Purpose-In this paper, we apply the Grey Cobb-Douglas production model to predict the GDP, examine the effects of the variation rate of capital, and labor inputs to economic growth. Many factors contribute to economic growth, such that technological progress, labor force, capital accumulation, the optimal using of sources, energy, institutional innovation ext. In reality, a variate of economic factors often intertwine with each other. Methodology-The capital and labor are main elements of economic growth. Improving the capital and labor performance plays important role in increase the wealth of a country. Traditionally, Cobb-Douglas (C-D) production model use only capital stock and labor to describe the economic growth. In this study, firstly C-D production function is established and confirmed that the capital and labor has a positive impact on economic growth (GDP). Then GM(1,1) prediction model is used to predict the future values of capital stock and labor force inputs. Findings-The future GDP values are predicted by the estimated capital and labor values putting into the Cobb-Douglas model. We also obtained the production elasticities of capital and labor inputs. Findings suggest that the contribution rate of capital is 0.403 and labor is 1.094 to economic growth. The sum of the contributions of factors is 1.497 and greater than one. Conclusion-Findings of this empirical studies shows that percentage of the increase in GDP is greater than that of the increase in capital stock and labor.

Recently, gene selection has played an important role in cancer diagnosis and classification. In this study, it was studied to select high descriptive genes for use in cancer diagnosis in order to develop a classification analysis for cancer diagnosis using microarray data. For this purpose, comparative analysis and intersections of six different methods obtained by using two feature selection algorithms and three search algorithms are presented. As a result of the six different feature subset selection methods applied, it was seen that instead of 15,155 genes, 24 genes should be focused. In this case, cancer diagnosis may be possible using 24 candidate genes that have been reduced, rather than similar studies involving larger features. However, in order to see the diagnostic success of diagnoses made using these candidate genes, they should be examined in a wet laboratory.

No abstract

Purpose-The aim of this study is modelled by examining the trading volumes of the tourism companies located in the high-risk tourism sector and traded in BIST. This modelling will gain point of view for the tourism firms as well as make an important contribution to the decision making of investors who want to invest in this sector. Methodology-The study is conducted for a sample of 2803 daily trading volumes over the period 01.01.2007-28.09.2017. Then, it is used daily returns rather than daily trading volume data because it provides the ability to measure investment performance independently of the scale used. Daily return data is modelled with stable distributions used with increasing interest in many application areas and that are well-suited to financial asset returns. Parameter estimates is made by using quantiles method which is one of the most known estimation methods. Findings-By means of the Chi-square test and graphs, it is seen that normal distribution was not suitable for trading volume data. Stable distribution parameters for the log-returns data are estimated according to the quantiles method and obtained the stable parameters , , and . Stable density function is obtained using the MATLAB STBL command according to estimated parameters. Conclusion-Estimated parameter values indicate that stable distributions can be used as a suitable model for modelling the transaction volume data of analysed index. It has been concluded that it is more appropriate to use the scale parameter of the stable distribution instead of the standard deviation as the risk measure.

No abstract

Discussions on the production function have always taken care of the attention of economists. The production function is a mathematical expression that shows the relationship between inputs and outputs. The characteristics of this relationship can be expressed in three different concepts, scale flexibility, output flexibility, and substitution flexibility, respectively. Gross Domestic Product (GDP) is an indicator of economic growth. This study aims to estimate the Cobb -Douglas production function in developing countries by using capital, labor, and energy consumption input factors and investigate the effect of economic input factors on economic growth. For this purpose, the Cobb -Douglas production model was created using capital, labor, and energy consumption inputs. In this study, linear panel data analysis techniques were used for 22 developing countries with the data of the 1980-2016 period. Output elasticity of capital, labor, and energy consumption inputs in Cobb -Douglas production function is 0.602, 0.455, 0.147, respectively, which means that the economies of developing countries are capital intensive. The total share of all production factors is 1.204, and there is an increasing return to scale. Capital, labor, and energy consumption inputs of these economies have a positive impact on GDP. In addition, insufficient capital in these countries can be compensated by labor and/or energy.

Purpose-The contribution of tourism sector to the national economy is crucial. But the sector has a structure which is always hold risks and uncertainties. For this purpose, the distribution of daily trading volumes of the tourism companies that are located in the high-risk tourism sector and traded in BIST will be modelled. Methodology-As the distribution of BIST Tourism trading volume data does not suitable for normal distribution, it is modeled by analyzing with stable distributions. Findings-The parameters of stable distribution are estimated according to the quantiles method which one of the most used estimation methods. Conclusion-Estimated parameter valuesshow that the stable distributions can be used as an appropriate model for daily trading volume of BIST tourism index.

In this paper, we investigate the effect on economic growth (GDP) for China’s economy with capital, labor and energy input factors by using CES production function and Translog production function. The empirical findings of the study showed that CES, consisting of capital and labor factors, is less efficient than the Translog function consisting of capital, labor and energy input factors for GDP estimation.The Ridge regression method is used to the parameter estimation of Translog production function using historical data because there is collinearity between variables. Then, based on the fitted Translog production model including capital, labor and energy input factors, the results of the output elasticities for each of the factors and the substitution elasticities between input factors have been dynamically estimated. To predict the future economic growth of the China economy, the inputs of Translog production model are predicted by using Holt-Winter’s method. The elasticities of the output of all input factors are positive. According to degrees of the effect on GDP, we can list the factors as labor, capital and energy, respectively. This situation represents the China economy is labor and capital intensive.