The causality analysis of air transport and socio-economics factors: the case of OECD countries

Air transport is one of the most important industries in the world with its rapid growth, and direct and indirect contribution to world economy. In other words, GDP, tourism and employment are the key factors causing that growth in air transport and an increase in those factors boost the demand for air transport. However, uncertainty in economy, rising unemployment and increased terrorist attacks towards tourism would be a big threat to the growth of air transport in the future. To understand the importance of the mentioned factors, we first aim to apply an econometric approach which is panel Granger causality analysis. To achieve that, data from World Bank data set for OECD countries between the year of 2000 and 2013 is used in this study. We apply Pesaran CDLM test and Friedman’s test which are preferred when the number of units (N) is higher than the time (T) to test cross-sectional dependence and we then perform Granger causality analysis in order to see whether there is a causal relationship (unidirectional or bidirectional) or not among air transport, tourism, economic growth and employment. Econometric results indicate that there is a unidirectional short run causal relationship between economic growth, tourism, employment and air transport and that those factors play an important role in the growth of air transport. In this paper, we also aim to discuss the future challenges for air transport within the frame of econometric results and statistical analysis.Publisher versio

BUSINESS SURVEYS AND REPEATED SURVEYS: A SIMULATION-BASED STUDY

The paper is a survey of repeated surveys with a few examples. Repeated surveys are surveys conducted across time. Therefore, the results appear as time series. The main question in repeated surveys is how to summarize the results, either using only the last survey, or using some (weighted) average of the most recent surveys. An example in economic statistics will be treated: the monthly business surveys in several countries. Besides, simulation results are presented based on some of the techniques proposed in the literature on repeated surveys and TRAMO-SEATS sometimes used for business surveys.info:eu-repo/semantics/publishe

SELF-REPORTED HEALTH STATUS: A MICROECONOMETRIC ANALYSIS FOR TURKEY

In this paper, we examine the effects of the demographic, health and socio-economic indicators on self-reported health status (SRH) in Turkey for the year of 2012. Independent variables taken into account in the study are formed under these three titles. The Health Survey data have been collected by Turkish Statistical Office (TURKSTAT). We first used ordered logit model as a microeconometric approach but, however, generalized ordered logit model is applied after the rejection of the parallel regression assumption. Results show that people who have a chronic disease and an accident in their life are less likely to report good health. An increase in body mass index, getting older, being a female cause a negative effect on reporting good health. Increasing income level, living in urban area, being employed have a positive effect on reporting good health. In the education category, people are more likely to report fair health but the effect decreases when the education level increases

What does Bayesian probit regression tell us about Turkish female- and male-headed households poverty?

The objectives of the study are to examine the determinants of the poverty status and to illustrate the probabilities of household poverty in Turkey using the Household Budget Survey which was prepared by the Turkish Statistical Institute, 2013. The data is reorganized as rural and urban area considering female- and male-headed households so that to analyze the determinants of household poverty. Bayesian probit regression is applied using a Markov Chain algorithm, Gibbs sampler. The results of the study show that the most effective variables, which cause a decrease of the probability of living under poverty line, are education level of bachelor for 4 years, master and PhD for female-headed households and household type of being single adult for male-headed households in urban area, working full time for male- and female-headed households in rural area. However, other most remarkable variables, which cause an increased risk of poverty, are being elderly, disabled or inoperable for male-headed households, being illiterate for female-headed households in urban area and for rural area, being elderly, disabled or inoperable for male- and marital status of being single for female-headed households