Forecasting Unemployment Rate in the Aftermath of the Covid-19 Pandemic: The Turkish Case

Abstract

The coronavirus (Covid-19) pandemic caused the loss of lives, global problems, and the collapse of economies. Especially, the high unemployment rates in developing countries at present makes the unemployment rate predictions important. The aim of this study is to estimate the unemployment rate for the future by ARIMA and Artificial Neural Networks (ANN) models for Turkey. The contribution of the study to the literature is to estimate the unemployment rate in Turkey in the aftermath of the Covid-19 by ARIMA and ANN models. In the study, the Box-Jenkins method was used to find the appropriate ARIMA process. Then, the estimated performance of the results obtained up to 2021M8 unemployment rates in Turkey have been compared in the framework of criteria for success. Our results show that ANN was more successful than the ARIMA model in estimating the unemployment variable. It seemed that the unemployment rate estimated by the model is very close to the actual unemployment rate. According to the model results, in the aftermath of Covid-19, the unemployment rate in Turkey will be occurred over 5% of the natural rate of unemployment.

Keywords

• Covid-19
• Unemployment
• Artificial Neural Networks
• ARIMA
• Turkey

Covid-19 Salgını Sonrası İşsizlik Oranının Tahmini: Türkiye Örneği

Abstract

Koronavirüs (Covid-19) salgını can kaybına, küresel sorunlara ve ekonomilerin çökmesine neden olmuştur. Özellikle gelişmekte olan ülkelerdeki yüksek işsizlik oranları, işsizlik oranı tahminlerini önemli hale getirmektedir. Çalışmanın amacı, Türkiye için ARIMA ve Yapay Sinir Ağları (YSA) modelleri ile geleceğe yönelik işsizlik oranını tahmin etmektir. Çalışmanın literatüre katkısı, Covid-19 sonrasında Türkiye'deki işsizlik oranını ARIMA ve YSA modelleri ile tahmin etmektir. Çalışmada, uygun ARIMA sürecini bulmak için Box-Jenkins yöntemi kullanılmıştır. Ardından, Türkiye'de 2021M8 dönemine kadar işsizlik oranlarından elde edilen sonuçların tahmini performansı kriterlere göre karşılaştırılmıştır. Bulgular, YSA'nın işsizlik değişkenini tahmin etmede ARIMA modelinden daha başarılı olduğunu göstermektedir. Model tarafından tahmin edilen işsizlik oranının gerçek işsizlik oranına oldukça yakın olduğu görülmüştür. Model sonuçlarına göre Covid-19 sonrasında Türkiye'deki işsizlik oranı doğal işsizlik oranı olan % 5'in üzerinde gerçekleşecektir.

Keywords

• Covid-19
• İşsizlik
• Yapay Sinir Ağları
• ARIMA
• Türkiye

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