Component Of Gross Domestic Product Research Articles

Banking sector and economic growth in the digital transformation era: insights from maximum likelihood and Bayesian structural equation modeling

PurposeIn the digital era, the banking sector has transformed into a powerful intermediary, effectively connecting surplus and deficit units. This dynamic landscape empowers savers to secure their finances and generate returns, while simultaneously enabling businesses and individuals to access capital for investment and promoting economic growth. This study explores the relationships among banking development dimensions – represented by primary assets and liabilities, bank capital (core capital and required reserves) and economic growth as measured by components of gross domestic product (GDP).Design/methodology/approachThe study consolidated monthly balance sheets from digital banks over a 20-year period, resulting in an aggregate monthly balance sheet that reflects the financial position of all digital banks in the Palestinian economy. The research employs both maximum likelihood and Bayesian structural equation modeling to measure the causal pathways of the consolidated balance sheet with the individual components of GDP.FindingsThe results revealed that bank main assets (investments and loans) and liabilities (deposits) collectively explain for 97% of bank capital. Investments and loans demonstrate significant negative correlations with bank capital, while deposits exhibit a positive impact. This leads to a fundamental conclusion that a substantial proportion of retained earnings within the banking sector is reinvested, fueling expansion and growth. Additionally, the results showed a significant relationship between bank capital and various GDP components, including private consumption, gross investment and net exports (p = 0.000). However, while the relationship between bank capital and government spending was insignificant in the maximum likelihood estimation, Bayesian estimation revealed a slight yet positive impact of bank capital on government spending.Originality/valueThis research stands out due to its unique exploration of the intricate relationship between bank sector development dimensions, primary assets and liabilities and their impact on bank capital in the digital era. It offers fresh insights by dividing this connection into specific dimensions and constructs, utilizing a comprehensive two-decade dataset covering the digital banks records.

Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company

Electrical energy is one of the components of Gross Domestic Product that is able to encourage the economy because it has become a basic need of the community. To meet the increasing demand for electrical energy, the Indonesia National Electricity Providers (PLN) need to predict the amount of electrical power required based on the customer numbers to meet the demand for adequate electricity supply. This study aims to predict electric power based on electricity user customers using a time series regression model. The data used in this study are secondary data which get from PLN annual report in 2021. This study resulted in a finding of the best prediction model based on the Akaike Information Criterion (AIC) value, namely the time series regression model with the error value modeled by the AR(1) model, while the forecasting accuracy measure used the value MAPE of 9.77%. This means that the result of model prediction is highly accurate.

Identification barriers to entry in the sport tourism industry: A challenge for local sports entrepreneurs

The Indonesian sports industry is required by law to contribute to the country's gross domestic product (GDP) by becoming an upstream-to-downstream industry, which refers to the total value added to sports products annually and is a component of GDP. The objectives of this study were to find the factors that become barriers to entry (and also exit) from the sports industry. The study used a qualitative method. Using the purposive method, the data were collected from 10 people including the village head, entrepreneurs, and Cisaat Village Community through FGD’s (Forum Group Disscussion) and semi-structured interviews were gathered. To analyze the data using thematic analysis. Cisaat Village was chosen as an analysis material to find barriers to entry factors, especially for sport tourism. The findings of this study are that the capital factor, the lack of knowledge and experience, human resources, are the most barrier to entry in sport tourism industry. As implications of future research, researchers can use these factors to identify how they affect sports entrepreneurs in their countries. By addressing these barriers, the sports industry can thrive and make a substantial contribution to national economic development.

The Forecasting of a Leading Country’s Government Expenditure Using a Recurrent Neural Network with a Gated Recurrent Unit

Economic forecasting is crucial in determining a country’s economic growth or decline. Productivity and the labor force must be increased to achieve economic growth, which leads to the growth of gross domestic product (GDP) and income. Machine learning has been used to provide accurate economic forecasts, which are essential to sound economic policy. This study formulated a gated recurrent unit (GRU) neural network model to predict government expenditure, an essential component of gross domestic product. The GRU model was evaluated against autoregressive integrated moving average, support vector regression, exponential smoothing, extreme gradient boosting, convolutional neural network, and long short-term memory models using World Bank data regarding government expenditure from 1990 to 2020. The mean absolute error, root mean square error, and mean absolute percentage error were used as performance metrics. The GRU model demonstrates superior performance compared to all other models in terms of MAE, RMSE, and MAPE (with an average MAPE of 2.774%) when forecasting government spending using data from the world’s 15 largest economies from 1990 to 2020. The results indicate that the GRU can be used to provide accurate economic forecasts.

Facilitating Economic Growth by Leveraging on Infrastructure Development

To achieve most of the United Nations’ Sustainable Development Goals (SDGs) such as the elimination of poverty and hunger, the establishment of good health and well-being, provision of quality education, creation of decent work and economic growth, infrastructure development is central. Thus, the study aimed at determining Nigeria’s priority infrastructure based on the endogenous growth framework. The study covered a period of forty years; from 1981 to 2020. Data was sourced from the Global Economy Data (2020) and the World Development Indicators (2020). Using Autoregressive Distributed Lag (ARDL) technique, the study found that in the short-run, communication, electricity, education and health infrastructures had positive impact on the real gross domestic product (GDP), while transport infrastructure had a negative impact on the real GDP. In the long-run, communication, electricity and education had a positive impact on the real GDP, while transport and health infrastructures had a negative impact on the real GDP. Also, the study found that of all the infrastructure components, electricity infrastructure was most impactful as it positively influenced all the components of GDP both in the short-run and in the long-run. Therefore, the study recommended that the Nigerian Communications Commission should work on reducing the high tariff on communication infrastructure. The Nigerian Ministry of Education should review the current educational curriculum to accommodate the needs of firms in terms of the quality of labour. Finally, the Nigerian government should spend more in providing electricity, transport, education and health infrastructure; but electricity should be given priority attention.

Research on the comparison effect of urban residents' consumption

As an important component of gross domestic product, expanded consumer spending is essential for promoting economic growth. To provide a realistic basis to support effective policy formulation to meet diversified consumption needs and enhance the fundamental role of consumption in economic development, this study constructs a multi-layer panel extended linear expenditure system (ELES) model that includes external habit formation (comparison effect). The study empirically analyses the comparison effect and causes using nested consumption data of urban residents from 31 provinces in China from 2002 to 2019. The results show that the comparison effect is relatively large for urban residents’ subsistence consumption expenditure. In contrast, the comparison effect in development and enjoyment consumption expenditure is smaller overall. Second, a widening income gap increases food and clothing comparison effects. Increased educational attainment reduces comparison effects on housing, health care and transport. Significant geographical differences in the comparison effect on various types of consumer spending are evident based on the degree of regional economic development. Finally, the study proposes policy recommendations of increasing residents’ income, raising educational levels and promoting balanced regional development. The most significant contribution of this study is the innovative multi-layer panel ELES statistical model which incorporates external habit formation theory to systematically analyse comparison effects and causes.

A Dynamic Factor Model for Nowcasting Household Consumption

A Dynamic Factor Model (DFM) is one of the time series models that can be used to forecast within a very short period in the future known as nowcasting. This model can be used to accommodate the frequency difference that exists between monthly explanatory variables and a response variable which is measured quarterly. This model has been commonly used in economics especially to forecast household consumption for the purpose of constructing economic policies. The economic condition of a country can be reflected in the country's Gross Domestic Product (GDP). Consumption is an important component of GDP because of its large proportion of GDP. One of the household economic activities to meet the various needs of goods and services is referred to as household consumption. This paper discusses the DFM to forecast household consumption based on the varimax and quartimax rotations. The results show that both rotational methods can be used for transmitting household consumption with the same precision.

Macroeconomic Indicators and Sukuk Market Performance in Selected OIC Economies

. Sukuk plays a critical role in developing Islamic finance globally. Therefore, it is important to identify macroeconomic determinants that drive the sukuk market. The study investigates the relationship between macroeconomic indicators and sukuk market performance for the period 2004 until 2019 in 10 sukuk issuing countries using a panel data approach. The macroeconomic indicators are Gross Domestic Product (GDP), consumer price index, trade openness, exchange rates, and financial crisis. The findings reveal that GDP, trade openness, and exchange rates have a significant impact on sukuk market performance. In addition, the study analyses the growth components of GDP in these countries five years before the Covid-19 pandemic outbreak. The countries' performance in terms of changes in percentage contribution of consumption, investment, government spending, and net export, towards GDP growth, is measured. The findings indicate that the consumption component contributes the most to economic stability. Therefore, the authorities can implement policies that would support productive consumption and design trade policies that will enhance sukuk market performance.

Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach

The national accounts provide a coherent and exhaustive description of the current state of the economy, but are available at the quarterly frequency and are released with a nonignorable publication lag. The paper illustrates a method for nowcasting and forecasting the sixteen main components of Gross Domestic Product (GDP) by output and expenditure type at the monthly frequency, using a high-dimensional set of monthly economic indicators spanning the space of the common macroeconomic and financial factors. The projection on the common space is carried out by combining the individual nowcasts and forecasts arising from all possible bivariate models of the unobserved monthly GDP component and the observed monthly indicator. We discuss several pooling strategies and we select the one showing the best predictive performance according to a pseudo-real-time forecasting experiment. Monthly GDP can be indirectly estimated by the contemporaneous aggregation of the value added of the different industries and of the expenditure components. This enables the comparative assessment of the indirect nowcasts and forecasts vis-à-vis the direct approach and a growth accounting exercise. Our approach meets the challenges posed by the dimensionality, since it can handle a large number of time series with a complexity that increases linearly with the cross-sectional dimension, while retaining the essential heterogeneity of the information about the macroeconomy. An application to the Italian case leads to several interesting discoveries concerning the time-varying predictive content of the information carried by the monthly indicators.

Are Banks in India Diversified Enough, Geographically, Across States and Economic Sectors?

This article, the first of its kind for the Indian economy, constructs lending environment portfolios of economic activities that banks in India have been faced with, for four Indian banking groups based on the extent of their operations in terms of their deposit shares in each state and the lending-portfolio mix of economic activities in those states. For empirical analysis, data on seven components of gross domestic product and state gross domestic product for 29 states from 1980–1981 to 2016–2017 were taken. The results reveal that the portfolio variances (risk) of the bank groups have declined in the last three decades. Compared to domestic private banks, the State Bank of India group and nationalised banks seem to have significantly reduced their environmental portfolio variability. Simulations to grasp the reasons for this geographic risk reduction show that structural economic reforms introduced in the early 1990s seem to have contributed more than changes in the banking structure in reducing the portfolio risk of banks.

SMALL AND MEDIUM ENTERPRISESAND ECONOMICS GROWTH

Small and medium enterprises play a key role in the economic development of many countries. Precisely for that reason, there is research that examines and analyzes this connection and these effects. Numerous studies on the role and importance of small and medium-sized companies, both in BiH and beyond, highlight and point to the importance and need to strengthen the role of small and medium-sized enterprises. Thus, the authors point out that small and medium enterprises are the drivers of development in most countries, while in BiH there is an entrepreneurial initiative but without significant government policy when it comes to implementing business ideas of entrepreneurs and potential entrepreneurs, despite their success spreading to society.Small and medium enterprises are the subject of numerous researches and analyzes. Among these analyzes, it is often examined how small and medium-sized enterprises (SMEs) affect economic growth expressed in gross domestic product (GDP). Research generally confirms that SMEs boost economic growth and development, reduce unemployment, and contribute to innovation. In this context, the aim of this paper is to examine the relationship between individual microeconomic and macroeconomic components related to small and medium enterprises but also to gross domestic product. In this case, the gross domestic product is observed through GDP per capita, and the research examines the effects of the observed variables on GDP per capita. The period covered by the analysis is from 2004 to 2019. Data are on an annual basis. Theoretical and empirical research seeks to answer the following research question: Which components of SME business increase GDP per capita in BiH? The answer to this research question has been investigated using several scientific methods. The research includes a review of the literature, analyzing the data that examine these relations and which indicate their impact on economic growth and development. Examination of the correlation and influence of the analyzed variables was performed using the method of correlation and regression. The obtained research results show that the only factor that has a positive and statistically significant impact on the GDP per capita growth rate has loans granted to the private sector. Other factors, whether positive or negative, do not have a statistically significant impact on GDP per capita. The number of days required for business registration has a negative effect on GDP growth per capita. As the number of days to register a business decreases, so does GDP per capita. On the other hand, insolvency resolution of companies is positively maintained at GDP per capita. The unemployment rate has a negative effect on GDP per capita, while inflation has a positive one. Investments in RiD also have a positive effect on GDP per capita. Such findings indicate that BiH should focus on these factors if it wants to stimulate growth measured through GDP per capita through small and medium enterprises.

External shocks and output composition: evidence from Nigeria

PurposeThe study investigates the impact of external shocks on output composition (consumption and investment) in Nigeria for the period 1981:Q1– 2018:Q4. Trade-weighted variables from the country's five major trading partners are constructed to capture the impact.Design/methodology/approachThe study employs a block exogeneity open economy structural vector autoregressive (SVAR) analysis in studying the stated relationship.FindingsThe study reveals that external shocks significantly affect consumption and investment in Nigeria. Results from the structural impulse response function suggest that foreign output, real effective exchange rate and foreign interest rate have significant negative effects on consumption and investment. Specifically, results from error variance decomposition show that foreign inflation and real effective exchange rate shocks are major drivers of fluctuations in consumption and investment in Nigeria. Interestingly, the study finds that oil price shock accounts for minor variations in consumption and investment in Nigeria.Research limitations/implicationsThe findings suggest that consumption and investment in Nigeria are substantially and largely driven by external shocks.Practical implicationsThere is need for the monetary authority and the Nigerian government to design appropriate policies to stabilise the naira and salvage the country's exchange rate from unexpected large swings so as to reduce the vulnerability of the economy to external shocks.Originality/valuePrevious studies on external shocks have concentrated on the impact of external shocks on aggregate variables such as output and inflation, while few studies on external shocks in Nigeria capture external shocks through single-country data. This study differs from previous similar studies in Nigeria in two ways. First, the study examines the impact of external shocks on output composition such as consumption and investment. Second, the study captures the impact of external shocks on the two components of gross domestic product (GDP) by constructing trade-weighted variables from Nigeria's five major trading partners.

Gross Domestic Product, Number of Employees and Inflation Rate and Its Relationship to Private Final Consumption in Jordan

Jordan is affected by an increase in the ratio of public debt to a percentage of GDP. The increase rate has reached 85.8% (Jordan’s central bank in numbers). And this is a serious indicator that affects the economic conditions and living standards. The study dealt with the final private consumption and the factors affecting it. The first factor is the gross domestic product (GDP), and it is a reverse view of influence, consumption is one of the components of GDP through spending, which is influential and influenced by it, the second factor was the demographic effect, and the study took the number of workers as part of the demographic factor and the third factor is the level of prices. The study took the rate of inflation. In cases of inflation and high prices, the state resorted to reducing public spending and increasing taxes to reduce actual demand (Hardan, Tahir 1997).The study was from 2006 to 2016 as a sufficient period for measurement and the availability of accurate data. The study found that there is a relationship between the first and second variable and denied the relationship to the third variable, due to price fluctuations in the study period and one of the most important recommendations was to replace local labor with foreign labor.

Nowcasting GDP and its Components in a Data-Rich Environment: The Merits of the Indirect Approach

The national accounts provide a coherent and exaustive description of the current state of the economy, but are available at the quarterly frequency and are released with a nonignorable publication lag. The paper proposes and illustrates a method for nowcasting and forecasting the sixteen main components of Gross Domestic Product (GDP) by output and expenditure type at the monthly frequency, using a high-dimensional set of monthly economic indicators spanning the space of the common macroeconomic and financial factors. The projection on the common space is carried out by combining the individual nowcasts and forecasts arising from all possible bivariate models of the unobserved monthly GDP component and the observed monthly indicator. We discuss several pooling strategies and we select the one showing the best predictive performance according to a pseudo real time forecasting experiment. Monthly GDP can be indirectly estimated by the contemporaneous aggregation of the value added of the different industries and of the expenditure components. This enables the comparative assessment of the indirect nowcasts and forecasts vis-a-vis the direct approach and a growth accounting exercise. Our approach meets the challenges posed by the dimensionality, since it can handle a large number of time series with a complexity that increases linearly with the cross-sectional dimension, while retaining the essential heterogeneity of the information about the macroeconomy. The application to the Italian case leads to several interesting discoveries concerning the time-varying predictive content of the information carried by the monthly indicators.

Forecasting exports across Europe: What are the superior survey indicators?

In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo-out-of-sample analyses reveal that the best performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best performing indicators and thus increasing the accuracy of export forecasts.

EVALUATION OF DIGITAL MARKETING USE IN THE PROMOTION OF LATGALE REGION TOURISM

Destination communication is essential for improving the economic and social development of cities, regions and countries, as tourism has become a major component of gross domestic product in most countries of the world. The development of information technology has fundamentally changed the use of traditional communication tools. Due to the development of modern information technologies, the Internet has become an integral part of tourism communication. Aim of the research is to explore the use of digital marketing in tourism promotion in Latgale region. Research methods - document analysis, statistical data analysis, survey - surveyed tourism information providers in Latgale region; statistical grouping, reading method, interview. The results of the research show that only 20% of Latgale region tourism information providers are actively involved in the use of social media. In the work of tourism information providers more attention is paid to printed materials than to video materials in the digital environment. But the most effective means of digital marketing is that all tourism information providers emphasize the importance of social networks. Digital Marketing Costs has only 40% of the Latgale region tourism information providers and they make between 100 and 3000 euros per year. On Visitlatgale.com and latgale.travel, most of the analyzed digital marketing criteria are not present. Latgale marketing action plan is quite varied and wide where costs are for many exhibitions both in Latvia and abroad, visits of Latvian tour operators and journalists, visits of foreign tour operators and bloggers, training seminars, but the marketing plan for 2019 does not include expenses for digital marketing. Latgale Region Tourism Association Ezerzeme marketing plan necessary to provide expenditure for the development of a communication plan - actively should be used funded projects possibilities, for example costs can be partly covered by the European Union's European Agricultural Fund for Rural Development (EAFRD) and the Rural Development Program. Latgale Region Tourism Association needs to attract project funding for tourism information providers training in digital marketing. More attention should be paid to publishing video materials and attracting appropriate specialists. Latgale Planning Region and Rezekne Municipality should improve tourism websites Latgale.travel and visitlatgale.com.

Forecasting Exports Across Europe: What are the Superior Survey Indicators?

In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo out-of-sample analyses reveal that the best-performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best-performing indicators and thus increasing the accuracy of export forecasts.

Relations of GDP growth and development of SMEs in Poland

Micro, small and medium enterprises play a very important role in economic development of a country, what is the reason that they are subject to so many studies and analyzes. In addition to contributing to the creation of the Gross Domestic Product, they also play a key social role as they reduce unemployment.The aim of the article is to evaluate the relations between the components of gross domestic product (GDP) and the development of small and medium-sized enterprises (SMEs) in Poland in the years 1996-2016. To achieve this goal, we conducted a literature review, presented statistical data confirming the importance of these enterprises in the development of the economy, such as: entrepreneurship indicator (calculated as the number of SME enterprises per 1000 inhabitants), SME participation in total employment and SME participation in generating value added. We have presented this data for selected EU countries. In the further part of the work, we conduct a statistical analysis of data collected for Polish SMEs, which allowed us to verify the hypotheses set in the article. The results are presented at the end of the article.

Seasonal adjustment subject to accounting constraints

The indirect seasonal adjustment obtained by aggregating component seasonal adjustments may be inadequate, whereas the direct adjustment of the aggregate can typically be ensured to be adequate by adjusting the statistical model. Reconciliation techniques can be used to allocate the discrepancies between the direct and indirect adjustments of the aggregate unto the various component series, essentially enforcing that the indirect procedure yields the same outcome as the adequate direct procedure. This paper proposes utilizing adequacy of the component seasonal adjustments—given the modifications entailed by reconciliation—as an additional constraint to the accounting problem. We focus on seasonal adjustments arising from X‐13ARIMA‐SEATS and apply this constrained reconciliation procedure to copper imports, a component of gross domestic product.

Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China

The speeding-up of economic development and industrialization processes in China have brought about serious atmospheric pollution issues, especially in terms of particulate matter harmful to health. However, impact mechanisms of socio-economic forces on PM2.5 (the particle matter with diameter less than 2.5 μm) have rarely been further investigated. This paper selected GDP (gross domestic product) per capita, energy consumption, urbanization process, industrialization structure, and the amount of possession of civil vehicles as the significant factors, and researched the relationship between these factors and PM2.5 concentrations from 1998 to 2016, employing auto-regressive distributed lag (ARDL) methodology and environmental Kuznets curve (EKC) theory. Empirical results illustrated that a long-term equilibrium nexus exists among these variables. Granger causality results indicate that bi-directional causality exist between PM2.5 concentrations and GDP per capita, the squared component of GDP per capita, energy consumption and urbanization process. An inverse U-shape nexus exists between PM2.5 concentrations and GDP per capita. When the real GDP per capita reaches 5942.44 dollars, PM2.5 concentrations achieve the peak. Results indicate that Chinese governments should explore a novel pathway to resolve the close relationship between socio-economic factors and PM2.5, such as accelerating the adjustment of economic development mode, converting the critical industrial development driving forces, and adjusting the economic structure.