The Determinants of Foreign Direct Investment Outflows from Turkey

Cergibozan, Raif; Demir, Caner

doi:10.4018/978-1-7998-2448-0.ch028

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The p ‐value for each series is zero, implying that the series is either I(0) or I(1). To verify the stability of the model, we perform unit root and structural break tests (Perron, ), ignoring structural breaks that might lead to model misspecifications and inconsistent estimation results of model parameters (Cergibozan & Demir, ). A dummy (D94) variable was created in 1994 to account for the Brazilian trade liberalisation and economic stabilisation programmes.…”

Section: Methodsmentioning

confidence: 99%

Effects of Outward Foreign Direct Investment on Domestic Investment: The Cases of Brazil and China

Gondim

Ogasavara

Masiero

2018

J of Intl Development

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Policymakers face a dilemma over their investment‐promotion strategies for becoming more competitive in international markets. Encouraging firms to invest abroad could reduce domestic economic activity. We investigate this issue by analysing the long‐ and short‐run relationships between outward foreign direct investment and domestic investment for Brazil and China. We use a time series approach, namely autoregressive distributed lag for the period between 1975 and 2013. Our findings indicate a crowding‐in effect of outward foreign direct investment on domestic investment for both countries. Copyright © 2018 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Effects of Outward Foreign Direct Investment on Domestic Investment: The Cases of Brazil and China

Gondim

Ogasavara

Masiero

2018

J of Intl Development

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“…The renewable energy policies and the employment rate of the EU countries are studied for the 1990-2012 period using Granger causality tests and it is found out that there exists a causality relationship between the wind power plants and the employment rate (Jaraitea et al, 2015). The relationship of the foreign direct investments and the employment rate for Türkiye in the period of 1970-2005 is studied in another work using the Granger causality test and it is shown that there does not exist any causality relationship between the foreign direct investments and the employment rate (Karagoz, 2007). In another work, the relationship between the GDP and the unemployment rate in Türkiye for the 1970-2006 period is analysed utilizing the cointegration and the Granger causality tests where it is concluded that there exists a unidirectional causality relationship from the GDP to the unemployment rate (Ayhan, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Employment Rate in Türkiye Using Machine Learning Methods

Tuncsiper

2023

Int J Res Rev

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Employment rate is a crucial economic indicator that measures the percentage of the working-age population that is employed. Considering this importance, the employment rate of Türkiye is modelled in this work. The employment rate, gross domestic product, gross fixed capital formation, government expenditures, export and import data of the 1991-2021 period are taken from the World bank database and then the seasonal-trend decomposition of these data are performed in EViews software. After inspecting the nonlinearity of the data, a nonlinear machine learning model namely a feedforward artificial neural network model is developed in Python programming language for the modelling of the employment rate dependent on the gross domestic product, gross fixed capital formation, government expenditures, export and import data. The 70% of the available data is used as the training data and the developed feedforward artificial neural network is trained with a successful convergence despite the low number of samples thanks to its optimal structure. Then the remaining 30% of the available data are utilized as the test data. The actual employment rate data and the result of the developed feedforward neural network model are plotted on the same axis pair which show continuous overlap in a wide range. Apart from the visual inspection of the results of the developed model, the performance metrics are also calculated in Python programming language such as the mean absolute percentage error, coefficient of determination, root mean square error and the mean absolute error. The performance metrics of the results of the developed model also verify the high accuracy of the model. It is argued that the developed feedforward neural network model can also be used for the situations where low number of samples need to be modelled thanks to the optimal structure. Keywords: Employment rate, modelling, machine learning, artificial neural networks, estimation.

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“…A brief overview of existing literature relevant for this research is listed in Table 1. Likelihood (PPML) Estimation [24] 2010-2016 Gravity model lower institutional quality and higher political risks affected negatively on Germany's direct investment [25] 1996 Next, the research by [27] could not find evidence that FDI inflows are influenced and predicted with a country business regulatory environment neither in Central Eastern European nor in Southeast European countries [21]. studied the determinants of Turkey's OFDI by using the ARDL Bound Test.…”

Section: Literature Reviewmentioning

confidence: 99%