Gri Markov Modeli ile Türkiye’de İşsizlik Oranı Tahmini

Gelişmemiş ve gelişmekte olan ülkelerin kronik problemlerinden birisi olan işsizlik, özellikle küresel kriz zamanlarında gelişmiş ülkelerde dahi gündem haline gelebilmektedir. Bu sebeple işsizlik, dünya ülkelerinin ortak problemi olarak düşünülebilir. Dolayısıyla bu kadar önemli bir ekonomik değişkenin geçmiş dönemlerde aldığı değerlerden hareketle gelecek dönemlerde alabileceği değerlerin tahmin edilmesi çok önemli hale gelmektedir. Günümüzde zaman serilerinin tahmini için birçok nicel teknik kullanılmaktadır. Fakat burada önemli olan tekniklerin yaptıkları tahminler neticesinde oluşan hata oranlarının minimum seviyede tutulabilmesidir. Bu anlamda, bazen hata terimlerinin modifiye edilmesi, bazen de farklı tekniklerin kombinlenmesi ile hata oranları düşürülmeye ve daha doğru tahminler elde etmeye çalışılmaktadır. Çünkü geleceğe yönelik yapılan planlar ve belirlenebilecek politikalar ancak bu öngörülerle anlam kazanmaktadır. Çalışmamızda da daha doğru tahmin yapmaya odaklanılarak, Türkiye’de işsizlik oranı GM(1,1) modeli ile tahmin edildikten sonra GM (1,1) modeline Markov zincirleri entegre edilerek Gri Markov modeli kurulmuş ve işsizlik oranı bu şekilde yeniden tahminlenmiştir. Sonuçta, GM (1,1) modelinin ürettiği tahmin sonuçları ile Gri Markov modelinin ürettiği tahmin sonuçları karşılaştırılmış ve Gri Markov modelinin yüksek doğruluk değerleriyle tahmin değerleri ürettiği görülmüştür.

Grey Markov Model for the Prediction of Unemployment Rate in Turkey

Unemployment, which is one of the chronic problems of undeveloped and developing countries, can be an agenda item even in developed countries, especially in global crises. For this reason, unemployment can be considered as the common problem of the world countries. Therefore, forecasting the values that such an important economic variable can take in the coming periods, based on the values in the past is very important. Today, many quantitative techniques are used for the prediction of time series. However, the important thing here is to keep the error rates to a minimum. In this sense, sometimes by modifying error terms and sometimes combining different techniques, error rates are tried to be reduced and more accurate predictions are tried to be obtained. Because future plans and policies that can be determined gain meaning only with these predictions. In our study, we focus on more accurate estimation results. After the estimate unemployment rate in Turkey with GM (1,1) model, the Grey Markov model is established with the help of the Markov chain and the unemployment rate is re-estimated in this way. As a result, the estimation results produced by the GM (1,1) model and the estimation results produced by the Grey Markov model were compared and it was seen that the Grey Markov model produced prediction values with high accuracy valus.

Kaynakça

Akay, D. ve Atak, M. (2007). Grey Prediction with Rolling Mechanism for Electricity Demand Forecasting of Turkey. Energy. 32(9). 1670-1675.

Chen, C. I. (2008). Application of the Novel Nonlinear Grey Bernoulli Model for Forecasting Unemployment Rate. Chaos, Solitons & Fractals. 37(1). 278-287.

Chen, K. M., Xie, L. F. ve Xiang, W. S. (2012). Traffic Accidents Prediction Using Improved Grey-Markov Model. In Advanced Materials Research, Trans Tech Publications Ltd. Vol. 378. 222-225.

Chen, L. H. ve Guo, T. Y. (2011). Forecasting Financial Crises for an Enterprise By Using the Grey Markov Forecasting Model. Quality & Quantity. 45(4). 911-922.

Chen, S., Ye, L., Zhang, G., Zeng, C., Dong, S. ve Dai, C. (2011, October). Short-Term WindPower Prediction Based on Combined Grey-Markov Model. In 2011 International Conference on Advanced Power System Automation and Protection IEEE. Vol. 3. 1705-1711.

Chen, X., Jiang, K. ve Liu, Y. (2015, August). Inflation Prediction for China Based on the Grey Markov Model? In 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). 301-306.

Deng, J. L. (1982). Control Problems of Grey Systems. Sys. & Contr. Lett. 1(5). 288-294.

Deng, J. L. (1989). Introduction to Grey System Theory. The Journal of Grey System. 1(1). 1-24.

Dong, S., Chi, K., Zhang, Q. ve Zhang, X. (2012). The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations. Journal of Ocean University of China. 11(1). 13-17.

Dumičić, K., Čeh Časni, A. ve Žmuk, B. (2015). Forecasting Unemployment Rate in Selected European Countries using Smoothing Methods.World Academy of Science, Engineering and Technology: International Journal of Social, Education, Economics and Management Engineering. 9(4). 867-872.

Edlund, P. O. ve Karlsson, S. (1993). Forecasting the Swedish Unemployment Rate VAR vs. Transfer Function Modelling.International Journal of Forecasting. 9(1). 61-76.

Floros, C. (2005). Forecasting the UK Unemployment Rate: Model Comparisons.International Journal of Applied Econometrics and Quantitative Studies. 2(4). 57-72.

Funke, M. (1992). Time‐Series Forecasting of the German Unemployment Rate.Journal of Forecasting. 11(2). 111-125.

Golan, A. ve Perloff, J. M. (2004). Superior Forecasts of the US Unemployment Rate Using a Nonparametric Method. Review of Economics and Statistics. 86(1). 433-438.

He, Y. ve Huang, M. (2005, November). A Grey-Markov Forecasting Model for the Electric Power Requirement in China. In Mexican International Conference on Artificial Intelligence. 574-582. Springer, Berlin, Heidelberg.

Hu, Y. C. (2017). Predicting Foreign Tourists for the Tourism Industry Using Soft Computing-Based Grey–Markov Models. Sustainability. 9(7). 1228.

Huang, M., He, Y. ve Cen, H. (2007). Predictive Analysis on Electric-Power Supply and Demand in China. Renewable Energy. 32(7). 1165-1174.

İçen, D. ve Günay, S. (2015). Türkiye’deki İşsizlik Oranının Bulanık Doğrusal Regresyon Analiziyle Tahmini.İstatistikçiler Dergisi: İstatistik ve Aktüerya. 8(1). 10-26.

Johnes, G. (1999). Forecasting Unemploy-ment. Applied Economics Letters. 6(9). 605-607.Karaali, F. Ç. ve Ülengin, F. (2011). Yapay Sinir Ağları ve Bilişsel Haritalar Kullanılarak İşsizlik Oranı Öngörü Çalışması.İTÜDERGİSİ/d. 7(3). 15-26.

Kurita, T. (2010). A Forecasting Model for Japan''s Unemployment Rate.Eurasian Journal of Business and Economics. 3(5). 127-134.

Lasso-Valderrama, F. ve Zárate-Solano, H. M. (2019). Forecasting the Colombian Unemployment Rate Using Labour Force Flows, Banco de la Republica de Colombia. 1073. 1-19.

Ma, H. ve Zhang, Z. (2009). Grey Prediction with Markov-Chain for Crude Oil Production and Consumption in China. In the Sixth International Symposium on Neural Networks (ISNN 2009). Springer, Berlin, Heidelberg. 56. 551-561.

Önalan, O. (2014). Currency Exchange Rate Estimation Using Grey Markov Prediction Model.Journal of Economics Finance and Accounting. 1(3). 205-217.

Proietti, T. (2003). Forecasting the US Unemployment Rate. Computational Statistics & Data Analysis. 42(3). 451-476.

Qingfu, L., Qunfang, H. ve Peng, Z. (2007, November). Application of Grey-Markov Model in Predicting Traffic Volume. In 2007 IEEE International Conference on Grey Systems and Intelligent Services. 707-711.

Rapach, D. E. ve Strauss, J. K. (2008). Forecasting US Employment Growth using Forecast Combining Methods. Journal of Forecasting. 27(1). 75-93.

Seyidoğlu, H. (1999). Ekonomik Terimler. İstanbul: Güzem Can Yayınları.

Sun, X., Sun, W., Wang, J., Zhang, Y. ve Gao, Y. (2016). Using a Grey–Markov Model Optimized by Cuckoo Search Algorithm to Forecast the Annual Foreign Tourist Arrivals to China. Tourism Management. 52. 369-379.

TÜİK (2020). http:// www . tuik . gov . tr / UstMenu . do ? metod = temelist . (Erişim: 1 Haziran . 2020 ) .

Tüzemen, A. ve Yıldız, Ç. (2018). Holt-Winters Tahminleme Yöntemlerinin Karşılaştırmalı Analizi: Türkiye İşsizlik Oranları Uygulaması.Atatürk University Journal of Economics & Administrative Sciences. 32(1). 1-19.

Wang, X. P. ve Meng, M. (2008, July). Forecasting Electricity Demand Using Grey-Markov Model. In 2008 International Conference on Machine Learning and Cybernetics. 3. 1244-1248.

Wei, S. ve Yanfeng, X. (2017). Research on China's Energy Supply and Demand Using an Improved Grey-Markov Chain Model Based on Wavelet Transform. Energy. 118. 969-984.

Wu, L., Liu, S., Liu, D., Fang, Z. ve Xu, H. (2015). Modelling and Forecasting CO2 Emissions in the BRICS (Brazil, Russia, India, China, and South Africa) Countries Using a Novel Multi-Variable Grey Model. Energy. 79. 489-495.

Xu, W., Li, Z. ve Chen, Q. (2012, January). Forecasting the Unemployment Rate By Neural Networks Using Search Engine Query Data. 45th Hawaii International Conference on System Sciences. 3591-3599.

Yücel, L. I. (2017). Türkiye'de 2012-1/2016-3 Arası Dönemde 15-64 Yaş Grubu için İstihdam Dışı Oranın Bulanık Doğrusal Regresyon Analizi ile Tahmini.Ekonometri ve İstatistik Dergisi. 27. 29-50.

Zhang, Y. (2010). Predicting Model of Traffic Volume Based on Grey-Markov.Modern Applied Science. 4(3). 46-50.

Zhan-Li, M. ve Jin-Hua, S. (2011). Application of Grey-Markov Model in Forecasting Fire Accidents. Procedia Engineering. 11. 314-318

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