Forecasting of Unemployment and Economic Growth for Turkey: ARIMA Model Application

Year 2021, , 12 - 22, 29.08.2021

Uğur Ayık , Gökhan Erkal

https://doi.org/10.34110/forecasting.917300

Abstract

The main purpose of the study is to obtain the forecasting values of unemployment rate and economic growth in the coming years. Since unemployment and economic growth series are not stationary series in level I(0), the preferred model for forecasting is the Autoregressive Integrated Moving Average (ARIMA) model. Models determined for unemployment and economic growth forecasting with the help of model measurement criteria are ARIMA (2,1,1) and ARIMA (1,1,0) models respectively. In the study, the period between 1988-2017 has been considered as the prediction period and the forecasting values obtained for this period have been compared graphically with the actual values and the success of the forecasting has been evaluated. Since the forecasting power of the models is successful, forecasts have been made for the 2018-2019 (ex-post) period and it has been determined that the error rate between forecasting values and actual values is at a level to be considered good. Forecasting values for the 2020-2025 (ex-ante) period have been designed, it has been observed that unemployment rates will increase in a fluctuating manner in the coming years and economic growth is in a constant rising trend.

Keywords

Unemployment Rate, Economic Growth, ARIMA Model, Ex-Post Forecasting, Ex-Ante Forecasting

Supporting Institution

Atatürk University

References

• [1] B. Ali, T. Altintas, Time Series Analysis: A Case Study on Forecasting Turkey’s Inflation and Unemployment, Journal of ABMYO, 39 (2015), 65-75.
• [2] B. Meyer, M. Tasci, Lessons for Forecasting Unemployment in the United States: Use Flow Rates, Mind the Trend, Federal Reserve Bank of Atlanta Working Paper Series, No:2015-1 (2015)
• [3] M. Tasci, C. Treanor, Forecasting Unemployment in Real Time during the Great Recession: An Elusive Task, Federal Reserve Bank of Cleveland Economic Commentary, No:2015-15 (2015)
• [4] S. Karlsson, F. Javed, Modeling and Forecasting Unemployment Rate In Sweden Using Various Econometric Measures (Masters in Applied Statistics), Örebro: Örebro University School of Business (2016)
• [5] U. Mahmudah, Predicting Unemployment Rates in Indonesia, Economic Journal of Emerging Markets, 9(1) (2017), 20-28.
• [6] A. Tuzemen, C. Yildiz, Holt-Winters Forecast Methods’ Comparative Analysis: Turkey Unemployment Rates Implementation, Ataturk University Journal of Economics and Administrative Sciences, 32(1) (2018), 1-18.
• [7] N. Dritsakis, P. Klazoglou, Forecasting Unemployment Rates in USA Using Box-Jenkins Methodology, International Journal of Economics and Financial Issues, 8(1) (2018), 9-20.
• [8] O. Claveria, Forecasting the Unemployment Rate Using the Degree of Agreement in Consumer Unemployment Expectations, Journal for Labour Market Research, 53(3) (2019), 1-10.
• [9] T. Wang, Forecast of Economic Growth by Time Series and Scenario Planning Method-A Case Study of Shenzhen, Modern Economy, 7 (2016), 212-222.
• [10] P. Higgins, T. Zha, K. Zhong, Forecasting China’s Economic Growth and Inflation, Nber Working Paper Series, National Bureau of Economic Research, No. 22402 (2016),1-23.
• [11] C. Chuku, A. Simpasa, J. Oduor, Intelligent Forecasting of Economic Growth for Developing Economies, International Economics,159 (2019), 74-93.
• [12] H. Erdogdu, Forecasting Turkey’s GDP Growth with Mixed Data Sampling (MIDAS) Method, Iğdır University Journal of Social Sciences, 22 (2020), 519-541.
• [13] G. Gecgil, Y. Akgul, A Study on the Prediction of Neural Network Value of Turkey’s GDP, Journal of Quantitative Sciences, 2(1) (2020), 61-77.
• [14] S.V. Jeric, D. Zoricic, D. Dolinar, Analysis of Forecasts of GDP Growth and Inflation for the Croatian Economy, Economic Research-Ekonomska Istrazivanja, 33 (2020), 310-330.
• [15] Turkey Statistical Institute, Basic Statistics, https://tuikweb.tuik.gov.tr/UstMenu.do?metod=temelist, (Accessed in December 2020).
• [16] World Bank, DataBank, World Development Indicators, https://databank.worldbank.org/source/world-development-indicators, (Accessed in December 2020).
• [17] R. Tari, Econometrics. Extended 13th Edition, Kocaeli: Umuttepe Publications (2018)
• [18] D. N. Gujarati, D. C. Porter, Basic Econometrics. Translations U. Senesen and G. G. Senesen. 5th Edition, İstanbul: Literature Publications (2018)
• [19] A. Kutlar, Applied Econometrics. Improved 3th Edition, Ankara: Nobel Publication Distribution (2009)