The government revenues and government expenditures are widely used as a means of public finance and eliminating budget deficits. However a change in the level of government expenditures or government revenues will create different effects on the economy depending on the relationship between the aggregates in question. The aim of this study is to exhibit the relationship between government expenditures and government revenues for Turkey in order to serve to determine the favorable policy. For that purpose, the relationship is tested by using Central Government Budget Revenue Realization and Central Government Budget Expenditure series from the Republic of Turkey Ministry of Finance with 2006 January -2015 April monthly data. In addition, by employing government revenues and expenditures in a disaggregated manner, the relationships about general government revenues, the revenues of regulatory and supervisory foundations, the revenues of private budgeted foundations, interest payments and non-interest payments are tried to be presented. Granger causality test is preferred as we have stationary series and also as this methodology is reliable, simple and widely used in literature.
Purpose: This paper aims to test the accuracy of some Machine Learning (ML) models in forecasting inflation in the case of Turkey and to give a new and also complementary approach to time series models. Methods: This paper forecasts inflation in Turkey by using time-series and machine learning (ML) models. The data is spanning from the period 2006:M1 to 2020:M12. Findings: According to our findings, although the linear-based Ridge and Lasso regression algorithms perform worse than the VAR model, the multilayer perceptron algorithm gives satisfactory results that are close to the results of the time series algorithm. In this direction, non-linear machine learning models are thought to be a reliable complementary method for estimating inflation in emerging economies. It is also predicted that it can be considered as an alternative method as the amount of data and computational power increase. Implication: The findings are expected to be useful as a guide for central banks and policy-makers in emerging economies with volatile inflation rates. Originality: We evaluate the forecasting performance of ML models against each other and a time series model, and investigate possible improvements upon the naive model. So, this is the first study in the field, which uses both linear and nonlinear ML methods to make a comparison with the time series inflation forecasts for Turkey.
Bu çalışmanın amacı kamu transfer harcamaları ve iktisadi büyüme ilişkisinin dinamik bir yöntem ile gelişmekte olan ülkeler için sınanmasıdır. Literatürde yer alan önceki çalışmaların model spesifikasyonu, örneklem ve yöntemlerine ilişkin eksiklikler giderilerek oluşturulan ekonometrik model; sistem GMM yöntemi kullanılarak 27 gelişmekte olan ülke verileri (1990-2011) ile test edilmiştir. Çalışmanın sonucunda kamu transfer harcamalarının uzun dönem iktisadi büyüme üzerindeki etkisi istatistiksel olarak anlamlı ve pozitif yönde bulunmuştur. Bu bağlamda, kamu transfer harcamaları gelişmekte olan ülkelerde uzun dönemde üretken kamu harcaması niteliğinde olup düşük gelir düzeyi sorununun çözümüne hizmet etmektedir.
From the 1980s to onwards trade liberalization policies have been widely used in many countries. This process has significant impacts on many economic aspects one of which is on the labour market. However, the direction of the relationship between trade reforms and the labour market is controversial. This study aims to analyse the effects of a specific trade reform of import tariff changes on the formal and informal labour market for Turkey. For that purpose, we benefit from Computable General Equilibrium (CGE) Model that relies on nonlinear simultaneous equations. We construct an updated Social Accounting Matrix (SAM) which is compatible with our model. Our findings indicate that while there is a positive relationship between formal labour employment in total and import tariff rates, the negative relationship occurs between informal employment and tariff rates.
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