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

• Information Systems Review
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• v.20 no.2
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• pp.63-85
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• 2018

Emerging hotspot and trendy areas are formed into alleys and blocks with the help of viral effects among social network services (SNS) users called "Golmogleo." These users search for every corner of the alleys to share and promote their own favorite places through SNS. An analysis of hot places is limited if it is only based on macroeconomic indicators such as commercial area data published by national organizations, large-scale visiting facilities, and commuter figures. Careful analyses based on consumers' actual activities are needed. This study develops a "social big data analysis methodology" using Instagram data, which is one of the most popular SNSs suitable to identify recent consumer trends. We build a spatial analysis model using Local Moran's I. Results show that our model identifies new trend zones on the basis of posting data in Instagram, which are not included in the commercial information prepared by national organizations. The proposed analysis methodology enables better identification of the latest trend areas formulated by SNS user activities. It also provides practical information for start-ups, small business owners, and alley merchants for marketing purposes. This analytical methodology can be applied to future studies on social big data analysis.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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• pp.109-121
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• 2009

In these OECD countries, left-wingers Government focus on unemployment, but right -wingers Government cares more about inflation. It is that inflation and unemployment don't have differential effects across rich and poor and the happiness levels of these two groups are unaffected by identity of the Government in power. The poor people choose to left-wingers Government, but rich people prefer to right -wingers Government. I estimate whether above opinion is correct or not. Especially I check how my results change when I control for aggregate economy activity and government consumption, two variables that could be correlated with inflation and unemployment and affect each Government's happiness differentially. This paper, and I believe much of the happiness literature, can be understood as an application of experienced utility, a conception that emphasis the pleasures derived from private consumption and sentiment of it. In Granger Causality test, private consumption sentiment index related with industrial production interactively in Korea. The business cycles affect on private consumption sentiment index.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.1-17
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• 2013

The main purpose of this paper is to examine the aid effectiveness of ODA projects through a case of an e-Government ODA. The case at hand is the Mongolian National Data Center project, which was supported by Korea International Cooperation Agency in 2007~2009. In this paper, we analyze aid effectiveness through a case instead of a long-term macroeconomic analysis. By using OECD DAC guideline for performance evaluation of ODA project combined with PRM (Performance Reference Model) for evaluating e-Government, authors undertakes a post-implementation evaluation on the Mongolian NDC project. The result shows that the NDC project has been relevant and implemented effectively and efficiently given the political instability incurred by the election fraud in 2008. However, there have been problems as well. Provision of defected monitors was a critical problem while disagreement on the safety of the building was lasted for two more years to be settled. Some comments on the training program are also worth to hear to ameliorate similar projects. But more fundamental problems are found in impacts and sustainability areas. Low political status of ICTPA as a coordinating organization for ICT ODA and unstable job security of NDC are serious hurdles to increase effectiveness and efficiency of NDC. ICTPA has played very limited roles in coordinating government-wide ICT ODA projects because it lacks actual power and control over them. Other government organizations do not fully trust NDC as a reliable government data center due to low political status and lack of job security. Thus critical factors to affect the effectiveness and efficiency of e-Government ODA projects are political and organizational instead of economic.

• The Journal of Bigdata
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• v.2 no.1
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• pp.59-69
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• 2017

According to precedent research of disaster economics, most of the studies are either based on belated macroeconomic indicators or are limited to specific industries. It is certain that preventing disaster is important, but immediate analysis and reconstruction policy are crucial as well. This research analyzed the ripple effect of consumer spending followed by April 16 ferry disaster and MERS outbreak; it was done by applying credit card company's real-time big data with Marketing Mix Modeling. The main focus of this research is to see if it is possible to predict the scale of damage during ongoing disasters. It is found that setting up weekly MMM and moving the timeline draws significance conclusion. When disasters or events occur in future, this research may be the basis of building quick and intuitive indicator to monitor possible effects.

• KDI Journal of Economic Policy
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• v.34 no.1
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• pp.29-52
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• 2012

During the global financial turmoil in 2007-2008, deviation from the covered interest parity (CIP) between the Korean won and US dollar through the foreign exchange swap has escalated in its magnitude beyond 1,000bp in November 2008, and it still persists around 100bp level. In this paper, we examine a newly developed margin based asset pricing model using Kalman filter approach and show that the escalation of the CIP deviation is found to be significantly related to the global dollar funding illiquidity and country-specific funding conditions. Furthermore, we find evidence that the poor funding conditions (or higher margins) are driven by the general money market illiquidity and may lead to higher funding illiquidity, which suggests the reinforcing effects of the liquidity spiral. We also show that the supply of dollar liquidity and improved funding conditions help alleviate the deviations from the parity, however the persistent anomaly is found to be related to the high level of volatility in the FX swap market.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.141-154
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• 2018

In order to meet the ever-increasing demand for international air travel, several plans are underway to open new airports and expand existing provincial airports. However, existing air demand forecasts have been based on the total air demand in Korea or the air demand among major cities. There is not much forecast of regional air demand considering local characteristics. In this study, the outbound air travel demand in the southeastern region of Korea was analyzed and the fixed-effects model using panel data was proposed as an optimal model that can reflect the inherent characteristics of metropolitan areas which are difficult to observe in reality. The results of model validation show that panel data analysis effectively addresses the spurious regression and unobserved heterogeneity that are difficult to handle in a model using only a few macroeconomic indicators with time series characteristics. Various statistical validation and conformance tests suggest that the fixed-effects model proposed in this study is superior to other econometric models in predicting demand for international demand in the southeastern region.

• Asian Journal of Innovation and Policy
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• v.9 no.2
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• pp.187-206
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• 2020

Under the on-going evolution of the COVID-19 pandemic, estimating the economic impact of the pandemic is highly uncertain and challenging. This situation makes it difficult for policymakers, governors, and economic entities to formulate appropriate responses and decision makings. To provide useful information about the effect of the COVID-19 pandemic on the Korean economy, this study examined macroeconomic impact analysis stemming from the pandemic shocks with different scenarios for the Korean economy. Based on three scenarios using the growth rate of 2020 GDP and consumer expenditure patterns, the 2021 GDP by industry sector was forecast with two new approaches. First, the recovering process of the Korean economy from the shock was analyzed by applying a Flex-IO method. Second, a new forecasting approach combined with an IO coefficient matrix was applied to forecast the future GDP changes. The findings of this study are summarized as follows: First, the total GDP growth rate under the Pessimistic Scenario demonstrates less rebound from the shock than that of the Base Scenario. Second, agriculture, culture, and tourism-related sectors that are suffering from the severe losses of COVID-19 showed lower resilience than other different industries. Third, information and communications technology (ICT) industry maintains a stable growth trend and is expected to take the leading role for the Korean economy in the post-COVID-19 and the Industry 4.0 eras. The findings deliver that it needs to analyze how government expenditure responding the shock into the forecasting model, which can be more useful and reliable to simulate the resilience from the pandemic.