• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.222-233
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• 2010

This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

• ETRI Journal
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• v.10 no.1
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• pp.39-52
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• 1988

This study suggests the diffusion models to predict the spread pattern of telecommunications services. The extended models containing both (either) price and (or) income varible are offered on the basis of Bass model. At the empirical test using Korean telephone data, the models with either price or income varible are the best forecasting model under apriori selected econometric criteria.

• Journal of Korea Trade
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• v.25 no.3
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• pp.116-133
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• 2021

Purpose - Intra-industry trade of agricultural products can eliminate the disadvantage of Korea's traditional agriculture and improve its lack of comparative advantage. The main purpose of this paper is to measure the level and index of intra-industry trade of Korean agricultural products and to explore the spatial dependence and spillover effect associated with this type of trade. The main factors influencing intra-agricultural trade are analyzed from two perspectives: the population and the classification of agricultural products. Design/methodology - First, the level of intra-industry trade of Korean agricultural products is measured. Second, to obtain a more accurate estimate of the influence of various factors, and based on two types of weight matrices, a spatial econometric model is constructed from two aspects: population and classification of agricultural products. The status and the factors influencing intra-industry trade are also studied. Findings - It is concluded that there is a positive spatial correlation between Korea's intra-industry trade in agricultural products and that of its trading partners. The spatial spillover effect of this type of trade is verified by using the spatial autoregressive model (SAR). Labor-intensive agricultural products are found to have a positive spillover effect on intra-industry trade, while land-intensive products do not have a significant effect. Originality/value - In this paper, the two types of agricultural products are meticulously distinguished, and the spatial effect of the intra-industry trade of agricultural products as well as the influence of various factors are analyzed. In addition, the accuracy of the estimation of the coefficients of the factors by using the spatial econometric model is higher than that of the ordinary panel data model.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.9-12
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• 2019

With the Chinese government's attention to the artificial intelligence industry, the Chinese government has invested a lot in it recently. Of course, the importance of artificial intelligence industry for China's economic development is increasingly significant. The advent of artificial intelligence boom has also triggered a large number of scientists to analyze the impact of artificial intelligence on economic growth. Therefore, this paper use 31 China's cross-province panel data to study the effect of artificial intelligence on economic growth. Via empirical analyses under a series of econometric methods such as the province and year fixed effect model, the empirical result shows that artificial intelligence has a positive and significant effect on economic growth. Namely, the artificial intelligence is a new engine for economic growth. Meanwhile, the empirical results also indicate that the investment and consumption has a significant and positive effect on economic growth. Oppositely, the inflation and government purchase have a significant negative effect on economic growth. These findings in this paper also provide some important evidences for policy-makers to perform precise behaviors so as to promote the economic growth. Moreover, these finding enriches existing literature on artificial intelligence and economic growth.

• Journal of Distribution Science
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• v.17 no.1
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• pp.21-31
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• 2019

Purpose - As the market economy deepens, the issue of social equity caused by income distribution becomes more and more significant. Therefore, this paper attempts to exploit the determinants of income distribution in terms of macroeconomics. Research design, data, and methodology - The data set from 1990 to 2017 will be used to conduct an empirical analysis under a menu of econometric approaches such as vector autoregressive model and impulse response function. The income distribution and other macroeconomic variables such as foreign direct investment and employment will be used to conduct an empirical analysis to explore the determinants of income distribution in terms of macroeconomics. Results - The findings indicate that the income distribution is related with macroeconomics. More specifically, the export, import, GDP and foreign direct investment play a role in deteriorating the income distribution. Conversely, the industrialization, inflation and employment can improve the income distribution. Unfortunately, the inflation and employment do not get through under 5% significant test. Conclusions - Due to that a good income distribution can be beneficial for both a country and an individual, this paper provides a new scope for China's government to improve its income distribution in terms of macroeconomics.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.731-740
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• 2021

This research aims to investigate the determinants of real effective exchange rate in emerging ASEAN countries, including Indonesia, Malaysia, Philippines, Thailand, and Vietnam. The research was conducted by using quarterly time series data set from 1980Q1 to 2020Q3. Cointegration and the error correction model (ECM) methods were applied to test the long run and short run relationship of the real effective exchange rate and its determinants. The results indicate that the ratio of foreign direct investment to GDP and the government spending have significantly positive impact on real effective exchange rate in the Emerging ASEAN countries. The trade opening had influencing real effective exchange rate in most the Emerging ASEAN countries, except Vietnam. In addition, the international reserve (INR) had significant long-run impacts variables on real effective exchange rate in Malaysia, Thailand and Vietnam. In the short run equilibrium, the error collection term suggest that Indonesia and Malaysia are the fastest speed adjustment to equilibrium. In addition, the term of trade influence the real effective exchange rate in Indonesia, Malaysia, and the Philippines but it is not in Thailand and Vietnam. However, FDI is a major factor of the real effective exchange rate in Vietnam, but not for other countries.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.95-106
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• 2016

Air transportation demand is consistently increasing in Korea due to economic growth and low cost carriers. For this reason, airport expansion plans are being discussed in Korea. Therefore, it is essential to forecast reliable air transportation demand with adequate methods. However, most of the air transportation demand models in Korea has been developed by simple regression analysis with several dummy variables. Simple regression analysis without considering stationarity of time-series data can bring spurious outputs when a direct causal relationship between explanatory variables and dependent variable does not exist. In this paper, econometric model were developed for air transportation demand based on stationarity in time-series data. Unit root test and co-integration test are used for testing hypothesis of stationarity.

• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.430-446
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• 2018

In this paper, we conducted a comparative study on the methodology for impact analysis as the economic grounds for formulating policy and investment plan concerned with utilizing spectrum resource. In order to provide numerical results for objective comparison and selection among policy and investment planning, methods to be analyzed are focused on quantitative methodology based on mathematical models, consequently the utility and limits of econometric model, input-output analysis, computable general equilibrium and system dynamics are compared from various viewpoints including analysis cost. Besides, we compared the methodologies in the standpoint of utilizing spectrum and discussed the recent findings of mixed models combining multiple methodologies to exploit the advantages of each methodology and to offset the limit. Results of the research can be used as reference indicators to select the method that conforms to the purpose and priority of analysis verifying the efficiency of execution of policies and investment plans.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.