Multivariate Adaptive Regression Splines (Mars) Method For Unemployment in OECD Countries

Year 2020, Volume: 35 Issue: 3, 46 - 51, 04.01.2020

Haydar Koç , Emre Dunder , Tuba Koc

Abstract

Multivariate Adaptive Regression Splines (Mars) Method For Unemployment in OECD Countries

Abstract

Unemployment is one of the most important macroeconomic problems in all countries and it is very important task for identification of the key determinants of it. Therefore, in recent years determining the factors affecting the unemployment is attracting the researcher. In this study, the factors affecting unemployment in Organization for Economic Co-operation and Development (OECD) countries were tried to be determined. In this context, data for the years 2000-2017 were analyzed by using MARS method. For each year, we estimated the Multivariate Adaptive Regression Splines (MARS) models and we tracked the effective predictors. According to our findings, the indicators Gross domestic product (Gdp), tax revenue rate, long term interest rate, saving rate and inflation usually have a significant impact on the unemployment rates. The annual growth rate of import, export and exchange rate do not influence the unemployment ratios. Besides these results, the industrial production, the industrial value added and current account balance are influential for a few years.

Keywords

Multivariate Adaptive Regression Splines (MARS), Unemployment Rate, OECD

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