Nazım Ekren, İstanbul Ticaret Üniversitesi İktisat bölümünde profesördür ve İstanbul Ticaret Üniversitesi'nde Düşünce ve Proje Üretim Akademisi başkanlığı görevini yürütmektedir. Uzmanlık alanları; makro iktisat, iktisat politikası, ekonomi politik ve uygulamalı ekonomidir. Lisans ve lisansüstü öğrencilerine çeşitli dersler vermektedir. Mefule Fındıkçı Erdoğan, Finansal Ekonomi alanında doktora derecesine sahiptir. Aynı zamanda Marmara Üniversitesi Ekonometri alanında doktora yapmaktadır. Başlıca araştırma alanları arasında; finansal tahmin ve modelleme, davranışsal finans, uygulamalı makro ve mikro analiz, panel veri modelleri ve istatistiksel yöntemler bulunmaktadır. Kübra Hatice Bildik, Siyaset Bilimi ve Uluslararası İlişkiler ve Psikoloji lisans bölümlerinden mezun olmuştur. İstanbul Ticaret Üniversitesi'nde İktisat bölümü yüksek lisans öğrencisidir. Ekonomi politik, siyaset bilimi ve siyaset psikolojisi alanlarında araştırma yapmaktadır. * Çalışma kapsamında oluşturulan Yükselen Piyasa Ekonomiler Karması 2020/116892 sayı ve Mart 2021 itibari ile TURKPATENT Enstitüsü tarafından onaylanmış ve marka tescili alınmıştır. Bu bağlamda her yılın "Emerging Markets Mix-18 / EM 2 -18" ülkeleri akademik bir yayınla açıklanacaktır.
Purpose- The purpose of this study is to identify what has changed on the enterprises’ problems and solution proposals pre and during the COVID-19 period. In this context, the problem and solution proposals obtained from the "Chamber of Sworn-In Certified Public Accountant of Istanbul Economy Expectation Index and Encountered Problems and Solution" survey were evaluated on basis of sectors. Methodology- The survey used in the study has been applied quarterly since 2017 to 13 different sectors and more than 300 enterprises. In April 2020, the survey was applied as a COVID-19 thematic survey. In this study, the results of the pandemic thematic survey and the results applied in the 2019-2021 periods were compared using frequency breakdowns for 8 sectors. Findings- According to the findings, it was stated that serious losses occurred due to the exchange rate risks due to the problems experienced with the fluctuation of the exchange rate. In addition, the global economic recession has become one of the important problems of enterprises that have problems in terms of equity capital. While the profitability ratios decreased due to the contraction in demand and increase in costs, it was observed that enterprises with increasing credit costs had difficulties in accessing appropriate financing. Conclusion- According to the study, it has been demonstrated that sectors appear new problems in the pandemic. Hence, sectors' problems should be considered on a preferential basis by public authorities. It has been considered incentive that is applied by the public authority will have a great influence on enterprises. Keywords: Covid-19, sectoral problems, encountered problems and solution proposals. JEL Codes: P00, M21, M29
Purpose- In the study, the effects of sectors on the growth of OECD member countries were determined by using the Fuzzy Goal Programming method. These findings may help policymakers see sector impacts that help countries in their growth targets. The study aims to contribute to the literature in two ways. The first of these analyses are based on long-term economic growth and primary sector analysis. The second contribution is to propose an alternative empirical methodology with clustering analysis which is not used to obtain the basic assumption of homogeneity in the application of panel data analysis. Methodology- The effects of sectors on the growth of OECD member countries were determined by using the Fuzzy Goal Programming method. In the second step, countries were divided into groups using K-means clustering analysis according to these impact values. With the help of these weights, the growth dynamics of similar countries and the contributions of sectors to this dynamic were obtained. Findings- Countries analyzed in terms of the contribution of sectoral growth rates to the growth rate of the country were divided into groups by cluster analysis. It is determined that the countries grouped in terms of the contribution of sectors to growth are divided into 5 groups. The first group has 10 member countries. The second group has 12 countries and the third group it has 7 countries, the fourth group has 4 countries and only 1 country belongs to the fifth group. The countries in group 1 are Estonia, Turkey, Greece, Italy, Poland, Portugal, Lithuania, Latvia, Slovakia, and Slovenia. The countries in group 2 are Australia, Belgium, Czech Republic, Germany, Denmark, Hungary, Ireland, Mexico, Netherlands, Norway, Sweden, and New Zealand. The countries in group 3 are Austria, Spain, Finland, France, the Republic of Korea, Luxembourg, Switzerland, the USA, Israel, Costa Rica, the United Kingdom, and Japan. Conclusion- Countries that have similar sectoral structures can analyze growth with panel data analysis, but it is important to form homogeneous groups while doing this analysis. For this reason, another critical suggestion it is offered based on the study is the use of FGP methodology in the analysis method. Keywords: Economic growth, sectoral growth, Fuzzy Goal Programming, Cluster Analysis, Panel VAR JEL Codes: N10, C61, C38, C33
Purpose- In this study, a composite indicator that can be followed in the financial markets has been developed. The tourism financial conditions index has been developed in the literature for tourism based on MCI (Monetary Conditions Index) and FCI (Financial Conditions Index). The index is called the Tourism Financial Conditions Index in their studies. This study ensures that the calculation of the tourism index by adding the terror variable to the index can better reflect the expectations. Methodology- While constructing the composite indicator Z-score is used for standardization. With this standardization terrorist attacks variable is added to the composite indicator. The relationship between New Tourism Financial Composite Indicator and Tourism Stock Market Index Return is analyzed with the Threshold VAR method. This method is used because of the nonlinearity of variables. Findings: Monthly data for the period 2009:07 – 2017:12 were used in the study. The countries examined are Australia, Denmark, France, Italy, Spain, Türkiye and the UK. It is aimed to examine the relationship between the calculated NTFCI and the Tourism Stock Exchange data of each country. T-VAR results show the relationship between these variables. Conclusion - In this study, the new version of the TFCI index with terrorist incidents is presented. Chang (2015) developed the TFCI index for the tourism sector. We think that the countries should also consider the effect of terrorism in their economic and financial research in tourism area. Based on this idea, the index was expanded by adding the terror variable to the previously developed TFCI composite indicator and its relationship with the tourism stock market index as a financial indicator was examined. The TVAR model is applied which shows the short-term dynamic relationship. Models for different countries were estimated and similar results were obtained for different countries. Keywords: Tourism financial conditions index, tourism sector, monetary conditions index, financial conditions index JEL Codes: E44, E47, G32
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