Browsing by Author "Demir, Yildirim"

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Analyzing the Effect of Employment in the Agricultural and Industrial Sectors on Economic Growth With the Ardl Bounds Test
(int Journal Contemporary Economics & Administrative Sciences, 2021) Demir, Yildirim
One of the important indicators determining the welfare level of a country is its Gross Domestic Product (GDP). However, many parameters affect GDP, and employment in agriculture and industry sectors constitute two of them. This study aims to determine the effect of employment in the agricultural and industrial sectors on economic growth in Turkey with the ARDL bounds test. Turkey's employment rate in the agricultural and industrial sectors of the years 2000-2019 and GDP data were used as material. According to the ARDL model, it was determined that there is a long-term positive relationship between A_Employment and I_Employment and GDP. It was also observed that there was no structural break in the variables. With the Toda-Yamamoto test, a one-way causality relationship from A_Employment to GDP and a two-way causality relationship between S_ employment and GDP were determined. As a result, although about 20% of total employment in Turkey is in the agricultural sector labor productivity is quite low. This situation leads to an increase in the urban population and thus a decrease in employment in agriculture. Therefore, it is recommended that economic policies be developed to increase labor productivity in the agricultural sector.
Introduction and Applicability of Nonlinear Principal Components Analysis
(Kahramanmaras Sutcu Imam Univ Rektorlugu, 2021) Demir, Yildirim; Keskin, Siddik; Cavusoglu, Seyda
Nonlinear principal component analysis (NLPCA) is a descriptive dimension reduction method that examines the relationships between variables and displays the results numerically and visually in multivariate datasets that have a linear or nonlinear relationship between them. In this study, it was aimed to present the basic explanatory information about nonlinear principal components analysis (NLPCA) and to emphasize its usability by performing application. In the study, data obtained from 270 samples for 17 continuous variables concerning 3 pepper varieties were evaluated by Principal components analysis (PCA). With the 4 principal components obtained as a result of PCA, being 3 categorical variables Variety, storage time and Application were analyzed by NLPCA. In the analysis made with PCA, approximately 74% of the total variance was explained and in the analysis made with NLPCA, approximately 58% was explained as well. As a result of the analysis; it was observed that there was a strong relationship between PC1 and storage time and variety, and PC3 and PC2 variables, while the relationship between PC4 and application variables and all variables was low. As a result; by examining the linear and nonlinear relationships between the variables in the multivariate datasets, these relationships intended to be presented in an easily interpreted and easily understandable way in two-dimensional space; it was emphasized that NLPCA can be used alone and/or together with other multivariate analysis methods.