Gross Domestic Product Of Countries Research Articles

ARE FOREIGN DIRECT INVESTMENTS STILL IMPORTANT FOR GROWTH? A NEW APPROACH FOR TÜRKİYE

So far, especially underdeveloped and developing countries have tried to attract foreign direct investments by adopting open economy policies. In this study, we investigated whether direct foreign investments have a significant impact on the gross domestic product of countries. For this purpose, we examined the long-term relationships between GDP and foreign direct investment between 1986 and 2020 in Türkiye, which is considered a developing country, with a new approach, structural breaks. The results of the research show that there is a two-way positive relationship between the variables. Accordingly, while GDP affects foreign direct investments in the long run, foreign direct investments also affect GDP. The findings obtained from the CCR, FMOLS and DOLS methods used to estimate the coefficients in the long term are generally consistent with each other. It is understood from this study that the policy of adopting open economy policies and attracting direct investments continues to be valid in Turkiye.

How US Fiscal and Monetary Policy affect the GDP of Countries with Fixed and Flexible Exchange Rates: Estimates using Korean Data from 1963 to 2022

The simplest large country IS/LM/BP model (Mundell-Fleming Model) predicts that an increase in Government spending in the US would cause an increase in Gross Domestic Product (GDP) for countries with flexible exchange rate regimes and decreases in GDP for countries with fixed exchange rate regimes. In contrast, increases in the US money supply would cause an increase in GDP for countries with fixed exchange rate regimes and decreases in GDP for countries with flexible exchange rate regimes. This paper uses a solution to the omitted variables problem of regression analysis to estimate the effects of US fiscal and monetary policy on the Republic of Korea using quarterly data. The data splits into two sections: (1) from 1962 through 1997, Korea had a fixed exchange rate regime and (2) from 1998 through 2022, Korea had a flexible exchange rate regime. The empirical results fit the Large Country IS/LM/BP predictions.

Impact of Foreign Direct Investment on the Economic Growth of Nepal

The study investigates the factors influencing foreign direct investment (FDI) and its sectoral distribution in the Nepalese economy, specifically examining the relationship and impact of FDI on sectors such as minerals, manufacturing, construction, energy, and services over a ten-year period from 2011/12 to 2020/21. Descriptive and correlational research design was employed in the annual time series data. The major findings reveal a positive correlation (correlation co-efficient of 0.668) between FDI and Gross Domestic Product (GDP), signifying a significant relationship. The study concludes that FDI has a substantial impact on Nepal's GDP. Additionally, sectoral FDI investments, including minerals, construction, energy, manufacturing, and services, also positively influence the country's GDP. The study suggests that despite Nepal's untapped FDI potential, implementation challenges, lengthy approval processes, and a service sector bias among investors hinder optimal exploitation of investment opportunities in other sectors with higher productivity.

Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

The success of an economy can bring significant benefits to a country and its society. One way to measure economic growth is by looking at the value of gross domestic product (GDP). The value of a country's GDP is influenced by many factors, including inflation, exports, and imports. Therefore, predicting future economic growth requires forecasting the value of GDP. GDP forecasts are crucial as they provide information about the economic development of a country over a specific period of time. By forecasting GDP, governments and investors can make informed decisions to optimize profits or minimize risks when investing or doing business in a country. This research aims to forecast Indonesia's GDP for the second, third, and fourth quarters of 2023 using the best models from machine learning or deep learning methods. Forecasts are made for each method with and without additional variables. The results indicate that the SimpleRNN algorithm from deep learning without additional variables is the method with the smallest RMSE and MAPE for GDP forecasting. Therefore, the best method used to forecast GDP is the SimpleRNN algorithm, and the forecasted GDP values for Indonesia's second, third, and fourth quarters are 5.350.840,00, 5.483.895,00, and 5.610.077,50 billion rupiah.

Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases

The essential prerequisite for attending the G20 conference is a country's GDP because G20 members can significantly boost the economy and preserve the nation's financial stability. Time series data can be thought of as a country's Gross Domestic Product (GDP) at a particular point in time. In this research, the GDP numbers from five Southeast Asian nations that are attending the G20 fulfilling are used. The total was 47 observations made yearly, which extended from 1975 to 2001. A time series analysis was performed on the data gathered. The correctness of time series models is also evaluated using control charts based on this research. The control chart is constructed using the time series model's residuals as observations. After applying the IMR control chart for verification, the results revealed that the residuals, specifically the models for GDP in Malaysia, Singapore, and Thailand, are out of control. The white noise assumption is fulfilled by the time series model obtained for Brunei and Indonesia's GDP, but the residuals are out of control. Whether controlled residuals are used depends on the accuracy with which the time series model predicts the future. If the amount of residuals is under control, then the time series model produced is accurate and good enough for prediction. After using the IMR control chart to verify the residuals, the results indicate that the residuals, namely the models for GDP in Malaysia, Singapore, and Thailand, are not under control. The assumption of white noise is proved correct by the time series model obtained for the GDP of Brunei Darussalam and Indonesia. With that being said, the residuals are entirely out of control. The model must improve its ability to forecast various future periods. It is a consequence of the unmanageable residuals that the model contains. Even if the best available model has been obtained based on the criteria that have been defined, it is anticipated that the research findings will improve the theories that have previously been developed and raise knowledge regarding the usefulness of testing the time series model. In addition to all of that, it is intended that the research will produce a summary of cases of an increase in GDP from five Southeast Asian countries participating in the G20 conference.

The effect of low rate of corporate taxation on foreign direct investments (FDI) and gross domestic product (GDP): A case study of ten selected countries (2018-2022)

This research was undertaken to examine the effect of low corporation tax rate on Foreign Direct Investment (FDI) inflow and Gross Domestic Product (GDP). Investors and multinational firms are very rational and therefore prefer to invest in countries where the cost of taxation will be at the barest minimum to maximize their profit. The study aimed to critically analyze the Corporate Income Tax (CIT) rates of the randomly selected countries of the world, and their respective impacts on FDI and GDP. The study is descriptive in nature, based on quantitative data, sourced from various reports of Statutory Corporate Income Tax Rates of Tax Foundation, World Bank and UNCTAD World Investment Report of 2022. Ex-post Facto research design was deployed; while data were analyzed with a General Linear Model of Multivariate Analysis of Variance (MANOVA) with the aid of SPSS version 25. The study found that low rate of corporation tax has a positive and significant effect on FDI as well as on GDP. That is, CIT rate is a dominant determinant for FDI and GDP of countries.

Assesment Of Static Panel Model Of Tourist Demand In Croatia

Croatia is an important and widely recognizable tourist destination. The importance of tourism in Croatia is manifested in many aspects, and the share of income from tourism in BDP in Croatia is around 20%. The most numerous guests in Croatia are foreign guests, mostly from Germany, Austria, Slovenia, Italy and Poland. In this paper, foreign tourism demand in the Republic of Croatia was analyzed and impact of five selected variables on foreign tourism demand is estimated: gross domestic product of countries from which tourists come from, prices in Croatia, prices in competing countries, distance between countries and accommodation capacities. The analysis was carried out for the nine-year time period from 2010 to 2018 based on data for 32 countries from which tourists come, and OLS model, a panel model with fixed effects and a panel model with random effects were estimated. The adequacy of the model was tested with the Hausman test, Breusch-Pagan Lagrange test and F-test. A panel model with Driscoll-Kraay standard errors was estimated. The estimated model showed that the gross domestic product of the countries from which the tourists arrive has a statistically significant and positive influence on the foreign tourism demand in Croatia, while at a significance level of 10% the accommodation capacity in Croatia has a positive and statistically significant influence. The impact of other variables taken into account was not statistically significant.

Relationships between Brand Value and Country's GDP

Brand development has emerged as a critical strategy for economic prosperity where assets from both physical and nonphysical sources significantly influence a nation's economy. However, the impact of these intangible assets on economic growth still requires further clarification. This study aims to investigate the relationship between nation brand value and economic growth and to examine whether this impact varies depending on countries' income levels. Based on data from the global soft power index and gross domestic product (GDP) of 120 countries from Brand Finance Nation Brands and Word Bank in 2022, linear regression and moderation analysis results reveal that nation brand values positively impact national economic growth. The results of the moderation effect analysis by the PROCESS macro reveal that the impact of nation brand value on economic growth is significantly more substantial for lower-income economies than for higher-income economies. Our study is one of a few attempts to clarify the effect of nation brand value on a nation's economic growth. The outcome of this research provides more understanding for exploiting the nation brand development concept to create a superior competitive advantage, consequently leading to the prosperity of nation economies. Doi: 10.28991/HIJ-2023-04-04-08 Full Text: PDF

Looking at GDP from a Statistical Perspective: Spatio-Temporal GSTAR(1;1) Model

The gross domestic product (GDP) is a significant indicator for evaluating the performance of an economy. The GDP of a nation can be used to get a sense of the size and health of that nation's economy. Indonesia is the only nation from Southeast Asia to be represented in the G20. All G20’s countries play vital roles in creating the economic landscape of the region, the world, and everything in between. This research is focused on the increase of the GDP in Indonesia, Malaysia, Singapore, Thailand, and Brunei Darussalam. The spatial influence of GDP can be seen in the growth of each nation's infrastructure and industrial sector, for example. at the regional level, the increase of a country's GDP can also have an effect on the countries that are its neighbors. Using the GSTAR model, the aim of this study is to investigate the spatial and temporal influences on the GDP statistics of five different countries. The GSTAR model is distinguished by the presence of a weight matrix, which is one of its distinguishing features. In addition, the aim of this research is to select the most appropriate weight matrix for the purpose of representing the spatial effect on GDP statistics. Uniform, queen contiguity, and inverse distance weight matrices are the types of weight matrices that are utilized. Calculating each weight matrix, estimating relevant parameters, and performing diagnostic tests are the primary activities involved in this investigation. As a consequence of this, a weight matrix that is uniform in its distribution is the one that performs the best. The spatial and temporal correlations of GDP data may be accurately represented by the GSTAR model when it is equipped with a uniform weight matrix. This model is applied to five different countries.

Lexicon-Based and Naive Bayes Sentiment Analysis for Recommending the Best Marketplace Selection as a Marketing Strategy for MSMEs

MSMEs (micro, small, and medium enterprises) play an essential role in the Indonesian economy, contributing to 60% of the country's GDP (gross domestic product), creating jobs, and increasing non-oil and gas exports. However, MSMEs in Indonesia face various challenges, including access to technology, digital marketing tools, financial resources, limited market distribution, and low technological literacy. Marketplaces provide an essential marketing channel for MSMEs to increase their competitiveness and sales. Sentiment analysis can assist businesses in making informed decisions about which marketplace to use to increase customer satisfaction. Apart from the importance of the marketplace for MSMEs in Indonesia, research on sentiment analysis for marketplace recommendations is still minimal. Therefore, this study aims to analyze six popular marketplaces in Indonesia using Lexicon-based and naïve Bayes research methods to provide the best marketplace recommendations for MSME marketing. The results showed that Blibli.com had the highest accuracy, followed by Tokopedia, Tiktokshop, Lazada, Shopee, and Bukalapak. Blibli.com received positive reviews with 96.33%, followed by Tokopedia with 95.25%, Tiktokshop with 94.61%, and Lazada with the highest accuracy. 94.22%, Shopee 92.18%, and Bukalapak 89.57%. This research has two significant contributions. First, making a scientific contribution by applying a combination model of lexicon-based and naïve Bayes to analyze market sentiment in Indonesia Second, offering a practical contribution by providing recommendations to MSME actors and policymakers in choosing the best marketplace for MSMEs marketing purposes in Indonesia. By utilizing the recommended marketplace, MSMEs can optimize their marketing strategy and increase their competitiveness in the digital marketplace.

Dependent Structure of Gross Domestic Product and Inflow of Foreign Direct Investment from China in Asean Member Countries Using Varma-Garch Copula Approach

This study aims to investigate and analyse the construction of dependencies between gross domestic product (GDP) and inflow of foreign direct investment from China (FDI) on 5 ASEAN countries (ASEAN-5), namely, Singapore, Malaysia, Thailand, the Philippines, and Indonesia, by using each country's GDP data. The data of ASEAN-5 and FDI from 1996 until 2018 was used via the VARMA-GARCH method, considering the lowest error value in the form of copulas demonstration. The results of the study reveal that the data in the asymmetric copula group that the risk of direct investment from China occurred by gross domestic product volatility of each country has a massive impact. On the contrary, for any country in the symmetric copula group, the risk from gross domestic product volatility that is directly affected from foreign investment has a slight difference

CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning

The industrialization has been the primary cause of the economic boom in almost all countries. However, this happened at the cost of the environment, as industrialization also caused carbon emissions to increase exponentially. According to the established literature, Gross Domestic Product (GDP) is related to carbon emissions (CO2) which could be optimally employed to precisely estimate a country's GDP. However, the scarcity of data is a significant bottleneck that could be handled using transfer learning (TL) which uses previously learned information to resolve new tasks, more specifically, related tasks. Notably, TL is highly vulnerable to performance degradation due to the deficiency of suitable information and hesitancy in decision-making. Therefore, this paper proposes ‘Intuitionistic Fuzzy Transfer Learning (IFTL)’, which is trained to use CO2 emission data of developed nations and is tested for its prediction of GDP in a developing nation. IFTL exploits the concepts of intuitionistic fuzzy sets (IFSs) and a newly introduced function called the modified Hausdorff distance function. The proposed IFTL is investigated to demonstrate its actual capabilities for TL in modeling hesitancy. To further emphasize the role of hesitancy modelled with IFSs, we propose an ordinary fuzzy set (FS) based transfer learning. The prediction accuracy of the IFTL is further compared with widely used machine learning approaches, extreme learning machines, support vector regression, and generalized regression neural networks. It is observed that IFTL capably ensured significant improvements in the prediction accuracy over other existing approaches whenever training and testing data have huge data distribution differences. Moreover, the proposed IFTL is deterministic in nature and presents a novel way for mathematically computing the intuitionistic hesitation degree.

Assessing COVID-19 Effects on Inflation, Unemployment, and GDP in Africa: What Do the Data Show via GIS and Spatial Statistics?

What are the effects of Corona Virus Disease 19 (COVID-19) on inflation, unemployment, and GDP in Africa? Using geo-coded cross-sectional data taken from the World Health Organization and International Monetary Fund, we investigate the spatial distribution of COVID-19 and its effects on inflation, unemployment, and Gross Domestic Product (GDP) in Africa by employing the Geographic Information System (GIS), multivariate analysis of covariance (MANCOVA), and spatial statistics. The entire dataset was analyzed using Stata, ArcGIS, and R software. The result shows (1) that there is evidence of a spatial pattern of COVID-19 cases and death rate clustering behavior in Africa, verifying the existence of spatial autocorrelation. The result also reveals (2) that COVID-19 has a negative effect on unemployment, inflation, and GDP in Africa. We confirmed that (3) temperature, rainfall, and humidity were statistically significantly associated with the spread of the COVID-19 pandemic in Africa. The comparison of the GDP of African countries before and after the pandemic shows (4) a large decrease in GDP, the highest in Seychelles (23 percent). The result of the study shows (5) that there has been a significant increase in inflation and unemployment rates in all countries since the outbreak of the pandemic as compared to the time before the outbreak. There is also evidence that (6) there is a significant relationship between death rate due to COVID-19 and population density; temperature with COVID-19 cases and death rate; and precipitation with death rate due to COVID-19. Therefore, respective governments and the international community need to pay attention to controlling/reducing the impact of COVID-19 on inflation, unemployment, and GDP, focusing on the indicated demographic and environmental variables.

Using dashboards to verify coronavirus (COVID-19) vaccinations can reduce fatality rates in countries/regions: Development and usability study.

The new coronavirus disease 2019 (COVID-19) pandemic is raging worldwide. The administered vaccination has become a significant vehicle against the virus. Three hypotheses were made and required for validation: the number of vaccines administered is related to the country gross domestic product (GDP), vaccines can reduce the fatality rate (FR), and dashboards can present more meaningful information than traditionally static visualizations. Research data were downloaded from the GitHub website. The aims of this study are to verify that the number of vaccination uptakes is related to the country GDP, that vaccines can reduce FR, and that dashboards can provide more meaningful information than traditionally static visualizations. The COVID-19 cumulative number of confirmed cases (CNCCs) and deaths were downloaded from the GitHub website for countries/regions on November 6, 2021. Four variables between January 1, 2021, and November 6, 2021, were collected, including CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions. We applied the Kano model, forest plot, and choropleth map to demonstrate and verify the 3 hypotheses using correlation coefficients (CC) between vaccination and FRs. Dashboards used to display the vaccination effects were on Google Maps. We observed that the higher the GDP, the more vaccines are administered (Association = 0.68, t = 13.14, P < .001) in countries, the FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low income, Lower middle income, and South America, as well as the application (app) with dashboard-type choropleth map can be used to show the comparison of vaccination rates for countries/regions using line charts. This research uses the Kano map, forest plot, and choropleth map to verify the 3 hypotheses and provides insights into the vaccination effect against the FR for relevant epidemic studies in the future.

Indian millets trade potential-cum-performance: Economic perspective

The present study examines the trade performance of millets, competitiveness and their impact on the country’s growth in the agriculture sector. Empirical findings revealed that Indian millet exports contributed significantly to the global basket from the year 2000–2020 and had a huge potential in the international market. During the year 2011–2020, India’s major exporting partners were neighbouring countries, i.e. Pakistan, Vietnam, Nepal and Saudi Arabia, wherein India exported more than 50% of the country’s millet export. The study looked at India’s comparative advantage of millets by country by using Revealed Comparative Advantage (RCA), and the results showed that Pakistan, Saudi Arabia, Vietnam, and Yemen were the major millet importing countries exhibiting RCA value greater than one, indicating that India had a significant advantage in exporting millets to these partner countries during the recent study period (2011–2020). Furthermore, gravity model is calibrated by using the variables like Gross Domestic Product (GDP), exchange rate and population of the respective countries. The results of the gravity model suggested that the country's GDP would grow with an increase in millets exports in India, but the country's exports would decrease with an increase in population.

Modelling the Tourism Revenue of Türkiye Using Deep Learning Networks

The gross domestic product of countries plays a key role in the development and wealth of nations. There are several components of the gross domestic product, such as industrial revenue, revenue from services, and tourism revenue. Türkiye is located in Anatolia, which is very rich from a historical viewpoint. Therefore, Türkiye attracts tourists from all over the world, making its tourism revenue an important contributor to its gross domestic product. This study aimed to model the tourism revenue of Türkiye using machine learning methods. In this study, the tourism revenue of Türkiye, dependent on the number of tourists, oil prices, and the exchange rate, are modelled for the period of 2008-2022. The data of these variables were taken from official sources, and then the causality analyses were carried out. As the next step, the tourism revenue is modelled as a function of the number of tourists, oil prices, and the exchange rate. A deep learning network is developed using the Python programming language for modelling the tourism revenue. The developed deep learning network is then trained using a portion of the data. The performance of the developed deep learning network is then evaluated using the performance metrics such as the coefficient of determination, mean absolute error, root means square error, and the mean absolute percentage error. These metrics show that the developed deep learning network successfully models the tourism revenue dependent on the number of tourists, oil prices, and the exchange rate.

The impact of cancer on psychosocial function and quality of life: A cross-sectional study in 18 pan-European countries.

To improve psychosocial cancer care in Europe, more information is needed on psychosocial function and quality of life (QoL) among cancer patients in European countries. To investigate differences in psychosocial function between cancer survivors and the general population in Europe, in relation to national economic status and personal factors. Data were from the Survey of Health, Aging and Retirement in Europe (Wave 6). Main outcomes were psychosocial functioning: activity limitations, income adequacy, loneliness, depression, and QoL. Factors possibly associated with the main outcomes were ever having cancer, gross domestic product (GDP), and personal factors (age, gender, education, marriage status, employment status, number of children, number of chronic diseases). The study sample featured 6238 cancer survivors and 60,961 individuals without cancer aged 50 or older in 17 European countries and Israel. Levels of depression were higher and QoL was lower among cancer survivors compared to individuals without cancer and worse in low GDP countries, whereas differences in income adequacy and loneliness were not statistically significant. The interaction of cancer groups and country groups indicated a significant interactional effect on activity limitations, loneliness, depression, and QoL. In a multivariate regression analysis, personal factors, GDP, and being a cancer survivor predicted the main outcome variables. Cancer has a persistent negative effect on survivors that is related to a country's GDP. Cancer survivors in low-GDP countries are affected by the consequences of cancer intertwined with the hardships of living in a low-GDP country.

The Relationsip between Population Ages 30-34 and Inflation of The Growth Domestic Bruto Inflator

This study aims to determine the relationship between the population aged 30-34 years and inflation as measured by the Gross Domestic Product (GDP) deflator. The method used in this study is the Granger Causality Test. The impact of this research can be seen from this study's results, which is men aged 30-34 years are very influential in the economy. Moreover, most workers in this world are men. The population aged 30-34 greatly influences economic growth and inflation. The male population aged 30-34 has more influence on economic growth and inflation than the female population aged 30-34. In the opposite direction, an increase in unemployment causes GDP to grow more slowly or even fall. Labor is one of the factors driving GDP growth. Increased population growth also has a positive effect on government spending. However, the inflation rate will also have a negative impact on government spending. A high inflation rate can worsen the value of a country's real GDP. If GDP increases, then a country's economic growth is improving. And if the average rate of economic growth in a country from year to year is higher, the income per capita of the community will also increase.

Biological Control Methods for Agricultural Mites: A Review

Agriculture is the most important economic activity around the world with its contributions reflected by its share in the gross domestic product of countries. It ensures food security, provides raw materials for many industries and employment opportunities. Today, pest and diseases are among the main challenges plaguing the agricultural sector. More specifically, phytophagous mites are devastating many economically important crops worldwide. Tetranychus urticae Koch (Two-spotted spider mites), Raoiella indica Hirst, (red palm mites), Polyphagotarsonemus latus Banks (broad mites) and Panonychus ulmi Koch (European red mites) are widely known for their extensive damage to crops. To control these mites, farmers have incorporated the use of pesticides into their farming systems. However, chemical control comes with many problems such as insect resistance, detrimental environmental effects and various human health implications. As such biological control is an environmentally friendly alternative that uses bio-control agents such as predators, pathogens and parasitoids to control pests. In this review, it was found that predators and pathogens were the most widely used biocontrol agents, specifically, the Amblyseius spp. Berlese were most effective and widely documented. The available literature also suggests that for successful control of agricultural mites via biological control, there needs to be a clear understanding of the behaviors of these mites and the bio-control agents used.

Applying Machine Learning to Maximize Agricultural Yield to Handle the Food Crisis and Sustainable Growth

The intelligent agriculture system is a farming-based project, and it will suggest the best crops in the region and maximum yield. Thus, it will affect all the stakeholders related to farming. It may use various technologies such as big data and ML (machine learning). These technologies will help us in fetching the data to train it according to the needs. The agricultural sector also has a significant impact on the country's GDP (gross domestic product). India is rich in the area of agriculture, but the yields per hectare are exceptionally low as compared to the land. The business logic in Python uses machine learning techniques to predict the most suitable crops in the forecasted weather and soil conditions at a specified location. The proposed system will integrate the data obtained from the weather department and by applying machine learning algorithms: Naïve Bayes (polynomial) and support vector machine (SVM) and unsupervised machine learning algorithms like k-means clustering multiple linear regression for weather and environmental conditions are made.