The causal relationship between energy consumption and economic growth has been a controversial subject of the empirical literature. However, there is no common consensus neither on the existence nor on the direction of the causal relationship between energy consumption and economic growth. The purpose of this study is to investigate the causal relationship between energy consumption and economic growth using a consistent data set. Recently developed Granger causality tests in panel data models are used to uncover the existence and direction of causality between energy consumption economic growth in 21 low-income, 35 middle-income, and 26 high-income countries over the period 1990&ndash / 2004. The empirical results explicitly support that the Granger causality from energy consumption to economic growth is more common in high-income countries than low-income and middle-income countries. Furthermore, the Granger causality from economic growth to energy consumption is more common in low-income and middle-income countries than high-income countries.

To the best of my knowledge, there has not been any academic study about capital structure of Turkish REITs so far. This study attempts to fulfill this gap in the literature by analyzing the capital structure choices of Turkish REITs which are listed in Istanbul Stock Exchange (ISE) over the period of 1998 - 2007. The key contribution of this study is to understand whether the firm specific, institutional and country specific factors that affect the capital structures of all institutional firms including REITs in developed and developing countries are also applicable to the Turkish REITs sector. The data analysis demonstrates that Turkish REITs employ little long term debt in their capital structure and there exists strong short term debt dominance in the sector. Employing Tobit regression and panel data models, it is concluded that capital structure determinants that are significant in developed and developing countries are also significant in Turkish REITs&amp / #8217 / debt financing choices. However, we observe inconsistency in the sign and significance of some factors which give a way to understand the different institutional and country specific factors of Turkish real estate market and Turkish REITs.

In this study, I aimed to examine the impact of different levels of education on real GDP (and real GDP per workforce) in Turkey, and hence the relationship between different levels of education and the standard of living is estimated by panel data techniques. The panel data set in the study is constructed by pooling 67 provinces of Turkey over the period of 1975-2000. Furthermore, in the empirical work, two models are employed: the model introduced by Knowles (1997) and the augmented Solow model with different levels of education. The panel data estimation of the Knowles&rsquo / s model implies that the secondary level of schooling has the greatest contribution to real GDP, while the augmented Solow model implies that the higher level of schooling has the largest impact on real GDP per workforce.

The primary aim of this study is to explore the connection between rural - urban migration and unemployment in Turkey and examine whether this internal migration has an effect on increasing the unemployment rates. By using Two Stage Least Squares (2SLS) panel data techniques and fully identifying these very concepts: migration from rural areas to urban areas, unemployment and internal migrations effects on the unemployment, an attention will be taken to the (negative) impact of internal migration on unemployment in Turkey.

In this study, we aim to investigate the impact of inflation targeting (IT) and the recent global disinflation on exchange rate pass-through (ERPT) using quarterly data from 1980:1 to 2009:1 for 51 industrial and emerging market (EM) countries. To this end, we employ not only the conventional panel data estimation methods but also the recent Common Correlated Effects Pooled estimation procedure by Pesaran (2006) which allows estimating the impact of common global shocks such as global inflation. We also explore some other determinants of ERPT during the recent global disinflation period. Furthermore, we consider asymmetric effects of positive and negative output gaps as proxies for domestic demand conditions on ERPT for IT industrial and EM countries. Our results strongly suggest that, for the non-IT samples, ERPT is significantly higher in EM countries than industrial countries. For every country groups excluding Euro area countries, we find that ERPT declined substantially during the recent global disinflation period. The decline in the ERPT is, however, much higher in IT countries especially in EM ones. One striking result is the convergence of ERPT coefficients of EM countries to industrial IT countries with the adoption of IT. This supports the endogenous response of ERPT to monetary policy credibility and price stability. Consequently, a high ERPT, per se, may be interpreted as not a binding constraint for the adoption of IT as it tends to decline with the success of monetary policy regime. We also find that ERPT appears to be more sensitive to positive output gaps in IT industrial countries whilst it does not have such a response to positive or negative output gaps in IT emerging market countries.

Panel data, also known as longitudinal data, are composed of repeated measurements taken from the same subject over different time points. Although it is generally used in time series applications, forecasting can also be used in panel data due to its time dimension. However, there is limited number of studies in this area in the literature. In this thesis, forecasting is studied for panel data with binary response because of its increasing importance and increasing fundamental roles. A simulation study is held to compare the efficiency of different methods and to find the one that gives the optimal forecast values. In this simulation, 21 different methods, including na&iuml / ve and complex ones, are used by the help of R software. It is concluded that transition models and random effects models with no lag of response can be chosen for getting the most accurate forecasts, especially for the first two years of forecasting.

In this thesis, effects of economic crises after 1990 on the Turkish insurance sector are analyzed with special emphasis on 1994, 2001 and 2008 crises. In the first step, EGARCH model is used to measure the exchange rate uncertainty. Then, a time series model for the aggregate analysis and a panel data model for the disaggregate analysis which both include the estimated exchange rate uncertainty together with other macroeconomic and firm specific variables are set up. The results indicate that aggregate and disaggregate analyses suggest different variables in explaining the premium production which is used as a proxy for the performance of the insurance sector. Nevertheless, the common conclusion was that the growth of premium production decelerates during the crisis periods at a varying degree depending on the year of crisis. 2001 crisis is found to be the crisis which has the most detrimental impact on the Turkish insurance sector. On the other hand, effects of the 2008 crisis are found to be relatively limited.

This thesis investigates the relation between trade and convergence for Turkey and EU-15 in the period 1980-2008. The countries and time period are selected because Turkey has intensive trade relation with EU-15, and these economies had experienced conversion in their economic structures and adopted liberal economic policies, as well as liberal trade policies in this period. Using panel data methods two equations are estimated / an income dispersion equation for the impact of bilateral trade on per capita income differences and a gravity model of trade for the impact of per capita income differences on bilateral trade. Overall findings of this study give strong evidence for the hypothesis that trade causes convergence, whereas weaker support for the thesis that convergence causes trade.

Drought, which is an important result of the climate change, is one of the most serious natural hazards globally. It has been agreed all over the world that it has adverse impacts on the production of agriculture, which plays a major role in the economy of a country. Studies showed that the results of the drought directly affected the crop yields, and it seems that this negative impact will continue drastically soon. Moreover, many researches revealed that, Turkey will be affected from the results of climate change in many aspects, especially the agricultural production will encounter dry seasons after the rapid changes in the precipitation amount. Insurance is a well-established method, which is used to share the risk based on natural disasters by people and organizations. Furthermore, a new way of insuring against the weather shocks is designing index-based insurance, and it has gained special attention in many developing countries. In this study, our aim is to model weather index based drought insurance product to help the small holder farmers in the Cental Anatolia Region under different models. At first, time series techniques were applied to forecast the wheat yield relying on the past data. Then, the AMS (AgroMetShell) software outputs, NDVI (Normalized Difference Vegetation Index) values were used, and SPI values for distinct time steps were chosen to develop a basic threshold based drought insurance for each province. Linear regression equations were used to calculate the trigger points for weather index, afterwards based on these trigger levels / pure premium and indemnity calculations were made for each province separately. In addition to this, Panel Data Analysis were used to construct an alternative linear model for drought insurance. It can be helpful to understand the direct and actual effects of selected weather index measures on wheat yield and also reduce the basis risks for constructed contracts. A simple ratio was generated to compare the basis risk of the different index-based insurance contracts.

Many studies suggest the ability of the NAIC Risk-Based Capital ratio (RBC ratio) to predict insurer insolvency. Based on the US life insurer (insurer) data for the period of 2005 to 2008, this study finds explanatory variables that have a statistically significant relationship with the RBC ratio. Advantages of panel data over cross-sectional and time series data analysis are exploited to make valid inference on coefficients of the explanatory variables. Testing for unobserved insurer and time effects and for dependence between these effects and the explanatory variables indicates the appropriateness of the fixed insurer and time effects model. Based on the ordinary least squares estimates, it is found that insurers' size, capital-to-asset ratio, and return on capital have a statistically significant relationship with the RBC ratio. Additionally, health product, annuity product, opportunity, and regulatory risks of insurers are related to the RBC ratio. Accounting for heteroscedasticity and autocorrelation for a given insurer yields the same coefficient estimates, but increased standard errors. / text