GMM Analysis

BackgroundStudies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures.ResultsOur results reveal no evidence for statistically significant sexual size dimorphism, bu...

Global value chain (GVC) is defined as a production process involving value-added from at least two countries with increasing global integration. This study aims to identify the industries triggered by the exports/imports of intermediate inputs in the rest of the world / Türkiye for the industries that Türkiye has identified as targets in the 2023 Industry and Technology Strategy Document. In this way, it is determined in which industries Türkiye is articulated to GVCs through its target industries. The analysis is carried out by calculating the forward and backward linkage coefficients between countries in the multi-country input-output matrix. As an analysis tool, the input-output matrix with 45 industries and 64 countries produced by the OECD for 2018 is used. In the analysis, the matrix is used by aggregating it as two countries in the form of Türkiye and the rest of the world. Analysis indicates that inter-country calculated forward and backward linkage coefficients for both intermediate input exports and imports of the target industries "electrical equipment", "chemicals", "machinery" and "motor vehicles" are greater than 1. This finding can be interpreted as a mutual interdependence between the production of the world and Türkiye through these industries. Additionally, it can be said that Türkiye is unidirectionally dependent on imports from abroad in the "computers and electronic and optical products" industry. Another finding is that there is a relative compatibility between the industries that Türkiye has been exporting the most for many years and the industries with high value-added and high technology content that are targeted to be exported in the recent period.

This paper investigates the impact of Environmental, Social, and Governance (ESG) factors on asset pricing using a Generalized Method of Moments (GMM) estimation framework. The study aims to determine whether ESG scores are priced in financial markets, influencing risk-adjusted returns of assets. This study analyzes a panel of 100 publicly listed firms of China over a five-year period from 2019 to 2023. The firms are selected based on their availability of ESG scores from leading data providers (MSCI, Bloomberg) and financial data from widely used sources such as Compustat and CRSP. We find a significant negative relationship between ESG scores and excess returns. Specifically, firms with higher ESG ratings tend to exhibit lower excess returns, indicating that investors may accept lower returns in exchange for sustainability and reduced risk exposure. While traditional factors such as the market, size (SMB), and value (HML) continue to play significant roles in asset pricing, ESG factors are shown to influence returns in a manner consistent with risk-reduction theories. This study contributes to the growing literature on ESG investing, providing empirical evidence of ESG's role in asset pricing. The findings suggest that while ESG investing does not necessarily generate superior returns, it offers a riskreducing strategy that aligns with investor preferences for ethical and sustainable business practices. The paper concludes with implications for investors, policymakers, and future research on the integration of ESG in financial markets.

Peran Anggaran Kesehatan Pemerintah dalam Mentransformasikan Pertumbuhan Ekonomi untuk Indeks Pembangunan Manusia (IPM): Analisis Panel Dinamis
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Studi ini membahas kesenjangan empiris dalam memahami dampak anggaran kesehatan Pemerintah terhadap Indeks Pembangunan Manusia (IPM), khususnya di berbagai negara dengan tingkat pendapatan yang berbeda. Meskipun terdapat asumsi umum bahwa pertumbuhan ekonomi berkontribusi terhadap pembangunan manusia, bukti mengenai peran anggaran kesehatan Pemerintah dalam meningkatkan IPM masih belum meyakinkan. Untuk menyelidiki hal ini, studi ini menggunakan analisis panel dinamis dengan metode System Generalized Method of Moments (System GMM) menggunakan data dari 151 negara selama periode 2005–2019, dengan mengontrol variabel pertumbuhan ekonomi, angka kematian bayi, efektivitas pemerintah, tingkat pendapatan, dan PDB per kapita. Metodologi ini dipilih karena ketangguhannya dalam mengatasi endogenitas dan menangkap hubungan dinamis dalam data panel. Hasil penelitian menunjukkan bahwa peningkatan satu persen dalam pengeluaran pemerintah untuk kesehatan sebagai persentase dari PDB menghasilkan peningkatan rata-rata sebesar 1,07 poin dalam IPM, dengan dampak yang secara signifikan lebih kuat di negara-negara berpendapatan menengah dibandingkan dengan negara-negara berpendapatan tinggi. Temuan ini menyoroti adanya penurunan hasil marjinal dari anggaran kesehatan Pemerintah seiring meningkatnya tingkat pendapatan suatu negara, serta menekankan pentingnya alokasi anggaran pemerintah yang memadai untuk kesehatan di negara-negara berpendapatan menengah.

We estimate empirically the effect of immigration on house prices and residential construction activity in Spain over the period 1998-2008. This decade is characterized by both a spectacular housing market boom and a stunning immigration wave. We exploit the variation in immigration across Spanish provinces and construct an instrument based on the historical location patterns of immigrants by country of origin. The evidence points to a sizeable causal effect of immigration on both prices and quantities in the housing market. Between 1998 and 2008, the average Spanish province received an immigrant inflow equal to 17% of the initial working-age population. We estimate that this inflow increased house prices by about 52% and is responsible for 37% of the total construction of new housing units during the period. These figures imply that immigration can account for roughly one third of the housing boom, both in terms of prices and new construction.

Background information: Analyzing financial time series is essential for forecasting trends, detecting anomalies, and facilitating informed choices in fluctuating markets. This document presents a secure cloud-based framework that integrates DeepAR for time series forecasting, Neural Turing Machines (NTMs) for memory-based relationships, and Quadratic Discriminant Analysis (QDA) for strong classification. Utilizing cloud infrastructure, the system tackles issues like high dimensionality, noise, and non-linearity in financial data, delivering precise, scalable, and secure real-time analytics. Methods: The suggested system combines DeepAR for probabilistic predictions, NTMs for managing long-term dependencies, and QDA for classification tasks. These models collaborate to improve financial data analysis. Employing cloud-based architecture guarantees scalability, immediate processing, and secure management of sensitive information, addressing the drawbacks of conventional approaches in high-dimensional financial modeling. Objectives: This study seeks to create a cloud-based financial analysis platform that combines DeepAR, NTMs, and QDA to deliver precise predictions and strong classification of financial time-series information. The aims encompass handling sequential dependencies, enhancing classification of non-linear trends, ensuring scalability and security, and providing actionable insights for real-time decision-making in rapidly evolving financial markets. Results: The hybrid model reached 95% accuracy, outperforming standalone models such as DeepAR (88%) and NTMs (85%). Scalability achieved 95%, showcasing effectiveness in managing high-dimensional datasets. Recall and F1-scores of 92% and 91.5% validate the model's reliability, establishing it as a powerful resource for secure, realtime financial time-series evaluation. Conclusion: Incorporating DeepAR, NTMs, and QDA into a cloud-based platform provides a strong, scalable approach for analyzing financial time series. The hybrid approach surpasses individual models in terms of accuracy and efficiency, tackling issues such as non-linearity and high-dimensional dependencies. This robust system guarantees dependable, real-time analytics for knowledgeable decision-making in dynamic financial settings.

Background information: Monte Carlo simulations, Deep Belief Networks (DBNs), and Bulk Synchronous Parallel (BSP) processing are used in the suggested secure cloud-based financial analysis system to increase the effectiveness of risk prediction and financial modeling. The system uses cloud infrastructure for precise financial forecasts to guarantee scalability, security, and high-performance data processing. Computational time is greatly decreased by parallel processing, and data security is preserved by encryption, facilitating sound decisionmaking in intricate financial contexts. Methods: The system combines Monte Carlo simulations for forecasting risks, DBNs for identifying patterns, and BSP processing to enhance computational efficiency within a cloud setting. Data that is encrypted is processed over multiple cloud nodes, improving security and scalability. This integration enables simultaneous processing of multiple simulations, thus enhancing the speed and precision of financial analysis. Objectives: By combining Monte Carlo simulations, DBNs, and BSP processing, this work seeks to improve the efficiency of financial models and provide a safe, cloud-based financial analysis system. The solution is designed to safely manage huge datasets in a cloud environment that is scalable. The system aims to shorten calculation times by utilizing parallel processing, guaranteeing precise financial forecasts, risk assessment, and trustworthy decisionmaking. Results: The suggested system performs financial analysis jobs more accurately and efficiently. Strong security and scalability are offered by encrypted data management and parallelized simulations. Performance measures show notable gains in recall, accuracy, and precision over conventional techniques, with BSP processing improving scalability. For crucial financial decision-making, this method facilitates quick and safe data analysis. Conclusion: The suggested system performs financial analysis jobs more accurately and efficiently. Strong security and scalability are offered by encrypted data management and parallelized simulations. Performance measures show notable gains in recall, accuracy, and precision over conventional techniques, with BSP processing improving scalability. For crucial financial decision-making, this method facilitates quick and safe data analysis.

Massive datasets and dynamic fraud patterns pose serious obstacles to financial transaction fraud detection. In order to provide adaptive fraud detection, this study suggests a hybrid model that combines Test Case Reduction, Gaussian Mixture Models (GMM), and Regularized Discriminant Analysis (RDA). GMM detects anomalies by capturing the probabilistic distribution of financial behaviors, whereas RDA improves classification accuracy, particularly with unbalanced datasets. Test Case Reduction reduces superfluous data, which increases efficiency. The accuracy, precision, and scalability of the model are improved over conventional methods. It is perfect for financial institutions looking to increase security because it instantly adjusts to new fraud practices. When compared to current models, experimental results show a significant improvement in performance indicators like accuracy (98.4%) and F1-score (97.0%). Furthermore, the hybrid model's longterm efficacy is guaranteed by its capacity to continuously adjust to changing fraud tactics. It offers a scalable method appropriate for large-scale financial transactions by utilizing probabilistic modeling and classification approaches powered by machine learning. To improve fraud detection accuracy and operational efficiency even more, future developments might incorporate reinforcement learning techniques and deep learning-based improvements.

1980 sonrasında Türkiye ekonomisinde ihracata dayalı büyüme politikalarının uygulandığı dönem başlamıştır. Bu süreç içerisinde ithalat serbestleştirilmiş, ihracatın ekonomik faaliyetler içindeki önemi artmıştır. Bunun yanında 1989 yılında Türk Lirası'nın yabancı paralar karşısında tam konvertibilitesinin sağlanmasıyla birlikte hem mal hem de hizmet piyasalarının dünya ekonomileriyle entegrasyonu hız kazanmıştır. 1990 sonrası dönemde dış ticaret hareketlerinin ülke ekonomisi içindeki önemi de, bu bağlamda daha fazla önem kazanmıştır. İhracatın GSYİH içindeki payı giderek artmaya başlarken, uzun zamandır Avrupa Birliği (AB) üyeliği için müzakerelerin yapıldığı AB ülkeleri, 1996 yılında yürürlüğe giren gümrük birliği anlaşmasının da etkisiyle Türkiye'nin ticaret partnerleri arasında en büyük payı almaktadır. Ancak 2002 yılı sonrasında Türkiye ihracatının bölgelere göre dağılımına bakıldığında AB'nin payının giderek azaldığı görülmektedir. Buna karşın Yakın ve Orta Doğu, Asya ve Kuzey Afrika bölgelerinin ise ihracat yapılan ülke grupları arasında aldıkları pay dikkate değer bir şekilde artış göstermiştir. Ortaya çıkan bu durum Türkiye'nin ihracat yapısında meydana gelen bir dönüşümden dolayı mı meydana gelmiştir? Şayet böyle bir dönüşüm söz konusu ise bunun "nasıl?" ve "ne yönde?" olduğu gibi sorular da akabinde akla gelmektedir. Yaşanan bu değişim birçok çalışmada da kendine yer edinmiştir. Bu çalışmalar içerisinde, hem Türkiye içerisinde hem de yabancı akademik çalışmalar ve basın tarafından en çok dile getirilen nedenlerden biri 2002 sonrası yaşanan ekonomik ve siyasi "dönüşüm"dür. 2001 Krizi sonrası ekonomik kurumların düzenlenmesi ile nispeten daha istikrarlı bir döneme geçilmiştir. Bunun yanında 2002 seçimleri ile hükümetin başına gelen Adalet ve Kalkınma Partisi'nin izlediği dış politikanın araçları arasında ticaret önemli bir araç olarak kullanılmıştır (Öniş

In this study, the effects of energy efficiency, rebound effect, on energy production, consumption, saving, import as well as inflation and current account deficit are examined using time series analysis in the period of 1967-2015 in Turkish economy. Firstly, in order to determine the stationary level of the variables ADF and PP unit root tests are used and the results show that all of the variables considered within the model are stationary at the first difference level. Second, the finding of Johansen-Juselius cointegration analyze reveals the existence of long-run relationships among variables, and Granger causality analysis reflects at least the validity of one-way causal links among variables. The findings of the VEC analysis point out that the Jevons Paradox or rebound effect is valid in the Turkish economy, which means that energy consumption rises as energy efficiency and production increases. In addition, the findings show that depending on increasing energy consumption ene...

Although the World Bank points out these costs remain high, especially in Africa and in small nations. Globally, migrants pay an average cost of 9 per cent to send money home. 2 For example, deposits by the non-residents in India attract higher interest rates and are exempt from income tax. Similarly, Pakistan and Bangladesh give incentives to increase remittances. In 2008, India's remittance receipts were the highest at US$52 billion. Other countries with high remittances include China and Mexico.

It has been established that COVID-19 affected the world economy with nega ve impacts on the profitability and financial performance of many businesses. This study evaluated the financial performance of Nestle Plc pre-, during, and post-COVID-19 epidemic and subsequent lockdown. The study used secondary data from the company's audited annual financial reports (2017-2022). Financial data was collected and analysed using a mix of analy cal methods such as descrip ve analysis, correla on analysis, and single ANOVA in Excel 2010. The financial ra os analysed include financial performance, profitability ra os, liquidity ra os, gearing ra os, and investor ra os. The results indicated that ne le's financial performance was affected during the period of the COVID-19 lockdown between 2019-2021. However, based on the result for investor ra os (p-value = 0.708), the study states that there is not enough evidence to conclude. The study also iden fied limita ons such as only Nestle used is not representa ve of the food and beverages industry as well as the number of ra os analysed. It recommended that further studies should include more players in the industry as well as the number of ra os with more companies in the food and beverages industry in the UK and perhaps outside of the UK.

Ce papier présente en détails l’analyse multivariée des séries chronologiques avec son application sur le logiciel STATA. Il est subdivisé en plusieurs points suivants : (1) généralités sur les séries chronologiques; (2) Notions sur les données chronologiques et stationnarité; (3) causalité; (4) cointégration; (5) présentation de l’interface STATA; (6) implémentation des données au logiciel STATA; (7) rappel sur l’analyse univariée et bivariée; et (8) choix du modèle économétrique.
Le point (8) est l’essentiel de ce document. L’analyse multivariée des séries chronologiques permet d’explorer les relations entre plusieurs variables au fil du temps. Elle est essentielle pour comprendre les dynamiques économiques, financières, et sociales. La maîtrise de ces techniques dans STATA facilite l’application pratique de ces concepts théoriques.
Le document aborde à tour de rôle les modèles suivants : régression linéaire simple et multiple, modèle vectoriel autorégressif (VAR), modèle vectoriel autorégressif structurel (SVAR), modèle à correction d’erreur
(ECM), modèle vectoriel à correction d’erreur (VECM), et modèle autorégressif à retards distribués (ARDL).

The objective of this study is to examine the effect of financial leverage, size of the firm and capital intensity on the profitability of the listed firms in the food industry on Muscat Stock Exchange (MSX) in the Sultanate of Oman. The goal of this research is to try to gain some insight into the way these three variables affect the performance of these companies financially. The Financial Leverage indicates the ratio of total debt to equity, and reveals to what extent firms use debt to finance their operations. Size of the corporation, measured by total assets, refers to the level of operation and the resulting economies of scale. Capital intensity reflects the extent of investment in long-term assets, measured as the ratio of fixed assets to total assets. This research analyzes the financial statements of a sample of 11 public food industry firms listed on the MSE for a five-year span based on panel data analysis. To assess profitability, we use return on assets (ROA) as performance indicator. Financial leverage has a mixed effect on profitability, and results indicate that high leverage potentially increases financial risk and interest obligations, which reduce profitability. In contrast, more substantial enterprises generally prosper better owing to economies of scale, greater market presence, and improved resource access. The capital intensity is positively correlated with profitability, asset-heavy constitutes high levels of fixed asset investment, can enhance the production efficiency and competitive advantage. The findings provide useful guidance for food sector corporate managers and investors/analysts as to the strategic importance of financial management in achieving the optimal capital structure to maximize profit. The results can also guide policymakers to create a conducive environment for the food sector in Oman.

ABSTRACT
The relationship between the determinants of growth and development indicators is frequently examined in the literature. However, the focus has recently shifted from the impact of growth on development to the influence of development on economic growth. Accordingly, this study investigates not the contribution of economic growth to development (welfare) indicators, but rather the potential contribution of development to economic growth. This study analyzes the dynamic effects of social and demographic factors on Gross Domestic Product (GDP) for 36 selected countries during the 2013-2020 period. The variables used include the Human Development Index (HDI), life expectancy at birth (YB), urban population (KNFS), and dummy variables (DD) representing the effects of the COVID-19. The analysis conducted using the System Generalized Method of Moments (GMM) shows that the lagged value of GDP is positive and significant, indicating the continuity of economic growth. While IGE has a positive effect on Gross National Product (GNP), the negative effects of YB, KNFS and DD are noteworthy, and the negative effects of COVID-19 on macroeconomic indicators have seriously suppressed growth. The findings provide important insights for guiding social and economic policies. Policy recommendations such as increasing investments in education and healthcare, preparing crisis management plans, and strengthening urban and health infrastructure are crucial for supporting economic growth. Additionally, reducing the costs of urbanization and enhancing quality of life require increased investments in urbanization and healthcare infrastructure. These plans should include strengthening health infrastructure, improving social security systems, and implementing policies to maintain economic stability, as they play a vital role in enhancing both individual and societal welfare. All these measures will not only address current challenges but also contribute to building a more resilient and sustainable economic structure. As a result, economic development indicators also influence economic growth.
Keywords: GDP, Economic Growth, Social and Demographic Factors, Country Groups, GMM analyzis.

ÖZET
Büyümenin belirleyicilerinin, kalkınma göstergeleri ile ilişkisi literatürde sıklıkla incelenmektedir. Ancak artık büyümeden kalkınmaya değil, kalkınmanın büyüme üzerine bir etkinin varlığı da literatürde tartışılmaya başlanmıştır. Dolayısıyla bu çalışmada ekonomik büyümenin kalkınma (refah) göstergelerine değil, tam tersine kalkınmanın ekonomik büyümeye katkısının varlığı araştırılmaktadır.
Çalışmada, seçilmiş 36 ülke için 2013-2020 döneminde sosyal ve demografik faktörlerin Gayri Safi Yurt İçi Hasıla (GSYH) üzerindeki dinamik etkileri analiz edilmiştir. İnsani gelişme endeksi (IGE), doğumda yaşam beklentisi (YB), kentsel nüfus (KNFS) ve COVID-19 pandemisinin etkilerini temsil eden dummy değişkenler (DD) değişkenleri kullanılmıştır. Sistem Genelleştirilmiş Momentler (GMM) yöntemiyle gerçekleştirilen analiz sonuçlarına
göre, GSYH’nin bir dönem gecikmeli değeri pozitif ve anlamlı bulunmuş, bu durum ekonomik büyümenin sürekliliğini göstermektedir. IGE’nin, Gayri Safi Milli Hasıla (GSMH) üzerinde pozitif bir etkisi tespit edilirken; YB, KNFS ve DD ise negatif etkileri dikkat çekmekte, COVID-19’un makroekonomik göstergelere olumsuz etkileriyse büyümeyi ciddi ölçüde baskılamıştır. Elde edilen bulgular, sosyal ve ekonomik politikaların yönlendirilmesinde önemli ipuçları sunmaktadır. Eğitim ve sağlık yatırımlarının artırılması ve kriz yönetim planlarının hazırlanması gibi politika önerileri, ekonomik büyümeyi desteklemek için kritik önem taşımaktadır. Ayrıca kentsel nüfusun yarattığı maliyetlerin azaltılması ve yaşam kalitesinin artırılması için şehirleşme ve sağlık altyapısına yönelik yatırımların artırılması gerektiği vurgulanmaktadır. Bu planların sağlık altyapısının güçlendirilmesi, sosyal güvenlik sistemlerinin geliştirilmesi ve ekonomik istikrarı koruyacak politikaların hayata geçirilmesi gibi önlemleri içermesi hem bireylerin hem de toplumların refahını artırmak için hayati bir role sahiptir. Tüm bu önlemler, yalnızca mevcut sorunlara çözüm üretmekle kalmayacak, aynı zamanda daha dirençli ve sürdürülebilir bir ekonomik yapı oluşturulmasına da olanak sağlayacaktır. Sonuç olarak ekonomik kalkınma göstergeleri de ekonomik büyümeyi etkilemektedir.
Anahtar Kelimeler: GSYH, Ekonomik Büyüme, Sosyal ve Demografik Faktörler, Ülke Grupları, GMM.

The insurgence of exchange rate volatility over the years has gained the attention of not only scholars but also policy makers around the world. This paper investigates the influence of exchange rate volatility to the financial performance of agriculture firms in Malaysia. Authors use the system GMM dynamic panel techniques, wavelet coherence technique and GARCH (1, 1) for the period of 2001 and 2015. The findings show that the volatility of exchange rate of Malaysian Ringgit (RM) has a negative impact on the financial performance of agriculture firms in Malaysia. The ARME and AVA demonstrate a positive impact on the financial performance at 1% significance level for the full sample. The findings also reveal that financial performance, exchange rate, consumer price index, and interest rate comove while using the wavelet coherence.

The research utilizes a comprehensive dataset from MENA-listed companies, capturing data from 2013 to 2022 to scrutinize the influence of capital structure (CapSt) level on corporate performance across 11 distinct countries. This study analyzed 6870 firm-year observations using a quantitative research method through static and dynamic panel data analysis. The primary analysis reveals a positive correlation between the CapSt ratio and company performance using fixed effects (FE) techniques. Hence, the preliminary results were reexamined and affirmed using a two-step system generalized method of moment (GMM) estimator to address potential endogeneity concerns. This finding aligns with most studies conducted in advanced countries, indicating a positive correlation between CapSt and corporate performance. Furthermore, it is also consistent with some research conducted in less-developed markets. This research argues that, in the MENA region, the advantages of debt, such as tax saving, may outweigh the potential financial distress cost. Furthermore, it offers insights into the monitoring role of CapSt in MENA-listed companies. We strengthen our research results by employing various methodologies and using alternative measures of accounting performance and controlling size, notably panel quantile regression analysis.

Precise identification and detection of ailments in tomato plants are essential for preserving crop vitality and optimizing agricultural productivity. This promotes the use of agricultural methods that can be maintained over time and decreases financial losses caused by plant diseases. Detecting and classifying diseases in tomato plants is critical for ensuring crop health and maximizing agricultural productivity. Utilizing advanced computer vision techniques for this purpose enhances precision in monitoring plant health, ultimately leading to more efficient and targeted agricultural interventions. This research work presents a novel framework for Tomato Plant Disease Detection and Classification (TPDDC) using a fusion of swarm-based methods and deep-learning techniques. Our approach leverages K-means clustering with Grasshopper Optimization (GO) for segmenting Regions of Interest (ROI) from tomato leaf images, followed by feature extraction and optimization using Maximally Stable Extremal Regions (MSER) and GO. The optimized features are then classified using a Convolutional Neural Network (CNN). The proposed TPDDC model was evaluated using the Plant Village Dataset, encompassing ten different tomato leaf diseases. Experimental results demonstrate significant improvements in detection and classification accuracy, achieving an average accuracy of 97.6% with the GO-based approach compared to 92.7% without GO. These results underscore the effectiveness of integrating swarm-based optimization with deep learning for robust and precise disease detection in tomato plants.

Empirical estimates of the Reserve Bank of India's (RBI) intervention reaction function suggest that the central bank actively intervenes in the foreign exchange market to contain volatility but this intervention is neither continuous nor linear. It is better described by a nonlinear policy reaction function with a target range as opposed to a point target. It responds much more vigorously to appreciating or depreciating pressure outside the target range but the response is much more muted within the range. Moreover, the tolerance band is asymmetric i.e., the RBI responds much more strongly to appreciating pressure than depreciating pressure. Such a policy response in an era of continuous net capital inflows accounts for the large build-up in foreign exchange reserves witnessed in India in the recent past.

The study investigated the effect of capital structure on the performance of selected non-deposit financial institutions (also known as Development Finance Institutions, DFIs) in Nigeria. The study specifically examined the effect of Short-Term Debt to Total Assets (STDTA) on performance of selected of non-deposit financial institutions in Nigeria; and ascertained the effect of Total Debt to Total Equity (TDTE) on performance of selected non-deposit financial institutions in Nigeria. The study adopted an ex-post facto research design and data were collected from published annual financial records of the selected DFIs on both STDTA and TDTE, the independent variables; and Earnings per Share (EPS) and Return on Assets (ROA), the dependent variables, for a period of ten (10) years (2013-2022). The population of the study composed of seven (7) national DFIs: Central Bank of Nigeria (CBN), Bank of Agriculture (BOA), Bank of Industry (BOI), and Development Bank of Nigeria (DBN), Federal Mortgage Bank of Nigeria (FMBN), Nigeria-Export-Import Bank (NEXIM) and the Infrastructure Bank Plc (IBN). The study sampled four (4) national DFIs using a purposive sampling technique, including CBN, BOI, FMBN and NEXIM. Computation of the relevant ratios were done for the independent variables (STDTA and TDTE); and the dependent variables (EPS and ROA). Data analysis was done using multiple regression and the econometric technique of Dynamic Ordinary Least Squares (DOLS) with the aid of E-Views Version 13. The study found that STDTA has a positive and significant effect on EPS and ROA, indicating that a percentage change in STDTA leads to an increase in EPS and ROA by 1.165987 and 0.001258 respectively; while TDTE has a negative but significant effect on EPS by-0.595635 and a positive and significant effect on ROA, showing that a percentage change in TDTE will significantly increase ROA by 0.001696. The study concluded that the investigated DFIs have low equity capital which seems to hinder their ability to attract large loans for operations. The study recommended, amongst others that DFIs should maintain a balanced total debt to total assets (TDTE) and ensure the regular monitoring of the debt-to-asset ratio to ensure it remains within a healthy range which will help maintain a balance between leverage and risk.

Entrepreneurship education aims at developing not only knowledge and skills for entrepreneurial activity, but also the change of attitude toward entrepreneurship as a way of acting and thinking. Starting from the assumption that higher education is the basis for further professional affirmation and bearing in mind the importance of entrepreneurship for the development of the economy and society, entrepreneurial education should have a very important place in the study programs of higher education institutions. The objective of the subject research is to examine the representation of entrepreneurial education in higher education programs in Bosnia and Herzegovina, to determine the structure and content of the current curricula and to give a brief overview of the methods and techniques used in teaching. Additionally, opinions of business studies students about entrepreneurial education and entrepreneurship, in terms of interest in programs aimed at strengthening entrepreneurial competencies, were examined, as well as the evaluation of these programs by students, in terms of to which extent they assess the importance and usefulness education for entrepreneurship The existing study programs at business-oriented colleges at public universities in Bosnia and Herzegovina include numerous subjects that, in different ways, address the problems of entrepreneurship. However, education and education programs show weaknesses in terms of insufficient orientation to practice and the inaccessibility of these programs to some students.

Plant disease detection with deep learning models has shown promising results, but these models often struggle with generalizing across diverse agricultural environments due to domain shifts in imaging conditions. This paper presents a novel hybrid approach focusing on cross-domain adaptation techniques to address the challenge of domain shift. Our proposed method combines the Domain-Adversarial Neural Network (DANN) with Correlation Alignment (CORAL) to mitigate domain shifts between datasets. The DANN framework enforces domain-invariant feature learning through adversarial training. Using the PlantVillage Dataset, with controlled environment images, and the New Plant Village Dataset, with varied conditions, the model is first trained on PlantVillage and then adapted to New Plant Village using the CORAL loss to support the second-order statistics. In case of domain shift experiments with various datasets, DANN-CORAL achieved accuracies 91.39%, precision 93.36%, recall 88.9% and F1-scores 91.05% indicating the robustness and generalizability of our model is better than the other baseline models. This approach enhances model robustness and adaptability, providing insights into combining adversarial and statistical alignment for cross-domain adaptation in agricultural imaging.

Trade as an engine of growth has been debated much. Its ability to reduce poverty and inequality has attracted much debate in India. The present study investigates interlinkages of trade, poverty and inequality through employment generating capacity of India’s exports. The growing importance of value addition in trade requires countries to integrate into the global value chains to get factors of production employed gainfully. The sector-wise share of value addition and employment opportunities have been explored to know the employment-creating capacity of each sector. India a labour-abundant country is exporting capital-intensive items. It produces a skewed distribution of employment creating a skill premium consequently causing inequality. The study questions the long-term viability of poverty reduction through cheap imports amid rising inequality. It advocates for India’s increasing share of value addition in its exportable items of the manufacturing sector to ensure the generation of gainful employment and a reduction in poverty and inequality. Vertical integration with the Global Value Chain (GVC) has to be explored for a consistent and productive advantage for the local factor market. The
increasing trade in parts and components has placed them up for the creation of gainful employment thereby reducing inequality. The evaluation of sector-wise domestic value creation and its linkage with GVC is essential for policy purposes.

Purpose-This study aims to conduct a comprehensive methodological review, exploring the strategies used to address endogeneity within the realms of corporate governance and financial reporting. Design/methodology/approach-This research reviews the application of various methods to deal with endogeneity issue published in the 10 journals covering the corporate governance discipline included in the Web of Science's Social Sciences Citation Index. Findings-With a focus on empirical studies published in leading journals, the author scrutinizes the prevalence of endogeneity and the methodologies applied to mitigate its effects. The analysis reveals a predominant reliance on the two-stage least squares (2SLS) technique, a widely adopted instrumental variable (IV) approach. However, a notable observation emerges concerning the inconsistent utilization of clear exogenous IVs in some studies, highlighting a potential limitation in the application of 2SLS. Recognizing the challenges in identifying exogenous variables, the author proposes the generalized method of moments (GMM) as a viable alternative. GMM offers flexibility by not imposing the same exogeneity requirement on IVs but necessitates a larger sample size and an extended sample period. Research limitations/implications-The paper sensitizes researchers to the critical concern of endogeneity bias in governance research. It provides an outline for diagnosing and correcting potential bias, contributing to the awareness among researchers and encouraging a more critical approach to methodological choices, recognizing the prevalence of endogeneity in empirical studies, particularly focusing on the widely adopted 2SLS technique. Originality/value-Practitioners, including corporate executives and managers, can benefit from the study's insights by recognizing the importance of rigorous empirical research. Understanding the limitations and strengths of methodologies like 2SLS and GMM can inform evidence-based decisionmaking in the corporate governance realm.

O artigo analisa se a abertura comercial brasileira teve participacao ativa na promocao do nivel de renda per capita dos municipios cearenses e se esses efeitos se mostraram assimetricos, ao considerar diferentes polos de desenvolvimento do Estado, alem de avaliar a contribuicao do capital humano a partir de duas medidas, ensinos fundamental e medio. Apenas o capital humano medido pelo ensino medio apresentou influencia significante sobre o nivel de renda dos municipios cearenses. A abertura comercial apresentou um impacto negativo sobre a renda per capita do agregado dos municipios. Alem disso, ao considerar as diferencas regionais existentes no Estado, tendo os municipios menos desenvolvidos como referencia, conclui-se que a abertura comercial contribuiu para um crescimento desigual entre os municipios cearenses. Palavras-chave: abertura comercial; capital humano; renda per capita Titulo em ingles The impact of both trade openness and the contribution of the education differential...

This study examines the impact of exchange rate volatility on trade in Bangladesh to know the export, import risk, and leverage effect. Time-series implies monthly basis data from 2013M01 to 2019M06. The IFS, BB and FREED are the main data source for selected variables and this study conducts the ARCH, GARCH, EGARCH model to estimate the results. Major findings of the current study are GARCH models estimate that the exchange rate volatility creates a negative impact on trade but the EGARCH model estimates there is no leverage effect in the studied country. In addition that the findings of this study are to support the economic theory and activities in the aspect of Bangladesh's economy.  This perusal suggesting that the total trade volume will be increased with respect to time and stability in macroeconomic variables.

This paper examines the labor productivity gap between exporting and non-exporting Vietnamese manufacturing firms in industrialization during the period from 2010 to 2016 using enterprise-level panel data drawn from Vietnamese Annual Enterprise Censuses. Results show that the labor productivity of the manufacturing sectors increased during the study period. It can be concluded that the firms' productivity contributed to the current industrialization in Vietnam. On top of that, some manufacturing sectors with increasing labor productivity in the study period, namely: Fabricated metal products (code 25), Basic metals (code 24), Motor vehicles, trailers and semi-trailers (code 29), Computer, electronic and optical products (code 26), Leather and related products (code 15), Machinery and equipment not yet classified (code 28), Furniture (code 31), Electrical equipment (code 27), Other non-metallic mineral products (code 23), Other transport equipment (code 30), Wearing apparel (code 14), and Food products (code 10). Vietnam's government policy might play a crucial role in stimulating the current industrialization by targeting selective manufacturing sectors. Decomposing labor productivity gap between export and non-exporting firms, the results show that labor productivity in the former is about 57.5 percent lower than in the latter. By using Oaxaca-Blinder decomposition method, several firm-level variables are found to contribute significantly to the productivity gap via the endowment effect and the structural effect. Overall, the endowment effect surpasses the structural effect in the sample period. Among the factor contributions, capital stock plays the most important role. Empirical studies about the impact of the related policy on the manufacturing industries will be fruitful research agenda.

The ar� cle is an eff ect of the project-"Financializa� on-impact on the economy and society"-interna� onal conference, conducted by the University of Informa� on Technology and Management in Rzeszów with Narodowy Bank Polski under the scope of economic educa� on programme

Total public debt of ten cantons in the Federation of Bosnia and Herzegovina (FBiH), one of the two entities in BiH, have substantially increased in recent years. Since it is relatively small in nominal terms, this galloping trend have not attracted enough attention of decision makers. If these developments continue in the future, the public debt at cantonal level in FBiH might create serious fiscal problems and become one of central issues for policy makers. This has motivated our investigation of determinants that caused the increase in public debt over the period 2012-2018. We apply a panel regression analysis and investigate how budget deficit, trade balance, unemployment rate, size of population and institutional changes affect public debt. We find that public debt is positively associated with budget deficit but negatively associated with trade balance, the size of population and institutional changes. These findings motivated policy recommendations presented in the paper.

İş gücü verimliliği, üretim aşamasında gözlemlenen ya da ölçülen sermaye, iş gücü gibi belirli faktörlerin, üretime direkt ya da dolaylı yoldan etki eden katkılardır. Bu tanım, bir içinin ürettiği reel katma değer olarak da açıklanabilmektedir. Globalde, üretim aşaması ile istihdam arasındaki ilişkiyi kuran tek değişken iş gücü verimliliğidir. Ölçüm şekli, çalışan işçi başına üretim, brüt çıktının çalışan sayısı ile oranlanması olarak bilinmektedir. Literatürde, iş gücü verimliliği ve iş gücü verimliliğini etkileyen faktörler, uzun yıllardır temel araştırma konularından biri olmuştur. İş gücü verimliliğindeki düşüş, global ekonomilerde de olumsuz yaratmakta, tehlike oluşturmaktadır. Bu sebeple iş gücü verimliliğini olumsuz etkileyen makro değişkenleri kontrol altına almak için, onu etkileyen faktörlerin belirlenmesi gerekmektedir. Bu çalışmada, 22 OECD ülkesinin 1990-2016 dönemi verilerinden yararlanılarak, iş gücü verimliliğini etkileyen faktörleri panel veri analizi ile tespit etm...

The project aims to develop a personal virtual assistant for windows-based system. Jarvis draws its inspiration from virtual assistants like Cortana for Windows, and Siri for iOS. It has been designed to provide a user-friendly interface for carrying out a variety of tasks by employing certain well-defined commands. Users can interact with the assistant either through voice commands or using keyboard input. As a personal assistant, Jarvis assists the end-user with day-today activities like general human conversation, searching queries in google or yahoo, searching for videos, playing songs, live weather conditions, word meanings, searching for medicine details and reminding the user about the scheduled events and tasks. The virtual assistant takes the voice input through our microphone and it converts our voice into computer understandable language and gives the required solutions and answers which are asked by the user. This assistant connects with the world wide web to provide results that the user has questioned. This project works on voice input and gives output through voice and displays the text on the screen. The main agenda of our virtual assistant is that it makes people smart and give instant and computed results.

Many incentives have been implemented with the scope of increasing export in Turkey since 1980. In increasing competitive market condition, supporting export totally has significant importance to expand export in global markets. The exporter firms having international economic relationship must be provided sufficient competitive power in order to raise the country and sector share in global markets. The main purpose of this work is to state and reveal the effect of the export state subventions to Turkish foreign trade and economy. Since almost half of Turkey export has been fulfilled in the inward procecessing regime, we have analyzed the impact of inward processing regime export value on the growth of Turkey economy, Turkish export, and balance of trade. The results showed that there is a strong relation between the variables. Additionally, IPR has significant positive effect on the growth of Turkey economy. In this context, it can be said that the export state aids and inward processing regime have a positive effect on the growth of Turkey's economy positively.

The principles of the European Charter of Local Self-Government were accepted and incorporated in the legislation of Bosnia and Herzegovina. Fiscal federalism is classified in the area of public finance theory that studies the system of collection and distribution of resources in the public sector with two or more levels of government. In practice, in the public sector we have an increasing presence of a trend of decentralization of powers and jurisdictions of the state through: the transfer of political power and the transfer of influence from higher to lower levels of government. Decentralization achieves some of the following goals: reducing the share of public sector in the overall economy, mobilization of public revenues while reducing the cost of public services, harmonization of responsibilities for local public expenditures and the available financial resources, boosting of own revenues of local authorities, promotion of cooperation between different levels of fiscal government in provision of public services. Characteristic of the mechanisms of financing of local government units in Bosnia and Herzegovina are different levels of development, which is why there is a need to find financing models that would meet the needs of local population and achieve the highest possible level of harmonization of standards, as well as a higher degree of fiscal decentralization. The theory and practice developed the forms of financial equalization: vertical financial equalization and horizontal financial equalization. According to the principles of the European Charter on Local Self-Government, local authorities have the right to adequate sources of financing which they shall freely use within the framework of their powers. Funding sources should be appropriate to their powers. Objectification of allocation of public revenues by levels of state organization is achieved through harmonized policy for allocation of public revenue and fiscal equalization policy at all levels of government. A significant part of the local units' budget is quite meagre and strict. This is particularly true in local units in which most of the revenues are non-tax revenues. These non-tax revenues

We examine how participation and positioning in global value chains (GVC) affect wages. We also examine whether this relationship is conditioned by a country’s development level and labour market regulation. The results show that participation and upstream specialisation in GVCs are associated with higher wages but only in developed countries. In developing countries, while GVC participation is associated with higher wages, upstream specialisation exerts downward pressure on wages. For analysis focusing on the role of labour market regulation, we find that GVC participation only exerts a positive effect on wages under stringent labour market regulation. Under flexible labour market conditions, it exerts downward pressure on wages but allows for the effective reallocation of GVC workers into knowledge-intensive and high value added upstream activities in the value chain that are more productive and wage rewarding. Additional analysis on the effects of GVCs along the wage distribution...

This paper aims to estimate the Okun's Law for the Brazilian economy using the data from IBGE to quarterly values of the national and industrial Gross Domestic Product (GDP) from 1996 to 2013 and data from the Pesquisa Mensal de Emprego to the unemployment rates. The model used for the estimation is the gap model, which express how much the gap of the product relative to its potential affects the variation of the unemployment rate considering the nation as a whole and specifically the industrial sector. Thus, we can verify how sensible is the variation of the unemployment rate relative to the economic growth and from the coefficients' significance confirm the statistical validity of the Okun's Law in Brazil.

Recent decades have seen the emergence of global value chain (GVC) production arrangements in which firms fine-slice production processes and disperse activities over multiple countries. This paper analyses how the rise of GVCs affects trade politics in developed countries. Our theoretical model shows that GVCs drive a wedge between the interests of workers and of managers in unskilled-labourintensive industries, upsetting a traditional coalition that has favoured protectionism against competing imports. Managers of GVC firms switch towards favouring trade promotion since they can substitute foreign for local unskilled workers. The loss of their management ally further weakens the position of low-skilled workers, whose jobs and income are threatened by foreign competition. This new trend may help to explain the recent surge in anti-trade sentiment, while indicating the importance of an active policy response to deal with the economic challenges for affected workers.

El presente documento aborda el análisis de
clientes basado en un conjunto de transacciones que abarcan un
lapso de un año en un comercio minorista de regalos ocasionales
en línea con sede en el Reino Unido. El objetivo principal es
identificar y segmentar a los clientes en tres grupos con
características y comportamientos similares. Para lograr esto, se
emplean algoritmos de aprendizaje no supervisado en
combinación con el análisis RFM (Recency, Frequency, Monetary
value). Además, la metodología adoptada en este trabajo se centra
en la implementación efectiva de los algoritmos de ML por lo que
se apoya en MLOPs, haciendo énfasis en las dos fases iniciales
dispuestas en los principios de MLOPs [1]. Además, se llevan a
cabo tareas adicionales de exploración y preprocesamiento de
datos para comprender mejor la información y garantizar la
integridad de los datos utilizados en el análisis. Finalmente, se
aplican métricas de evaluación de rendimiento a los modelos
desarrollados y se utilizan herramientas visuales para comparar y
analizar los resultados obtenidos. Este enfoque integral
proporciona una comprensión profunda del comportamiento del
cliente para la formulación de estrategias de marketing más
efectivas y personalizadas para la empresa minorista.
This document addresses the analysis of
customers based on a set of transactions that span a
one-year span in an occasional gift retailer
online based in the UK. The main objective is
Identify and segment customers into three groups with
similar characteristics and behaviors. To achieve this,
employ unsupervised learning algorithms in
combination with RFM (Recency, Frequency, Monetary) analysis
value). Furthermore, the methodology adopted in this work focuses
in the effective implementation of ML algorithms so
relies on MLOPs, emphasizing the two initial phases
arranged in the principles of MLOPs [1]. Furthermore, they take
perform additional scanning and preprocessing tasks
data to better understand the information and ensure
integrity of the data used in the analysis. Finally I know
apply performance evaluation metrics to models
developed and visual tools are used to compare and
analyze the results obtained. This comprehensive approach
provides a deep understanding of the behavior of the
client for the formulation of marketing strategies
effective and personalized for the retail company.

In contemporary economies which are changing continuously, for achieving sustainable growth and satisfying stabilization, necessary interventions should be made on time. Hence business cycle fluctuations in the economy must be investigated carefully and the policies to be applied must be determined according to the achieved results. The aim of this study is to find out reasons of fluctuations and to state in which degree real business cycle theory can explain business cycles in Turkish economy. In able to confirm this by using quarterly data between 1987:1-2015:4 the shocks that cause to business cycles in Turkish economy are tried to be determined by vector autoregression (VAR) analysis through real business cycle theory. According to the results the main reason for GNP fluctuations is both long term and short term supply shocks which are acceptable with the business cycle theories previsions. Also it is discovered that nominal shocks do not have any effects on neither long run nor...

Various methods have been developed to assign pollen to its botanical origin. They range from technically complex approaches to the less precise but sophisticated chromatic assessment, in which the pollen colors are used for identification. However, a common challenge lies in the similarity of colors of pollen from different plant species. The advent of camera-based bee monitoring systems has sparked renewed interest in classifying pollen based on color and offers potential advances for honey bee biomonitoring. Despite the promise of improved sensor accuracy, a critical examination of whether color diversity within a single species may be the primary limiting factor has been lacking. Our comprehensive analysis, which includes over 85,000 corbicular pollen from 30 major pollen species, shows that the average color variation within each species is distinguishable to a human observer, similar to the difference between two dissimilar colors. From today's perspective, the considerable color variation within a single pollen source makes the use of color alone to classify pollen impractical. When picking a single pollen color from the entire dataset, we report a correct pollen type classification rate of 67 %. The accuracy was highly dependent on the type and ranged from 0 % for rare types with common colors to 99 % for distinct colors. The large color dispersion within species highlights the need for complementary methods to improve the accuracy and reliability of color-based pollen identification in biomonitoring applications.

The CPTPP, or the Comprehensive and Progressive Agreement for Trans-Pacific Partnership, is an example of a "mega-regional" free trade agreement, whose provisions on the rules of origin and trade facilitation can have potentially large impacts on the CPTPP-wide supply chains. In this paper, we investigate whether the CPTPP members are key upstream and downstream trade partners to each other in the global value chains. We develop formulas of bilateral upstreamness and downstreamness, based on the gross-export decomposition framework of Koopman, Wang and Wei (2014) and Borin and Mancini (2017). We demonstrate how the decomposition of gross exports can be used to construct informative measures of the position of countries in global value chains.

This study examines the impact of exchange rate volatility on trade in Bangladesh to know the export, import risk, and leverage effect. Time-series implies monthly basis data from 2013M01 to 2019M06. The IFS, BB and FREED are the main data source for selected variables and this study conducts the ARCH, GARCH, EGARCH model to estimate the results. Major findings of the current study are GARCH models estimate that the exchange rate volatility creates a negative impact on trade but the EGARCH model estimates there is no leverage effect in the studied country. In addition that the findings of this study are to support the economic theory and activities in the aspect of Bangladesh's economy.  This perusal suggesting that the total trade volume will be increased with respect to time and stability in macroeconomic variables.

Multiple economic indicators and tools are intensively discussed in the academic literature. In the current piece of art, the factors like FDI, Gross Domestic Productivity (GDP), Human development, literacy rate, youth and unemployment have been primarily analyzed to define the current economic situation in the country. Specifically the indicator of ‘Foreign Direct Investment’ (FDI) has been focused to define the economic growth of Pakistan. For the research secondary source of data has been gathered for the time duration of last 5 years. Findings show that, factors such as FDI, GDP and Human development are of significant importance for the economic growth and development of Pakistan, while the factors like literacy rate, youth and unemployment are relatively less important. The paper is of great importance. This study can play a vital role in compelling the government authorities to focus their intention towards the economic and growth indicators of high importance. The study can ...

This paper investigates the effects of human capital on bilateral domestic value-added trade in global value chains (GVCs) for 43 countries and 56 sectors. In contrast to previous studies, this paper estimated an approximate gravity model of value-added trade to capture the role of human capital in determining the cross-border production linkages via value-added trade. The results show that the domestic value-added trade flows depend critically on human capital development in both exporting and importing countries. The results indicate a positive effect of skilled intensity on bilateral domestic value-added trade in GVCs. We also observe a larger positive effect of skills on the GVC value-added trade for the developing economies. The paper highlights the importance of trade liberalization and forward-looking human capital development policies for the competitiveness of the developing countries in the value-added trade in GVC.

The goal of this paper is to understand the nature of capital adjustment costs. This topic is central to the understanding of investment, one of the most important and volatile components of aggregate activity. Moreover, understanding of the nature of adjustment costs is vital for the evaluation of policies, such as tax credits, that attempt to influence investment and thus aggregate activity. Despite the obvious importance of investment to macroeconomics, it remains an enigma. Costs of adjusting the stock of capital reflect a variety of interrelated factors that are difficult to measure directly or precisely. 1 Changing the level of capital services at a business generates disruption costs during installation of any new or replacement capital and costly learning must be incurred as the structure of production may have been changed. Installing new equipment or structures often involves delivery lags and time to install and/or build. The irreversibility of many projects caused by a lack of secondary markets for capital goods acts as another form of adjustment cost. Some industry case studies (e.g., Holt et al. [1960], Peck [1974], Ito, Bresnahan and Greenstein [1998]) provide a detailed characterization of the nature of the adjustment costs for specific technologies. A reading of these industry case studies suggest that there are indeed many different facets of adjustment costs and that, in terms of modeling these adjustment costs, both convex and nonconvex elements are likely to be present. 2 Despite this perspective from the industry case studies, the workhorse model of the investment literature has been a standard neoclassical model

The failure of attaining the desired outcome following the import substitution industrialization in the period of 1963-1980 led Turkey to adopt export-oriented growth (EOG) model after the 1980s. In this context, the impacts of export performance on economic growth are important for assessing how successful these practices are. With this motivation, the study aims to provide new empirical evidence on the validity of the export-led growth hypothesis in Turkey. The study employees time series data of the period 1990M1-2016M12 to examine the causality relation between export and economic growth. The Bootstrap approach developed by Hacker and Hatemi (2006) was followed in order to determine this relationship in the study. A two-way causality relationship between export and output growth is evident in test results based on the bootstrap simulation technique. Findings indicate that export-oriented growth hypothesis is not valid in narrow sense, whereas the hypothesis is valid in broad sense for Turkey. The overall results obtained from the study indicate that exports policies can be used as a tool to speed up economic growth with existing practices. However, the two-way causality relationship indicates that the increase in output growth has boosted exports by increasing imports of inputs used in export products. In this respect, in order to accelerate economic growth through the export-oriented strategies, Turkey needs to increase its domestic input production and the share of global value chains of local companies; in other words, it is necessary to focus on export of higher value-added products.

The details of the impact of exports on economic growth have been one of the most important topics in the literature. Numerous studies asserted that open economies grow faster than the closed ones because of the effect of the exports on GDP and increase of productivity and efficiency through foreign competition. Concurrently, these studies indicated that there are strong correlation exists between exports and economic growth. On the other hand, high share of production of technology in today's economies is the key for sustainable and high rates of growth and the relationship between technology exports and economic growth has been a topic of interest among economists. Many studies in the literature published on export-led hypothesis in recent years but only few studies addressed the technological issues. Therefore, the purpose of this study is to examine the impact of technology exports on economic growth of selected OECD countries using dynamic panel method. We established the m...

Abstrak Perdebatan tentang pelemahan hubungan antara nilai tukar dan ekspor telah meningkat dalam beberapa tahun terakhir, dan meningkatnya tren perdagangan terkait rantai nilai global (Global Value Chain/GVC) diasumsikan menjadi sumber melemahnya hubungan di antara keduanya. Dengan menggunakan data spesifik industri manufaktur, studi ini bertujuan untuk menyelidiki dampak GVC pada hubungan Nilai Tukar Efektif Riil (Real Effective Exchange Rate/REER) dan ekspor di empat negara ASEAN. Estimasi dilakukan menggunakan regresi Least Square Dummy Variable (LSDV) untuk periode sampel dari tahun 2009 hingga 2015. Hasil penelitian ini menunjukkan bahwa di Filipina, koefisien elastisitas nilai tukar ekspor dan partisipasi ke GVC tidak signifikan secara statistik. Sebaliknya di Indonesia dan Malaysia, secara rata-rata, integrasi ke GVC dengan berbagai pengukuran akan menurunkan elastisitas ekspor terhadap perubahan REER sekitar 70% sampai 89%. Lebih lanjut, estimasi terhadap data Thailand dan ...