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.

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