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The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

5th Istanbul Conference of Economics and Finance -- JUN 06-07, 2016 -- Yildiz Tech Univ, Istanbul, TURKEY Bildirici, Melike/0000-0003-3925-844X WOS: 000386630100032 The study has two aims. The first is to investigate the interrelations of haemophilia, consanguineous marriages and their impacts on economic development. The second aim of the paper is to augment the panel regression techniques by incorporating them with Multi-Layer Perceptron neural networks models and Support Vector Machine methods. The extension is proposed to overcome the commonly criticized aspect of panel regressions, the inability to obtain homogeneity in panels. The study utilizes a panel data set that consists of 46 countries covering the 1980-2009 period and models are evaluated in terms of their ability to model the interrelations between the variables analysed. According to the empirical results, the proposed Panel Neural Network Multi-Layer Perceptron and Panel Support Vector Machine models provide success with this purpose. The empirical findings suggest that haemophilia and consanguineous marriages have significant effects on economic development. (C) 2016 The Authors. Published by Elsevier B.V. Istanbul Econ & Finance Assoc, Isik Univ, Galatasaray Univ

The study analyses the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorporating with neural network architectures. Proposed models differ in terms of both the dynamics of the conditional volatility process and the forecasting capabilities compared to a family of GARCH models. Gray (1996) RS-GARCH model allows regime dependent heteroscedasticity structure following the markov switching methodology of Hamilton (1989). The MS-GARCH-NN model family differ in the sense that, they allow regime switching between GARCH-NN processes. Single regime GARCH-NN models are developed by Donaldson and Kamstra (1996) and further extended by Bildirici and Ersin (2009). Further, the proposed models incorporate a variety of neural network architectures. MS-GARCH-MLP and MS-GARCH-Hybrid-MLP models by Bildirici and Ersin(2014) are augmented with fractional integration (FI) and asymmetric power GARCH variants. And they developed models are MS-FIGARCH-Hybrid-MLP, MS-APGARCH-Hybrid-MLP and MS-FIAPGARCH-Hybrid-MLP models. In this paper, these models were used to test volatility of gold return. Tests are evaluated with MAE, MSE and RMSE criteria and equal forecast accuracy is tested with modified Diebold-Mariano tests. An empirical application is provided for forecasting daily returns in gold market. The results suggest that the proposed approach performs well in modeling and forecasting volatility in daily returns of international gold market.

This paper investigates the causality analysis among biomass energy consumption, oil prices and economic growth in Austria, Canada, Germany, Great Britain, Finland, France, Italy, Mexico, Portugal and the U.S. by using the autoregressive distributed lag bounds testing (ARDL) method, Granger causality and Toda and Yamamato non-causality test. The dataset covers the 1970-2013 period. Although many papers have explained the relationship between oil prices and economic growth since 1970, papers have not focused the relationship among biomass energy consumption, oil prices and economic growth. This paper focused the relationship because it was accepted the biomass energy is affected by economic growth and the oil price. For Austria, Germany, Finland and Portugal, the Granger causality test determined the evidence that the conservation hypothesis is supported. In state of U.S., the feedback hypothesis highlights the interdependent relationship between biomass energy consumption and economic growth. Tado Yamamoto test determined, for Austria, Germany, Finland and Portugal, the conservation hypothesis is supported. In state of U.S., the feedback hypothesis highlights the interdependent relationship between biomass energy consumption and economic growth.

The globalization process has accelerated the liberalization of foreign trade and capital movements. This acceleration is caused in widening and intensifying relations between foreign direct investment and foreign trade. This paper examines the foreign direct investments’ contribution to the foreign trade. The empirical study is based on time series analysis for Turkey and used monthly data over the period 1992-2017. Econometric techniques for time series are applied to test unit roots, Johansen cointegration test, ARDL bound model and Granger causality test. The test results indicate that there is a correlative relation between foreign trade and foreign direct investment. As a result foreign trade affects foreign direct investments.

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The study aims to evaluate economic growth process and the problems faced by the selected transition economies in Eurasia during the transition period and especially after their independence. The countries analyzed are Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The economic growth performances are evaluated with selected institutional and structural factors; namely, with political stability, democratization and corruption with panel regression analyses. Due to the availability of the data and to obtain a balanced panel of the analyzed countries, the sample is restricted to the 1987-2011 period. The empirical findings suggest that the role of democratization has been significant on growth for the period of transition and after the independence. Unexpectedly, the findings point at positive impacts of corruption on economic growth which suggest that high levels of corruption have significant effects on economic growth due to the enlargement of the informal economy and which translates itself on GDP growth rates. Empirical results also suggest that the success or failure in economic development in this regard is related to, in addition to measures taken in terms of economic reforms, the cooperation of the societies with these reforms, the level political stability, corruption, social-democratization of communist parties and failure or success in claiming the conducted reforms by the transition economies.

The study aims to evaluate the economic growth process and the macroeconomic factors, namely, the inflation rates, the value added in the production taken as a proxy of productivity and openness to trade for the selected Eurasian transition economies. The paper focuses on the transition period and the economic performance achieved following the independence of the analyzed countries. By using a sample that consists of Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, the relationship between economic growth with inflation, trade openness and value added obtained by the national industries are evaluated within a panel setting. The dataset is investigated with traditional and structural break unit root tests followed by panel regression analyses. Considering the findings in the literature which suggest either statistically insignificant or having positive or negative effects of inflation on growth depending on the countries analyzed, the empirical findings of the paper are in favor of negative effects of inflation on growth: though the size of the effect of inflation rates on growth in rather small, negative effect of inflation cannot be rejected. Further, the positive effects of value added in the production which is taken as a proxy to productivity shows significant positive impacts on growth. Similarly, openness to international trade is shown to have positive impacts for the transition countries analyzed. The results are in favor of the findings in literature suggesting that “it is not the inflation rates, rather than the variation in inflation” which could limit economic growth. The findings for openness and value added suggest policies to enhance productivity and international trade to accelerate the economic growth in the transition economies.

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO

The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to achieve improvement in forecasting capabilities. Gray (1996) RS-GARCH model allows within regime heteroskedasticity with markov switching of Hamilton (1989). Firstly, models are extended to fractional integration and asymmetric power GARCH and MS-ARMA-FIGARCH, MS-ARMA-APGARCH, MS-ARMA-FIAPGARCH models are evaluated and discussed. Secondly, in addition to regime swiching type nonlinearity, neural networks based augmentated models are evaluated and discussed. In this respect, MS-ARMA-GARCH models are augmented with Multi Layer Perceptron (MLP), Radial Basis Function (RBF), Elman type Recurrent NN (Elman RNN), Time lag (delay) Recurrent NN (RNN) and Hybrid MLP models. Therefore, the second model group includes MS-ARMA-GARCH-MLP, MS-ARMA-GARCH-RBF, MS-ARMA-GARCH-ElmanRNN, MS-ARMA-GARCH-RNN and MS-ARMA-GARCH-HybridMLP. Thirdly, fractional integrated versions are augmented with neural networks and denoted as MS-ARMA-FIGARCH-MLP, MS-ARMA-FIGARCH-RBF, MS-ARMA-FIGARCH-ElmanRNN, MS-ARMA-FIGARCH-RNN and MS-ARMA-FIGARCH-HybridMLP. Fourth, by allowing asymmetric power transformations, models are further extended to GARCH models. These models are MS-ARMA-APGARCH-RBF, MS-ARMA-APGARCH-ElmanRNN, MS-ARMA-APGARCH-RNN and MS-ARMA-FIGARCH-HybridMLP. Therefore, a total of four group of GARCH models are proposed and evaluated in the study. In the empirical application section, daily stock returns in ISE100 Istanbul Stock Index are modeled and forecasted. Forecast success is evaluated with MAE, MSE and RMSE criteria. Equal forecast accuracy is tested with modified Diebold-Mariano tests. Accordingly, fractional integration and asymmetric power transformation perform better in modeling the daily returns in IMKB100 stock index compared to the simple GARCH and RS-GARCH model of Gray. Hybrid MLP and time lag recurrent architectures (MS-ARMA-FIAPGARCH-HybridMLP and MS-ARMA-FIAPGARCH-RNN) provided the best forecast and modeling performance. In conclusion, the newly proposed GARCH models have significant forecasting improvement and therefore are promising in various economic applications.

The study focuses on a new class of nonlinear volatility models based on neural networks and STAR type nonlinearity. Accordingly, LSTAR–LST–GARCH family and LSTAR–LST–GARCH–NN family of models will be evaluated to analyze petrol prices with economic applications. The nonlinear behavior and leptokurtic distribution are discussed in many studies. The study aims proposing augmentation of linear GARCH, fractionally integrated FI–GARCH and Asymmetric Power APGARCH models with LSTAR type nonlinearity modeling. Further, the proposed models will be augmented with neural networks to benefit from well known learning and forecasting capabilities. The multilayer perceptron (MLP) neural network model and LSTAR model have significant similarities in terms of their architecture. The proposed LSTAR–LST–GARCH family and ANN augmented LSTAR–LST–GARCH–MLP models are evaluated for modeling petrol prices. Empirical findings of the study are: (1) Fractionally integrated and asymmetric power improvements among the GARCH family models provide better forecasting capability for petrol prices; better captured long memory and high volatility characteristics of petrol prices. (2). LSTAR–LST–GARCH model family results in even better gains in out-of-sample forecasting. (3) Donaldson and Kamstra (1997) based MLP–GARCH family provided similar results with the LSTAR–LST–GARCH family models. One exception is for MLP–FIGARCH and MLP–FIAPGARCH models; FI and AP augmented models proposed in this study. (4) Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are best captured with the LSTAR–LST–GARCH–MLP model family. Forecasting capabilities of neural network techniques are promising. Among the evaluated models, the LSTAR–LST–APGARCH–MLP model provided the best performance overall. With a political perspective, in addition to the highly volatile structure, the long memory characteristics of petrol prices requires that the economic policy interventions should be kept at the modest levels to avoid persistent impacts of shocks.

The importance of technology and research and development (R&D) on economic development through international trade has been discussed in many studies. However, the empirical studies focusing on the role of high technology exports has been limited. The study aims at filling this gap by evaluating the relationship between high technology exports and GDP per capita levels with structural unit root tests and cointegration methodologies for Turkey and South Korea for the 1989-2014 period. The following hypothesis is evaluated: by increasing high technology manufactured goods’ exports, countries could increase their GDP per capita which also requires increased R&D that translates itself as high technology manufactured exports. The empirical methodology is as follows: both GDP per capita and high-tech exports variables are tested with traditional ADF, PP unit root and KPSS stationarity tests. The series are further evaluated with Zivot-Andrews single break and Lee-Strazicich two break unit root tests. The structural break tests are necessary; it is well-known that structural breaks lead to biased results in the traditional unit root and additionally in the cointegration tests. Lastly, both variables are tested for cointegration with Engle-Granger and Johansen tests by incorporating the break dates as exogenous dummy variables. The estimated models are further checked for parameter instability with CUSUM type tests. The results obtained for Turkey and South Korea are slightly different: i. both variables are cointegrated for both countries; ii. For South Korea, the positive impact of high-tech exports on GDP cannot be rejected in the long and short run; ii. This conclusion cannot be obtained for Turkey, iii. the parameter estimates for Turkey hint a limited positive effect of high tech exports in the short-run only. The results suggest that, in the future, Turkey should increase the investments in human capital and R&D directed to high tech exports to which could accelerate the economic growth.

Bildirici, Melike/0000-0003-3925-844X; Ersin, Ozgur/0000-0002-9177-2780 WOS: 000287419900168 Consanguineous marriages and their effects on human beings in light of biological effects of genetic sicknesses are discussed in many studies. Among many, the likelihood of sicknesses such as phenylketonuria, thalassemia, Landsteiner-Fanconi-Anderson's syndrome, hemophilia and many neuro system anomalies increase drastically in countries with consanguineous marriage practices resulting in increasing economic costs. In the study, we aimed to analyze the effects of consanguineous marriage and its effect on economic growth and development. We also analyzed infant mortality in these countries in light of consanguineous marriages and economic development. In the study, Panel Cointegration specifications are integrated into Neural Network models known with their strong generalization properties. The study focuses the econometric analyses, where the Panel Cointegration Neural Network Model is investigated and compared to the Panel Cointegration Model. According to MSE, MAE and RMSE error criteria and Die-bold Mariano tests of equal forecast accuracy, the results suggest strong advantages of Panel Cointegration MLP models compared to Panel Cointegration models used in regression analysis. (C) 2010 Elsevier Ltd. All rights reserved.

In the study, we discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987-22.02.2008 period in Istanbul Stock Exchange. We proposed ANN-APGARCH model to increase the forecasting performance of APGARCH model. The ANN-extended versions of the obtained GARCH models improved forecast results. It is noteworthy that daily returns in the ISE show strong volatility clustering, asymmetry and nonlinearity characteristics.

Bu calismada, uluslararasi iktisat teorilerinde onem tasiyan Satinalma Gucu Paritesi’nin (SGP) Turkiye’de Subat 2001 sonrasi dalgali kur doneminde gecerliliginin test edilmesi amaclanmistir. Bu amac dogrultusunda, SGP’nin ana akim yaklasim olan dogrusal birim kok testlerinin temel teskil ettigi yaklasimdan farkli olarak dogrusal olmayan ekonometrik yontemler bakimindan incelenmesi hedeflenmistir. $/TL reel doviz kuru serisi dogrusal ADF ve PP testleri cercevesinde duragan bulunmadigindan SGP’nin saglanmadigi sonucuna varilmaktadir. Dogrusal olmayan KSS birim kok testi serinin dogrusal olmayan birim kok icerdigine isaret etmektedir. Reel doviz kuru serisinde dogrusallik Hansen, Tsay, Mcleod-Li ve Luukkonnen ve ark. testleri cercevesinde reddedildiginden, SGP’nin incelenmesinde dogrusal olmayan Obsfeld ve Taylor (1997) Band-TAR, Taylor, Peel ve Sarno (2001) ESTAR ve bu modellere ek olarak Luukkonnen, Saikkonnen ve Terasvirta (1988) LM tipi STAR model secim testleri ile secilen LSTAR2 reel doviz kuru modeli tahmin edilmistir. Dogrusal ve dogrusal olmayan reel doviz kuru modelleri MSE, MAE ve RMSE hata kriterleri ve Diebold-Mariano testleri ile karsilastirilmistir. Ampirik bulgular kapsaminda, reel doviz kurunun modellenmesinde dogrusal modellerden dogrusal olmayan Band-TAR, ESTAR ve LSTAR2 modellerine hareket edildiginde tahmin basarisinin arttigi gorulmustur. Turkiye’de Subat 2001 Krizi sonrasindaki dalgali kur rejimi doneminde orta rejimde SGP saglanamazken, ust ve alt reel doviz kuru esikleri asildiginda olusan dis rejimlerde SGP’nin saglandigi sonucuna varilmaktadir.

The study focuses on analyzing an economy that applies inflation targeting rule where the policy interest rate is determined actively by the Taylor rule. However, following the interest rate rule, the policy maker involuntarily becomes the affirmant of inflation. As a result, the apriori target and posteriori result contrast in terms of price stability. Therefore, the achievement of price stability is involuntarily negatively affected. In an economy that applies inflation targeting policy where interest rates are determined with a Taylor type rule, the establishment of price stability could be negatively affected, the financial stability might be harmed following the determination of policy interest rates. Hence, the Central Bank might fall into a position where it should assent inflation. By moving from the Turkish example, the main problem in the policies of Central Bank of Turkey is the difference between the borrowing and lending rates and its possible inflationary effects.

The English version of this paper can be found at: http://ssrn.com/abstract=2227747Calisma, temel GARCH modelinin Destek Vektor Makinesi ve Yapay Sinir Aglari ile iyilestirilmis modellerin incelenerek GARCH modelinin tahmin performansinin iyilestirilmesi bakimindan basarisinin test edilmesini amaclamaktadir. YSA yapisinin temel alindigi NN-GARCH modelinde Donaldson ve Kamstra (1997) NN-GARCH mimarisinin temel alindigi ve farkli ogrenme algoritmalari ile modellendigi Bildirici ve Ersin (2009) temel alinirken, SVR-GARCH modelinin olusturulmasinda Ou ve Wang (2010) ve Perez-Cruz (2003) calismalarindan hareket edilmistir. Modeller, tahmin basarisi bakimindan degerlendirilmistir. Bu kapsamda, IMKB-100 endeksinde gunluk getiriler modellenmesinde temel alinan GARCH, SVR-GARCH ve MLP-GARCH modelleri, orneklem ici ve orneklem disi tahmin basarisi acisindan degerlendirilmistir. Ampirik bulgular cercevesinde, seride one cikan leptokurtik dagilima ek olarak asimetri ve dogrusal olmama karakteristiklerinin SVR-GARCH ve MLP-GARCH modelleri ile modellenebilecegi; farkli hata kriterleri ve esit tahmin tutarliligi testleri kapsaminda temel GARCH modellerinden basarili bulundugu; orneklem disi tahmin basarisi bakimindan ise, Diebold Mariano testlerinden hareketle SVR-GARCH ve MLP-GARCH modelleri icin esit tahmin tutarliliginin 8 farkli gelecek tahmininden 6'si icin reddedilememekte iken; 2'si icin ise SVR-GARCH modelinin daha basarili bulundugu sonuclarina varilmaktadir. The study aims to augment commonly applied volatility models with support vector machines and neural networks. The proposed modeling strategy benefits from neural network based GARCH models of Donaldson and Kamstra (1997) and SVR-GARCH models discussed by Ou and Wang (2010) and Perez-Cruz (2003). Models are evaluated for in-sample and out-of-sample forecasting of daily returns in Istanbul ISE100 stock index. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more effectively with SVR-GARCH and MLP-GARCH models compared to the basic GARCH models. In terms of out-of-sample forecasting capabilities, Diebold-Mariano tests show that equal forecast accuracy could not be rejected for most of the horizons for the MLP-GARCH and SVR-GARCH models. On the other hand, SVR-GARCH is found to possess better forecast accuracy in 2 out of 8 forecast competitions.

The Turkish version of this paper can be found at: http://ssrn.com/abstract=2222071 The study aims to investigate linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models and their nonlinear counterparts based on Support Vector Regression (SVR) and Neural Network (NN) models. GARCH family models are extended to NN-GARCH architecture of Donaldson and Kamstra (1997) to various NN-GARCH family models (Bildirici and Ersin, 2009) such as NN-APGARCH model. The study aims to introduce a class of extended NN-GARCH and SVR-GARCH family of models with nonlinear augmentations in modeling both the conditional mean and variance. The SVR-GARCH, SVR-APGARCH and SVR-FIAPGARCH and their Multi-Layer Perceptron architecture based counterparts, MLP-GARCH, MLP-APGARCH and MLP-FIAPGARCH are evaluated. An application to daily returns in Istanbul ISE100 stock index is provided. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently with the models possessing neural network architectures.

Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of econometric models that deal with these concepts by benefiting from both LSTAR type and ANN based nonlinearity. With this purpose, the study proposed several LSTAR-GARCH-NN family models. It is noted that the multilayer perceptron (MLP) neural network and LSTAR models have significant architectural similarities. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power APGARCH models are augmented with a family of Neural Network models. The study has following contributions: i. STAR-GARCH and LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR-ST-APGARCH, STAR-ST-FIAPGARCH models are developed and evaluated. ii. By extending these models with neural networks, LSTAR-LST-GARCH-MLP family models are developed and investigated. These models benefit from LSTAR type nonlinearity and NN based nonlinear NN-GARCH models to capture time varying volatility and nonlinearity in petrol prices. ANN augmented versions of LSTAR-LST-GARCH models are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical findings are collected as follows. i. To model petrol prices, fractionally integrated and asymmetric power versions provided improvements among the GARCH family models in terms of forecasting. ii. LSTAR-LST-GARCH model family is promising and show significant gains in out-of-sample forecasting. iii. MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models, except for the MLP-FIGARCH and MLP-FIAPGARCH models. iv. Volatility clustering, asymmetry and nonlinearity characteristics of petrol prices are captured most efficiently with the LSTAR-LST-GARCH-MLP models benefiting from forecasting capabilities of neural network techniques, whereas, among the newly developed models, LSTAR-LST-APGARCH-MLP model provided the best performance overall.

The study analyzes the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorparating with neural network architectures with different dynamics and forecasting capabilities both in addition to the family of GARCH models. In addition to the Gray (1996) RS-GARCH model which allows for within regime heteroskedasticity with markov switching of Hamilton (1989), the models analyzed in the study allow regime switching modeled with GARCH-NN specifications developed by Donaldson and Kamstra (1996) and further investigated by Bildirici and Ersin (2009). In addition to regime swiching type nonlinearity, proposed models incorporate different neural network architectures based on Multi Layer Perceptron (MLP), and Hybrid MLP models. Obtained models are MS-GARCH-MLP and MS-GARCH-Hybrid MLP. Above mentioned models are further extended to account for fractional integration (FI) in GARCH specification to obtain MS-FIGARCH-MLP and MS-FIGARCH-Hybrid MLP. By allowing asymmetric power transformation as modeled in APGARCH model, models are augmented to obtain MS-APGARCH-RBF and MS-FIGARCH-Hybrid MLP. Models are evaluated with MAE, MSE and RMSE criteria and equal forecast accuracy is tested with modified Diebold-Mariano tests. Among the models analyzed, though models which allow fractional integration and asymmetric power transformation perform better in modeling the daily returns in IMKB100 stock index, hybrid MLP and time lag recurrent architectures such as MS-FIAPGARCH-HybridMLP provide significant forecast and modeling performance. Overall, results suggest models with markov switching and neural network methodologies in modeling volatility in forecasting future returns in an emerging market stock index.

This paper aims to investigate major determinants of interprovincial migration in Turkey until 2010. In recent decades the magnitude of migration in absolute terms has increased considerably: During 1975-1980, 3.6 million people migrated, which constitutes 9.4% of the total population. These numbers have increased to 6.7 million people and 11.2% in the 1995-2000 period. The rate of increase is especially tremendous for the 1985-1990 period with 41%. Over the years the composition of migration has also changed: In the past rural-to-urban migration was predominant; however, today there is remarkable amount of urban-to-urban migration. During 1975-1980, 66% of the total migrants were towards urban centers, which increased to 75% during 1995-2000. On the other hand, the percentage of total migrants towards the village centers declined from 34 to 25 in the respective periods. From 2008 on, the Turkish Statistical Institute (TUIK) started publishing yearly unemployment statistics at provincial level, which permits an analysis of Turkey’s migration patterns within the Harris-Todaro framework. Moreover since 2007, TUIK started implementing Address Based Population Registration System, which enables tracking migration moves continuously, rather than by intermittent five to ten year periods. However, data was not adequate to test Turkey’s migration within the Harris-Todaro framework, especially due to lack of average wages at the provincial level. Therefore, utilizing the 2010 provincial level data, we tried to explain Turkey’s internal migration based on variables such as population born outside of their current province, number of university students, and a proxy variable we developed for average wages.

In Economic theory, the impact of government debt through wealth effects that enhance the likelihood of real effects in an economy, in which the Ricardian Equivalence fail to hold, is investigated in many studies.Under active fiscal policies as puth foth by Leeper (1991), to equate the intertemporal budget constraint, the increases in price level leads to inflationary periods (Sims, 1991, 1993; Leeper, 1991; Sargent ve Wallace, 1981). As shown by Woodford (1994), in economies, the Quantity Theory of Money, therefore a Money demand function in addition to intertemporal budget constraint equation holds simultaneously, which leads to doubts regarding tests focusing to differentiate Ricardian and Non-Ricardian regimes. In the study, the evaluation methods based on econometric theory and literature are investigated to provide an overlook that follows a path from linear to nonlinear econometric tests. A model will be discussed in accordance with the FTPL theory. Further, the likelihood of increasing failure to evaluate the FTPL theory based on rational expectations with Markov regime switching models will be discussed. STAR type models and their applications to FTPL theory will be discussed.

Bildirici, Melike/0000-0003-3925-844X; Ersin, Ozgur/0000-0002-9177-2780 WOS: 000281680900004 PubMed: 21105495 In this study, development experiences toward economic development are investigated to provide an alternative analysis of economic development, human capital, and genetic inheritance in the light of consanguineous marriages. The countries analyzed in the study are discussed in accordance with consanguineous marriage practices and classified by their per capita gross domestic product (GDP) growth. A broad range of countries are analyzed in the study. Arab countries that experienced high rates of growth in their gross national income during the twentieth century but failed to fulfill adequate development measures as reflected in the growth in national income, countries undergoing transition from tight government regulation to free market democracy, and African nations that have experienced complications in the process of development show important differences in the process of economic development. It is shown that the countries that have reached high average development within the context of per capita GDP have overcome problems integral to consanguineous marriage.

In our study, development experiences towards economic development are investigated to provide an alternative analysis of economic development, human capital and genetic inheritance in light of consanguineous marriages. The countries analyzed in the study are discussed in accordance with consanguineous marriages. Neural network and panel data analysis are used for analysis of the relationship among consanguineous marriage, economic development, human capital and infant mortality. The results provide empirical findings suggesting that the negative effects of consanguineous marriages and infant mortality on economic development cannot be rejected for the analyzed countries, whereas the negative impact significantly increases for the analyzed African countries.

The effects of consanguineous marriages through genes have strong impacts not only on human capital, but also on political stability through the process that consanguineous marriages alter the sociological structure. Nevertheless, these types of marriages were abandoned by the developed nations at the beginning of the 19th century, although these types of marriages are prefered today in many Asian and African countries. In these countries, the traditional structure could not be broken; a twosome structure is created within the country, within cities, within different regions as well as between regions and between countries. Political stability fails to be achieved in these countries. The twosome structure promotes political instability and bad economic peformances. It is observed that, the countries with high consanguineous marriages experience different sociological and economic factors so that the motives towards individualism considered as an important source of industrial development fail to flourish. An important result of the twosome structure is on human capital investments and on economic development. We followed a different approach in analyzing economic development in accordance with consanguineous marriage and political instability in accordance with human capital investments and economic development. In the study, we analyzed 53 countries; we collected and compiled most recent data of consanguineous marriages, literacy rates and political risk as an indicator of political instability. We calculated GDP per capita growth rates between 1900 and 2003. We grouped countries according to their consanguineous marriage rates. Empirical results show that, the group of developed economies overcome consanguineous marriages before 1900 and achieved political stability, high human capital investments and steady growth rates. On the other hand, the countries practicing high consanguineous marriage rates are less immune to political instability and experience negative growth rates and very low human capital investments. The group of countries that lowered their consanguineous marriage rates below 10 % just before 1980, achieved very rapid economic growth afterwards. Empirical analysis based on factor analysis, correlation and covariance matrices suggest that countries with high consanguineous marriages experience high political instability and fall behind in the process of economic development.

In our study, development experiences towards economic development are investigated to provide an alternative analysis of economic development, human capital and genetic inheritance in light of consanguineous marriages. The countries analyzed in the study are discussed in accordance with consanguineous marriages and classified by their GDP per capita increase. Countries analyzed in the study represent a wide range of countries. Among these countries, Arab countries that experienced high rates of growth in their gross national income during the 20th century but failed to fulfill adequate development measures followed by the growth in national income; part of the transition countries from socialism to democracy; African countries that experience important complications in the process of development; have important differences in the process of economic development. It is shown that the countries that reached high average development within the context of GNP growth rates, had overcome consanguineous marriages and taken the steps accordingly. The countries that reached the highest development levels banned consanguineous marriages in 19th Century. The prohibition of consanguineous marriages supported the foundation of the labor class during the transition to capitalism and industrialization. According to our opinion, consanguineous marriage is a significant impediment to economic development.

The consanguine marriages and/or causine marriages are generally accepted and evaluated in light of the genetic problems. However, in the study, we aim to explain the effects on economic development and genetics problem of consanguine marriages and to investigate the relationship between economic development, consanguine marriages and genetics. The first part of the study focuses on economic development, consanguine marriages, genetic structure and human capital relations in theoretical framework. The second part contains the econometric analyses, Panel Neural Network investigated.

The study aims at analyzing the nonlinear effects of GDP on CO2 emissions for a long span of data, covering the period of 1870-2011 for a panel of 13 developed countries with nonlinear heterogeneous dynamic Panel-STAR framework. The group analyzed consists of Australia, Belgium, Canada, France, Germany, Sweden, Switzerland, UK, USA, Italy, Netherlands, Norway and Spain. The Panel-STAR model evaluated in the study allows modeling and testing heterogeneity and nonlinearity simultaneously within the nonlinear STAR methodology. The difference of the Panel-STAR models analyzed in the study from the Panel-STR models is, they allow dynamic effects. The motive behind proposing dynamic modelling of heterogeneous Panel-STAR models is related to the fact that CO2 emissions are an accumulated process which possess path-dependence to the history of the emissions and economic growth. Both variables are integrated of order 1 due to serial panel unit root tests. For the data, Panel-STAR type nonlinearity cannot be rejected. The empirical findings suggest that various thresholds could be obtained and the rejection of linearity is strongest for the Panel-STAR model with the transition variable taken as the emissions growth rate. A second-best model is also estimated by taking the transition variable as the rate of economic growth. The empirical findings suggest that i. the effects of economic growth on emissions growth cannot be rejected in both regimes, ii. in both regimes, the effect is positive in contrast to EKC type theoretical expectations, iii. the positive effects of economic growth are lower in the second regime suggesting asymmetry, iv. after achieving a threshold of emissions’ growth, though the political commitment against environmental degradation exists, the growth of CO2 emissions is partially slowed and the relation is not reversed.

In this study, the forecasting capabilities of a new class of nonlinear econometric models, namely, the LSTAR-LST-GARCH-RBF and MLP models are evaluated. The models are utilized to model and to forecast the daily returns of crude oil prices. Many financial time series are subjected to leptokurtic distribution, heavy tails, and nonlinear conditional volatility. This characteristic feature leads to deterioration in the forecast capabilities of traditional models such as the ARCH and GARCH models. According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes. Several model groups are evaluated: (i) the models proposed in the first group are the LSTAR-LST-GARCH models that are augmented with fractional integration and asymmetric power terms (FIGARCH, APGARCH, and FIAPGARCH); (ii) the models proposed in the second group are the LSTAR-LST-GARCH models further augmented with MLP and RBF type neural networks. The models are compared in terms of MSE, RMSE, and MAE criteria for in-sample and out-of-sample forecast capabilities. The results show that the LSTAR based and neural network augmented models provide important gains over the single-regime baseline GARCH models, followed by the LSTAR-LST-GARCH type models in terms of modeling and forecasting volatility in crude oil prices.

WOS: 000368562900011 The study focuses on analyzing an economy that applies an inflation-targeting rule in which the policy interest rate is determined actively by the Taylor rule, and the policy maker involuntarily becomes the affirmant of inflation. In an economy that applies inflation-targeting policy where interest rates are determined in light of the Taylor rule, as the Central Bank tries to establish price stability and financial stability by determining policy interest rates, the Central Bank might fall into a position to do nothing but to assent inflation. In the empirical section, two new indices, the psychological dominance (pdi) and market dominance indices (mdi) are developed based on the difference between the policy rates. The band within which the indices follow random walk processes are determined with Band-TAR models. The CB policy is additionally modeled with a nonlinear Taylor rule with TVEC models. The most significant point of the process is its inflation-creating effect. By moving from the Turkey example, the main problem in the policies of Central Bank of Turkey is the difference between the borrowing and lending rates and its inflationary effect.