• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.2
• /
• pp.295-303
• /
• 2022

Information and communication technology (ICT) is one of the primary zones that stimulates economic development in today's globalized world. It promotes technological developments in worldwide communication and manufacturing systems, as well as economic growth and development. Many economic activities, such as international trade and foreign direct investment, rely heavily on contemporary information and communications technologies (FDI). The goal of this study is to look at the dynamic relationship between FDI, ICT, trade openness, and economic growth in the context of BRICS countries from 2000 to 2018, with Gross Domestic Product as the dependent variable and Telephone subscriptions, Mobile subscriptions, Broadband subscriptions, Internet subscribers, Secure internet servers, Trade, and Foreign direct investment as the independent variables.Two variables are used as proxies to manage the macroeconomic environment, while five variables are used as proxies for ICT infrastructures. The outcomes of this study are analyzed using Generalized Methods of Movements (GMM). According to this study, ICT has a positive impact on the economic growth of a few countries. Trade openness and foreign direct investment, on the other hand, have a negative impact on economic growth. As growing countries, the BRICS must participate in economic reform and liberalization measures. This report suggests policy proposals for improving ICT standards, focusing especially on economic growth, trade openness, and increasing foreign investment in the BRICS countries.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.2
• /
• pp.141-149
• /
• 2022

Despite the importance of the insurance business for financial and economic development, few studies have looked at the factors that influence its growth. This research adds to the body of knowledge by empirically examining the impact of numerous factors on the development of the insurance business in 15 Middle East and North African (MENA) countries from 2000 to 2017. The study looks at macroeconomic, demographic, and institutional factors as potential drivers of the insurance industry's growth, with the insurance premium as a percentage of GDP as the dependent variable. All variables are stationary at the first difference, according to the IPS panel unit root test. The Pedroni residual cointegration test, Kao residual cointegration test, and Johansen-Fisher panel cointegration tests are then used to look for long-run associations. The cointegration tests strongly suggest that the insurance premium and the various variables have long-run correlations. Findings from the Fully-Modified OLS imply that GDP per capita, gross capital formation, and the KOF economic globalization index have a positive long-term impact on the insurance business. The insurance business is also driven by combating corruption and the rule of law. The population and regulatory quality, on the other hand, have no significant impact.

• KDI Journal of Economic Policy
• /
• v.31 no.2
• /
• pp.1-13
• /
• 2009

The monetary model suggests that nominal exchange rates between two countries will be determined by important macroeconomic variables. The existence of a cointegrating relationship among these fundamental variables is the backbone of the monetary model. In a recent paper, Rapach and Wohar (2002, Journal of International Economics) advance the literature by testing for linear cointegration in the monetary model using a century of data to increase power. They find evidence of cointegration in five or six of ten countries. We extend their work to the nonlinear framework by performing threshold cointegration tests that allow for asymmetric adjustments in two regimes. Asymmetric adjustments in exchange rates can occur, for example, if transactions costs are present or if policy makers react asymmetrically to changing fundamentals. Moreover, whereas Rapach and Wohar (2002) found it necessary to exclude the relative output variable in some cases to maintain the validity of their cointegration tests, we can include this variable as a stationary covariate to increase power. Overall, using their same long-span data, we find more support for cointegration in a nonlinear framework.

• International Area Studies Review
• /
• v.15 no.2
• /
• pp.247-273
• /
• 2011

This study analyzes the impacts on the M&A and greenfield of macroeconomic variables of home and host countries, after identifying current status and characteristics of the M&A and greenfield related to the entering way of Korean firms in China. Main empirical results are summarize as follows. First, as for foreign exchange variable, the decreased value of Korea won shows the negative correlations with both of the greenfield and M&A. Second, the real interest rate of Korea to measure the cost of capital is not significant statistically. Third, while the host country's stock market index, Shanghai Comprehensive Index, shows the expected negative correlations with the investment in the case of small & medium firm and light industry, it shows the positive correlations which is not consistent with general expectation in the case of large firm and heavy industry. Fourth, the openness of host country shows the positive correlations with both of the greenfield and M&A. Finally, in regard to the M&A, China's GDP to measure the market size of host country is not significant statistically while it shows the strong positive relationship with the greenfield investment.

• International Commerce and Information Review
• /
• v.14 no.4
• /
• pp.27-47
• /
• 2012

The study aims to analyze how the variables for Korea, such as the exports, imports, FDI(Inward) and FDI(Outward), influence the economic growth and how they affect each other. For the purpose of empirical analysis, this paper used the quarterly time series data from 1980 to 2010, dividing the period before and after 1997(IMF). The variables used in this study were log-transformation from the original variables. This study empirically tests the relationship among variables by using VECM with considering the time-series properties of each variable. The results found from the study are as followings. Causality analysis using VECM proved that no causality between GDP and exports existed, whereas causality between GDP and FDI(Inward) existed, in which GDP affected FDI(Inward) since IMF. However, it was found that other periods and FDI(Inward) did not affect GDF and had no causality among them.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.7
• /
• pp.55-66
• /
• 2021

The purpose of this study is to analyze the relationship and effects of variables both directly and indirectly (e.g., investment (INV), government expenditure (GE), unemployment rate (UR), economic growth (EG), and income inequality). The analytical phases consist, first, to transform the data using the Log Natural (Ln) method. Second, to check normality and multicollinearity of data. Third, to test direct effects of variables (government expenditure and investment effect on the unemployment rate and economic growth; investment on government expenditure; economic growth on unemployment rate; economic growth and unemployment rate on income inequality). Fourth, to test indirect effects using Sobel test, which involves UR and EG as intervening variable. Fifth, to test hypotheses with p-value < 0.05. The results of the study reveal that, of the 12 relationships, statistics show that 11 variations of the association have significant positive and negative effects. Theoretically, the different characters and goals of GE and INV in each country will have a different impact on EG and UR goals. The study provides an input, especially for the government. To create optimal EG through GE and INV, it is necessary to allocate budgets to industrial sectors that can absorb a massive labor force and to new economic growth sectors.

• Journal of Navigation and Port Research
• /
• v.46 no.4
• /
• pp.385-391
• /
• 2022

In this study, a multivariate time series analysis was conducted to identify various variables that impact ocean freight rates in addition to supply and demand factors. First, we used the ClarkSea Index, Clarksons Average Bulker Earnings, and Clarksons Average Tanker Earnings provided by the Shipping Intelligence as substitute variables for the dependent variable, ocean freight. The following ndependent variables were selected: World Seaborne Trade, World Fleet, Brent Crude Oil Price, World GDP Growth Rate, Industrial Production (IP OECD) Growth Rate, Interest Rate (US$ LIBOR 6 Months), and Inflation (CP I OECD) through previous studies. The time series data comprise annual data (1992-2020), and a regression analysis was conducted. Results of the regression analysis show that the World Seaborne Trade and Brent Crude Oil P rice impacted the ClarkSea Index. Only the World Seaborne Dry Bulk Trade impacted the Clarksons Average Bulker Earnings, World Seaborne Oil Trade, Brent Crude Oil Price, IP, and CP I on the Clarksons Average Tanker Earnings.

• The Korean Journal of Applied Statistics
• /
• v.37 no.1
• /
• pp.13-38
• /
• 2024

As economic uncertainties have increased recently due to COVID-19, there is a growing need to quickly grasp private consumption trends that directly reflect the economic situation of private economic entities. This study proposes a method of estimating private consumption in real-time by comprehensively utilizing big data as well as existing macroeconomic indicators. In particular, it is intended to improve the accuracy of private consumption estimation by comparing and analyzing various machine learning methods that are capable of fitting ultra-high-dimensional big data. As a result of the empirical analysis, it has been demonstrated that when the number of covariates including big data is large, variables can be selected in advance and used for model fit to improve private consumption prediction performance. In addition, as the inclusion of big data greatly improves the predictive performance of private consumption after COVID-19, the benefit of big data that reflects new information in a timely manner has been shown to increase when economic uncertainty is high.

• The Journal of the Korea Contents Association
• /
• v.22 no.1
• /
• pp.214-223
• /
• 2022

The purpose of this study is to present a new industrial land demand prediction method that can consider external economic factors. The analysis model used ARIMA-X, which can consider exogenous variables. Exogenous variables are composed of macroeconomic variable, Business Survey Index, and Composite Economic Index variables to reflect the economic and industrial structure. And, among the exogenous variables, only variables that precede the supply of industrial land are used for prediction. Variables with precedence in the supply of industrial land were found to be import, private and government consumption expenditure, total capital formation, economic sentiment index, producer's shipment index, machinery for domestic demand and composite leading index. As a result of estimating the ARIMA-X model using these variables, the ARIMA-X(1,1,0) model including only the import was found to be statistically significant. The industrial land demand forecast predicted the industrial land from 2021 to 2030 by reflecting the scenario of change in import. As a result, the future demand for industrial land was predicted to increase by 1.91% annually to 1,030.79 km². As a result of comparing these results with the existing exponential smoothing method, the results of this study were found to be more suitable than the existing models. It is expected to b available as a new industrial land forecasting model.

• Analyses & Alternatives
• /
• v.6 no.2
• /
• pp.155-183
• /
• 2022

The purpose of this study is to econometrically analyze the negative impact of the May 18 Democratic Uprising on the Gwangju/Jeonnam regionional economy using the Synthetic Control Method (SCM). The SCM SCM is a methodology similar to the difference-in-difference(DID) method of microeconometrics. It is applied to macroeconomic variables such as country, region, etc. to estimate the causal relationship between specific events and the dependent variable. In this study, as of 1980, local tax revenue data of metropolitan local governments were used as a proxy variable for the economy of the region, and the impact of the May 18 Democratic Uprising on the economy of Gwangju/Jeonnam region was analyzed through various socio-economic indicators. In this study, data were used to analyze from 1971 to 2000, and as a result of empirical analysis, local tax revenues in Gwangju/Jeonnam area were less collected than normal routes up to 17%. In addition, the significance of this analysis was confirmed through in-time placebo effect analysis and in-space placebo effect analysis, which are methods of analyzing the robustness of the control group synthesis method.