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    Modeling and forecasting of egg production in India using time series models
    (Selçuk Üniversitesi, 2021) Al Khatib, Abdullah Mohammad Ghazi; Yonar, Harun; Abotalep, Mostafa; Mishra, Pradeep; Yonar, Aynur; Karakaya, Kadir; Badr, Amr; Dhaka, Vinti
    Aim: Eating habits have changed in India and this change has also affected protein consumption habits. The change in eating habits of egg products is an indication of this. Considering the population growth rate and the resulting increase in egg demand, the countries should increase their production of protein poultry products. Aim of the study was to obtain results for both policymakers and suppliers to develop strategies with the forecast of egg consumption. Materials and Methods: In this study, the production of Eggs in India is considered and forecasts are made by the several time series model such as the ARIMA, BATS, TBATS, and Holt’slinear trend. The testing data for the production of the egg is considered from 2015-2019. Results: It is detected that Holt’s Linear Trend Model is the best fit model for forecasting. The MAPE values are obtained as 2.137%, 5.378%, 4.681%, and 1.392% by the best-fitted models BATS, TBATS, ARIMA (1,2,2), and Holt’s Linear Trend respectively. According to Holt’s linear trend Model, the Eggs production continues its upward trend in India. The Eggs production in India would be increased from 111350.3 to 148696.9 during the period 2019-2020 to 2023-2024. Conclusion: This study might help policymakers in the Livestock sector to under standard making strategies for the future to invest in it. Furthermore, it is important to make a strategic plan for eggs export, eggs supply, eggs demand, and eggs prices by the Indian government.
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    Modeling The Causality Relationships Between Gdp/Gni and Electricity Consumption According to Income Levels of Countries By Generalized Estimating Equations
    (Selçuk Üniversitesi, 2018) Yonar, Harun; İyit, Neslihan
    Gross domestic product (GDP) and energy consumption in the economic evaluations of countries are seen as two basic concepts of development. The need for energy resources in recent years has brought countries closer to technology, but in some cases, it causes problems such as wars. It is also important to determine the economic feasibility of energy consumption as well as the feasibility of many aspects such as the origin, usage, and necessity of energy. When we look at the crises that have taken place in the last 20 years, it is once again seen that energy is the dynamism and indispensable necessity of the countries. If we look at the effect of the consumed energy on the country's economy, the first economic variable will be GDP. Interpretation and evaluation of GDP, which reveals steady growth, will give effective results on economic indicators of the country. A lot of research has been done in the literature between the amount of energy consumption (according to the sectors, type of energy used, supply, and etc.) and the GDP which is the most important indicator of the country's economy. The final relationship between these two variables has been examined in details for different countries and energy concepts. In previous studies, it is sometimes observed that energy consumption is a cause of GDP or vice versa, and sometimes a two-way causality between them is determined. On the other hand, a causality relationship can not be always determined between the variables. In this case a suitable regression model can be established without looking for causality. In this study, the causality relationship between the GDP values, categorized by five income levels, and the energy consumptions of the countries between 1980 and 2014 is determined by using the Granger causality test. When we look at the results of the causality test, we find that only one causality relationship exists between high income level countries by GDP and the energy consumption of them. According to the causality test result, dependent and independent variable are determined before generalized estimating equations (GEE) method is used for modelling the data. In GEE method, the smallest values of QIC and QICC information criteria are found in the direction of causality relationships. The same causality assessment is done between gross national incomes (GNI) of countries categorized by income levels and energy consumptions, and it is concluded that the GEE models established according to the causality relationship direction are much better fit to the data. These findings obtained from this study suggests that causality test is a guide for us when we have insufficient knowledge in determining dependent and independent variables before fitting regression models to the data.