Im Allgemeinen nimmt die Erwerbsbeteiligung von Frauen mit der wirtschaftlichen Entwicklung eines Landes zu, doch in Entwicklungsländern ist dieser Zusammenhang nicht immer eindeutig. Aufgrund diverser ökonomischer und sozialer Faktoren wie Wirtschaftswachstum, Bildungsniveau und gesellschaftlichen Normen spiegeln die Trends der weiblichen Erwerbsbeteiligung nicht immer einen u-förmigen Zusammenhang mit dem Bruttoinlandsprodukt wider. Jenseits der Erwerbsbeteiligung muss die Politik deshalb den Zugang von Frauen zu guten Jobs verbessern. Erwerbsbeteiligung von Frauen in Entwicklungsländern Die Verbesserung der Beschäftigungsergebnisse von Frauen erfordert nicht nur eine Steigerung der Erwerbsbeteiligung, sondern auch gute Arbeitsplätze

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Erwerbsbeteiligung von Frauen in Entwicklungsländern

Brent crude oil is considered as one of the most important sources of crude oil pricing in the worldwide market, and it is used to set the price of two-thirds of the traded crude oil supplies in the world. To predict the price of Brent crude oil, LSTM and Bi-LSTM methods are applied, which are the architecture of the recursive neural network. Initially, the database creates the appropriate data for the period January 2015 to March 2021 from Brent crude oil price signals and daily data from a financial market, and then, the modeling process is performed via the use of MATLAB software. Also, about 90% of the data are for training and the remaining for validation and comparison. Using LSTM and Bi-LSTM neural networks, the network architecture has been worked on, and by adding the number of layers and changing the solvers (SGDM, RMSProp, and Adam), the errors of different models are compared with each other. Nonlinear techniques of artificial neural networks and deep learning were used for modeling. Then, the network architecture was worked on and the model error rate was evaluated by comparing different layers and solvents such as SGDM, RMSProp, and Adam. The superiority of SGDM solvent over others was shown, and finally, it can be mentioned as the superior method of modeling of price forecasting in Brent crude oil field. The results show that the model with two layers of LSTM and SGDM solver has less error and better accuracy.

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Brent Crude Oil Price Forecast Utilizing Deep Neural Network Architectures

Since soil temperature (ST) is one of the most critical determinants affecting the soil’s physical and chemical properties, the studies on soil temperature estimation increase with the widespread use of deep learning and machine learning algorithms. This study estimates soil temperature at four depths for Giresun and Bayburt stations in Turkey employing the Bayesian Tuned Gaussian Process Regression (BT-GPR), Bayesian Tuned Support Vector Regression (BT-SVR), and Long Short Term Memory (LSTM) models. The stations were selected from semiarid (Bayburt station) and very humid (Giresun station) climates to compare the models’ performance and measure their applicability in different climate classes. Common meteorological indicators were determined as input parameters in the developed models, and a five-and-a-half-year daily dataset was used for all models. This paper represents a novel scheme to optimize the hyperparameters of kernel functions for GPR and SVR models using the Bayesian optimization method to expand predictive efficiency. The developed GPR and SVR models’ outputs are compared with LSTM via three statistical metrics comprising the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The results show that the BT-GPR model has a superior estimation ability than other developed models for the two stations. The daily ST estimation with the highest accuracy was obtained at a 5-cm depth using BT-GPR at Giresun station (RMSE = 0.0439, R2 = 0.9535, MAE = 0.0344 in the testing phase) and Bayburt station (RMSE = 0.0525, R2 = 0.9438, MAE = 0.0412 in the testing phase). These outcomes provide helpful benchmarking guidance for future soil temperature investigation at various depths across the selected regions.

Estimation of soil temperatures with machine learning algorithms—Giresun and Bayburt stations in Turkey

In Economic Development, human capital was previously seen as production factors but gradually evolved into endogenous growth theories. Most of the previous studies have examined the relationships between economic development and human capital via econometric models. Since this relationship is usually nonlinear and machine learning (ML) models can resolve it better, this study investigates the relationships by employing ML methods to provide a new perspective. For this purpose, the optimized ML methods, namely Bayesian Tuned Support Vector Machine and Bayesian Tuned Gaussian Process Regression (BT-GPR), were performed to develop the prediction model for economic development. The hyperparameters have been optimized with the Bayes method by using different kernel functions to increase SVM and GPR methods' predictive performance. The Multiple Linear Regression model has been employed to make a comparison as an econometric model. The performance of the models is evaluated using three statistical metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The BT-GPR with the exponential kernel model has superior prediction ability with the highest accuracy (R2: 0.9727, RMSE: 0.4022, MAE: 0.3728 in the testing phase). The study shows that the BT-GPR model increases the accuracy of R2 6.4%, RMSE 10.7%, and MAE 1% compared with other developed models.

Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital

The LNG price is basically determined based on the oil price, but other than that, it is also determined by the influence of the method of LNG transportation; storage; processes; and political, economic, and geographical instability. Liquefied natural gas (LNG) may not reflect its market value if the destination of the purchase is restricted or the purchase contract includes a take-or-pay clause. Furthermore, it is difficult for the buyer to flexibly manage procurement, resulting in the decoupling of oil and natural gas prices. Therefore, as the LNG bunker price is expected to be more volatile than the marine bunker price in the future, shipping companies need to prepare countermeasures based on scientific forecasting techniques. This study aims to be the first to analyze the forecasting of short-term LNG bunker prices using recurrent neural network (RNN) models suitable for highly volatile data such as time series. Predictive analysis was performed using simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) models, which effectively forecast time-series data, and the prediction performance of LSTM among the three models was excellent. LSTM had relatively excellent prediction performance of outliers and beyond. In addition, it was possible to effectively manage ship operating costs with improved forecasting in practice. Furthermore, this study contributes to establishing a systematic strategy for supervisors in global shipping companies, port authorities, and LNG bunkering companies.

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Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

Ham petrol ve petrol urunleri, endustriyel uretimin onemli girdileri arasinda oldugu kadar lojistik ve tasimacilikta da kritik bir rol oynamaktadir. Dolayisiyla, petrol fiyatlarindaki ani artislar ve dususler kuresel ekonomilerde ve dahasi ekonomiler uzerinde dogrudan veya dolayli bir etkisi vardir. Ayrica, gelismekte olan ekonomilerdeki krizler, buyuk ekonomiler arasindaki ticaret anlasmazliklari ve petrol fiyatinin dinamik dogasi, petrol arz ve talebi uzerinde etkisi olmaktadir ve petrol fiyatinda zaman zaman oynaklik cok sert olmaktadir. Petrol fiyatlarindaki bu belirsizlikler hem tuketicilere hem de ureticilere agir potansiyel kayiplar yaratabilmektedir. Bu hizli degiskenlik ve dalgalanma nedeniyle petrol fiyatlarinin tahmin edilmesi kuresel oneme sahiptir. Bu calismada, Brent Petrol fiyatlarinin gelecekteki trendini tahmin edilebilmek icin gecmis degerleri girdi alan Uzun Kisa Sureli Bellek (LSTM) ve Facebook Prophet (FBPr) yontemleri kullanilmistir. Iki modelin petrol fiyatlari icin Haziran 1988 ile Haziran 2020 arasinda haftalik 32 yillik veri seti kullanilarak karsilastirilmis ve en uygun model belirlenmistir. Veri seti egitim ve test setleri olmak uzere iki gruba ayrilmistir; egitim seti icin ilk yirmi bes yil secilirken ve son yedi yil ise tahmin dogrulugunu onaylamak icin kullanilmistir. LSTM ve FBPr modelleri icin katsayi tayini (R2) egitim asamasinda 0.92, 0.89 ve test asamasinda 0.89, 0.62 bulunmustur. Elde edilen sonuclar incelendiginde, LSTM modelinin petrol fiyatlarindaki trendi tahmin etmek icin daha iyi sonuc verdigi gorulmustur.

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The Prediction of Brent Crude Oil Trend Using LSTM and Facebook Prophet

Economic complexity showing a holistic measure of countries' economic productive power and characteristics has become a new tool for understanding the dynamics of the economy. Examining the relationship between sustainable development and this new tool is vital in determining new policies. By applying panel data of OECD countries covering different development levels from 1996 to 2017 to a data-driven dynamic econometric model, the research provides fresh insight between sustainable development and economic complexity. The results indicate that economic complexity is significantly affected by sustainable developments’ economic indicators such as GDP, FDI, R&D expenditure, social indicators such as human development, income inequality, and environmental indicators such as production-based CO2 emissions, renewable energy consumption, and greenhouse gas. The research, consequently, suggests that switching to technology and knowledge-based production processes, expanding qualified production factor capacity, raising social living standards, and making investments in the green economy will foster economic complexity while ensuring stable sustainability.

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Erdemalp Ozden

Papers

The Prediction of Brent Crude Oil Trend Using LSTM and Facebook Prophet

Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital

The Effect of Sustainable Development on Economic Complexity in OECD Countries