Template-Type: ReDIF-Article 1.0 Author-Name: Kutluk Kağan SÜMER Author-Email: kutluk@istanbul.edu.tr Author-Workplace-Name: İstanbul Üniversitesi Title: Monte Carlo Methods and Sümer?s New Jump Diffusion Processes and their Application in Gold Price Abstract: This study aimed to execute Monte Carlo simulation method with Wiener Process, Generalized Wiener Process, Mean Reversion Process and Mean Reversion Jump Diffusion Process and to compare them and then expended with the idea of how to include negative and positive news shocks in the gold market to the Monte Carlo simulation. By enhancing the determination of the 3 standard deviation shocks within the process of Classic Mean Jump Diffusion Process, an enchanted model for the 1,96 and 3 standard deviation shocks were being used and additionally positive and negative shocks were added to the system in a different way. This new Mean Reversion Jump Diffusion Process that have been developed by Sümer, executes Monte Carlo simulation regarding the gold market return with five random variables that are chosen from Poisson distribution and one random variable chosen from the normal distribution. Additionally, by accepting volatilities as outlies over the 1,96 and 3 standard deviations with the effect of the new and good news and the standard deviations on the traditional approximate return and the standard deviations (volatility) and the obtained new approximate return and the new standard deviation (volatility) and compares them with the Monte Carlo simulations Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 85-99 Volume: 5 Issue: 5 Year: 2016 Month: Feb DOI: 10.17740/eas.stat.2017â€�V8â€�06 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/928 File-Format: Application/pdf Handle: RePEc:eas:econst:v:5:y:2016:i:5:p:85-99 Template-Type: ReDIF-Article 1.0 Author-Name: Kadir GÜÇ Author-Email: kadirguc1988@gmail.com Author-Workplace-Name: Kara Harp Okulu, Gazi Üniversitesi Author-Name: Emel BAŞAR Author-Email: ebasar@gazi.edu.tr Author-Workplace-Name: Gazi Üniversitesi Title: Categorical Regression Based on Optimal Scaling and An Application Abstract: Logistic regression analysis which aims to explain the models whose dependent variable is categorical is frequently used in social science studies. In comparison with alternative techniques, exiguity of assumptions and easy interpretation of outputs make logistic regression analysis attractive. Similarly, the techniques which are known optimal scaling are used in the analysis of categorical variables. Just like logistic regression, another optimal scaling technique that aims to explain the models whose dependent variable is categorical is categorical regression analysis. Also, similar to logistic regression analysis, categorical regression has few assumptions and produce highly effective solutions. In this study the structure and properties of categorical regression analysis that based on optimal scaling are investigated. For this purpose, optimal scaling techniques are explained briefly, then information about the structure of the categorical regression analysis is described. In the application part of the study, by benefiting from the study named �Sociological Analysis of Southeast Problem� by Bilgi� and Aky�rek (2009), the relationships between the confidence level in media and various demographic variables will be investigated using categorical regression analysis. Thus, it is deduced that the categorical regression could be an alternative technique to logistic regression and models involving categorical variables were concluded with the categorical regression analysis can be made. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 14-27 Volume: 5 Issue: 5 Year: 2016 Month: Feb DOI: 10.17740/eas.stat.2016â€�V5â€�02 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/929 File-Format: Application/pdf Handle: RePEc:eas:econst:v:5:y:2016:i:5:p:14-27 Template-Type: ReDIF-Article 1.0 Author-Name: Necati Alp ERİLLİ Author-Email: aerilli@cumhuriyet.edu.tr Author-Workplace-Name: Cumhuriyet Üniversitesi Author-Name: Kamil ALAKUŞ Author-Email: kamilal@omu.edu.tr Author-Workplace-Name: 19 Mayıs Üniversitesi Title: PARAMETER ESTIMATION IN THEIL-SEN REGRESSION ANALYSIS WITH JACKKNIFE METHOD Abstract: Regression analysis; including the cause - result relationship examines the relationship between dependent and independent variables. Estimation is one of the most widely used statistical analysis techniques. Parametric regression analysis is based on some assumptions. The most important of these assumptions, the dependent and independent variables is known of the relationship between forms. In cases not provided estimates of the assumptions made, they are not qualified to be a good estimate. In this case, in order to make better predictions that allow stretching of linearity assumption in the parametric regression methods are needed. These methods are the regression model known as nonparametric and semi-parametric regression methods. Nonparametric regression analysis, is the methods that are successful for some of the assumptions used in case of failure to provide valid parametric regression methods. Jackknife method throwing an observation at a time from the sample which statistics calculates that as the number of individuals in the sample and the effect of extreme values can be defined as a method with relieving properties. In this study, it is proposed for Theil-Sen nonparametric regression analysis using the Jackknife method. Theil-Sen mean and median of the estimates also made for proposed Jackknife method. The results obtained were compared with the jackknife method OLS and Theil-Sen methods. Theil-Sen has been determined that the jackknife results in better outcomes. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 28-41 Volume: 5 Issue: 5 Year: 2016 Month: Feb DOI: 10.17740/eas.stat.2016â€�V5â€�03 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/930 File-Format: Application/pdf Handle: RePEc:eas:econst:v:5:y:2016:i:5:p:28-41 Template-Type: ReDIF-Article 1.0 Author-Name: İpek TEKİN Author-Email: itekin@cu.edu.tr Author-Workplace-Name: Çukurova Üniversitesi Title: The Economic and Demographic Determinants of Savings in East Asia: GMM-System Approach Abstract: Private saving rates which are the main source of investment are low not only in some emerging economies but also in industrialized countries for which it is essential and crucial to analyze the determinants of saving behavior in some of these countries. On the other hand, East Asian countries? success -particularly developing ones- is striking in terms of high saving rates. The aim of the present study is to analyse the determinants of private savings in East Asian countries that have higher rates among the other regions. In this regard, a private saving model is estimated by using Panel data analysis method that dynamic panel data analysis is specifically and differently employed. In accordance with this purpose and in line with Life Cycle Income Hypothesis, GDP growth rate, real interest rate and age dependency rate are taken into consideration as the determinants of saving behavior. Apart from these variables, other economic and demographic factors are also supposed to determine savings and included in the estimated model through which the study is more extensive than the earlier ones. According to the estimated model which is the most similar to standart life cycle model, growth and one lagged value of private saving have a positive impact on saving while dependency rate, real interest rate and government saving have a negative impact. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 42-59 Volume: 5 Issue: 5 Year: 2016 Month: Feb DOI: 10.17740/eas.stat.2016â€�V5â€�04 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/931 File-Format: Application/pdf Handle: RePEc:eas:econst:v:5:y:2016:i:5:p:42-59 Template-Type: ReDIF-Article 1.0 Author-Name: Ferda Yerdelen TATOĞLU Author-Email: yerdelen@istanbul.edu.tr Author-Workplace-Name: İstanbul Üniversitesi Title: Various Approaches for the Estimation of the Three-Dimensional Fixed and Random Effect Models Abstract: Two dimensional panel data models which include unit and/or time effect is inadequate sometimes for the analysis of the economic theory every aspect, therefore a need for multi-dimensional data model with more than two effects. On this need, recently studies in econometric literature have focused on how to estimate the multi-dimensional panel data model. In the estimation of the multi-dimensional panel data models with more than one unit and time effects nested each other, it is very important for which hypothesis and specifications is included in the model of the effects to obtain unbiased and efficient parameter estimators. While to estimate with fixed effects, within transformation vary depending on the whether the combination of included effects or individually and it varies the meaning of the model. The purpose of this study, to consider the alternative models it can be derived to estimate three dimensional fixed and random effect panel data models and to choose between them. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 60-70 Volume: 5 Issue: 5 Year: 2016 Month: Feb DOI: 10.17740/eas.stat.2016â€�V5â€�05 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/932 File-Format: Application/pdf Handle: RePEc:eas:econst:v:5:y:2016:i:5:p:60-70