NNAR(1,1,10)[4]. The study compared this model with auto-regressiveintegrated moving average (ARIMA) and Holt-Winter’s (HW) methods andthe results revealed that the estimated NNAR model outperformed thesemodels. The findings indicate that the unemployment rate is expected toremain high with values oscillating between 23.8 to 28.1%. Hence, this studysuggests that unemployment is a chronic economic problem with strong seasonality.The findings provide valuable insights for policy implications andstrategies to address the unemployment issue in Palestine.References
Abugamea, G. H. (2018). Determinants of unemployment: Empirical evidence from Palestine. Munich Personal RePEc Archive, Paper No. 89424.
Adebiyi, A. A., Adewumi, A. O., and Ayo, C. K. (2014). Comparison of arima and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, pages 1–7.
Chakraborty, T., Chakraborty, A. K., Biswas, M., Banerjee, S., and Bhattacharya, S. (2021). Unemployment rate forecasting: A hybrid approach. Computational Economics, 7:183–201.
Ciaburro, B. and Venkateswaran, G. (2017). Neural Networks with R. Packt Publishing.
Daoud, Y. (2006). Short term measures to alleviate unemployment in palestine. In MAS’s Annual Conference: Unemployment in the Palestinian Territory ” Reality and Strategies to Alleviate it. MAS Institute, Ramallah.
Davidescu, A. A., Apostu, S. A., and Paul, A. (2021). Comparative analysis of different univariate forecasting methods in modeling and predicting the Romanian unemployment rate for the period 2021–2022. Entropy, 23:1–32.
Dritsaki, C. (2016). Forecast of SARIMA models: An application to unemployment rates of Greece. American Journal of Applied Mathematics and Statistics, 4(5).
Dritsakis, N. and Klazoglou, P. (2018). Forecasting unemployment rates in USA using box-Jenkins methodology. International Journal of Economics and Financial Issues, 8(1):9–20.
Dumiˇciˇc, K., ˇCeh ˇCasni, A., and ˇZmuk, B. (2015). Forecasting unemployment rate in selected European countries using smoothing methods. International Journal of Social, Education, Economics and Management Engineering, 9(4):867–872.
Feuerriegel, S. and Gordon, J. (2019). News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions. European Journal of Operational Research, 272(1):162–175.
Galbraith, J. W. and van Norden, S. (2019). Asymmetry in unemployment rate forecast errors. International Journal of Forecasting, 35(4):1613–1626.
Gogas, P., Papadimitriou, T., and Sofianos, E. (2022). Forecasting unemployment in the euro area with machine learning. Journal of Forecasting, 41(3):551–566.
Gostkowski, M. and Rokicki, T. (2021). Forecasting the unemployment rate: Application of selected prediction methods. European Research Studies Journal, XXIV(3):985– 1000.
Hillis, S. A., Alaref, J. J. S., and Takkenberg, W. M. (2018). Enhancing job opportunities for skilled women in the Palestinian territories. Technical report, World Bank.
Hossain, M. I., Yagamaran, K. S. A., Afrin, T., Limon, M., Nasiruzzaman, M., and Karim, A. M. (2018). actors influencing unemployment among fresh graduates: A case study in Klang valley, Malaysia. International Journal of Academic Research in Business and Social Sciences, 8(9):1494–1507.
Husin, W. Z. W., Abdullah, N. S. A., Rockie, N. A. S. Y., and Sabri, S. S. M. (2023). Neural network model in forecasting Malaysia’s unemployment rates. MS Science Journal, 18:1–9.
Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.
ILOSTAT (2023). Unemployment rate: Ilostat labour force statistics.
Katris, C. (2019). Forecasting the unemployment of med counties using time series and neural network models. Journal of Statistical and Econometric Methods, 8(2):37–49.
Khan Jaffur, Z. R., Sookia, U. N., Nunkoo Gonpot, P., and Seetanah, B. (2017). Out-of-sample forecasting of the Canadian unemployment rates using univariate models. Applied Economics Letters, 24(15):1097–1101.
Koˇzuch, A., Cywicka, D., and Asamowicz, K. (2023). A comparison of artificial neural network and time series models for timber price forecasting. forests, 14(2):1–23.
Nagao, S., Takeda, F., and Tanaka, R. (2019). Nowcasting of the us unemployment rate using Google Trends. Finance Research Letters, 30:103–109.
PCBS (2022). Labor Force Survey Report: 2022. Palestinian Central Bureau of Statistics.
PCBS (2023). The performance of the Palestinian economy for 2023, and economic forecasts for 2024. Technical report, Palestinian Central Bureau of Statistics.
Rublikova, E. and Lubyova, M. (2013). Estimating ARIMA-ARCH model rate of unemployment in Slovakia. Prognostickˇe prˇace, 5(3):275–289.
Stasinakis, C., Sermpinis, G., Theofilatos, K., and Karathanasopoulos, A. (2016). Forecasting US unemployment with radial basis neural networks, Kalman filters and support vector regressions. Computational Economics, 47:569–587.
Tabachnick, B. G. and S, F. L. (2001). Using multivariate statistics. Pearson Education, Upper Saddle River, NJ, USA.
UN (2017). Global indicator framework for the sustainable development goals and targets of the 2030 agenda for sustainable development. In SDG Indicators. United Nations.
Vicente, M. R., Lˇopez-Menˇendez, A. J., and Pˇerez, R. (2015). Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing? Technological Forecasting and Social Change, 92:132 – 139.
Yamacli, D. S. and Yamacli, S. (2023). Estimation of the unemployment rate in turkey: A comparison of the Arima and machine learning models including covid-19 pandemic periods. Heliyon, (e12796).
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