Durmuş Özdemir

THE PEACE DIVIDEND EFFECT OF TURKISH CONVERGENCE TO THE EU: A MULTI‐REGION DYNAMIC CGE MODEL ANALYSIS FOR GREECE AND TURKEY

This paper examines the peace dividend effect of Turkish convergence to EU membership. By employing a multi‐region dynamic CGE model, we examine the prospect for conflict resolution if Turkey becomes an EU member. The model allows us to analyse several scenarios that imply varying amounts of reduction of the military expenditure/GDP ratios. On the one hand, this change will cause a decrease in sectoral demand for military expenditures, while on the other hand, reallocation of the reduced expenditure on (i) education, (ii) tax decrease, and (iii) infrastructure, should have a huge growth impact. Our dynamic CGE simulation experiments emphasize the economic gain for all parties involved.

Economic growth and financial liberalization in the EU accession countries

This article investigates the impact of financial liberalization on aggregate productivity growth. Based on a sample of the EU accession countries and using quarterly longitudinal panel data between 1995 and 2007, the static robust and dynamic panel data estimates indicate clear evidence of a link between the long-run growth and a number of indicators of financial liberalization. The empirical results illustrate that financial liberalization is negatively related to economic growth significantly. The results imply that higher levels of post-EU-membership growth are not caused by liberalized financial markets.

The Analysis of Time Series

In this chapter, we will examine time series. The main aim of time series analysis is to try to predict the future by projecting the patterns identified in the past. It is assumed that these patterns might be the same in the near future. However, they may not be the same in the distant future. These patterns consist of various elements such as trend, seasonal factors, random factors and cyclical factors. We will firstly explain these factors and examine them within an example. We can predict the systematic components (trends, cycle and seasonal) of any time series data but not the random component.

The demand for Turkish wine: estimates of the wine price elasticities

This article examines the impacts of the recent high taxation policy on Anatolian wine demand and wine price elasticities. This article uses quarterly data between 1997 and 2013 to estimate key elasticities of the Turkish demand for wine. No prior study has estimated specific elasticities for wine consumption and the results also indicate that the high taxation policy is significantly reducing the wine demand and production in Turkey.

The impact of economic and social factors on the prevalence of hepatitis B in Turkey

BackgroundViral Hepatitis is one of the major global health problems, affecting millions of people every year. Limited information is available on the impact of social and economic factors on the prevalence of Hepatitis B virus (HBV) in Turkey. This study, contrary to other studies in the literature, was undertaken with the aim of examining the Majority of the excluded data come from the volunteers.MethodsThere are medical and the social-economic factors affecting the prevalence of HBV. This research, while taking medical factors as control variables, clarify the social and economic factors affecting the prevalence of HBV by utilising clinical data with the use of the Binary Probit Model (BPM). The BPM estimation is a powerful tool to determine not only the factors but explain also the exact impacts of each factor.ResultsThe estimations of the BPM shows that economic and social variables such as age, gender, migration, education, awareness, social welfare, occupation are very important factors for determining HBV prevalence. Compared to the youngest population, the 46 to 66+ age group has a higher prevalence of HBV. The male respondents were 5% more likely to develop HBV compared to females. When region-specific differences are taken into account, migrating from the poorest parts of the country such as the eastern and south-eastern regions of Turkey are approximately 16% more likely to be infected. The welfare indicators such as a higher number of rooms in the respondent’s house or flat decreases the probability of having HBV and, relatively higher income groups are less likely to develop HBV compared to labourers. The Self-employed/Business owner/Public sector worker category are approximately 10% less likely to develop HBV. When people are aware of the methods of prevention of HBV, they are 6% less likely to be infected. Previous HBV infection history increases the probability of having HBV again B by 17%.ConclusionsThese findings strongly suggest that the impact of social and economic factors on the prevalence of HBV is vital. Any improvements in these factors are likely to reduce prevalence of HBV.

Applied Statistics for Economics and Business

â Introduction.- 1.Collecting Data.- 2.Data presentation: Graphs, Frequency Tables and Histograms.- 3.Measures of Location.- 4.Measures of Dispersion.- 5.Index Numbers.- 6.Inequality Indices.- 7.Probability Theory.- 8.Probability Distributions.- 9.Estimation and Confidence Intervals.- 10.Hypothesis Testing.- 11.The I 2, F-Distributions and the ANOVA.- 12.Correlation.- 13.Simple Regression. Chapter 14.Multiple Regression.- 15.The Analysis of Time Series.- Appendixâ .

Measures of Dispersion

The last chapter considered several measures in which we can summarize a set of data using just one value. However, as was concluded, these single values do not say very much about the data itself and how it is dispersed (its spread). For instance, it is possible to take two separate sets of data with the same mean, but with great differences in distribution.

The Chi-Squared, F-Distribution and the Analysis of Variance

In this chapter we will consider two main, widely used distributions; the chi-squared and F distributions. Firstly we will focus on to the two similar distributions; the F and the chi-squared distributions. They share a number of common features. The main common features are that they are

Measures of Location

In the second chapter we discussed graphical techniques. Their advantage is that they provide a quick overview of data. However, they are limited in that they are not very precise and do not allow for a further analysis. The following chapter introduces some simple numerical techniques that allow us to make a further summary on data by computing an average.

Estimation and Confidence Intervals

In our first chapter we stated that statistics is the study of how to collect, organize, analyze, and interpret numerical data. In statistical inference we draw a sample of observations from a larger population. Estimation is the use of sample data in order to derive conclusions about the population. If we denote the Sample parameters as \( \overline{x},\;{S}^2 \) then the Population Parameters are μ, σ2 for the mean and variance.