• International Area Studies Review
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• v.21 no.4
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• pp.159-172
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• 2017

In a typical trend-cycle decomposition of GDP, the trend component is usually assumed to follow a random walk process. This paper considers an ARIMA trend and assesses the validity of the ARIMA trend model. I construct univariate and bivariate unobserved-components(UC) models, allowing the ARIMA trend. Estimation results using U.S. data are favorable to the ARIMA trend models. I, also, compare the forecasting performance of the UC models. Dynamic pseudo-out-of-sample forecasting exercises are implemented with recursive estimations. I find that the bivariate model outperforms the univariate model, the smoothed estimates of trend and cycle components deliver smaller forecasting errors compared to the filtered estimates, and, most importantly, allowing for the ARIMA trend can lead to statistically significant gains in forecast accuracy, providing support for the ARIMA trend model. It is worthy of notice that trend shocks play the main source of the output fluctuation if the ARIMA trend is allowed in the UC model.

• Korean Journal of Labor Studies
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• v.23 no.2
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• pp.47-77
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• 2017

This paper analyzes the changes in wage inequality and its contributing factors since the mid-2000s. Although trends vary by data and wage indices, the Gini coefficient of the total wage of all workers shows an increasing trend due to the part-time increase of less than 35 hours per week, while the wage Gini coefficient of hourly wages and the total wage Gini coefficient of full-time workers showed a declining trend. Part-time increases have increased inequality based on total wages, but part-time hourly wage increases can be considered to have reduced hourly wage inequality. Therefore, as a result of decomposing the factor of Gini coefficient reduction only for full-time workers, factors that contributed absolutely to inequality reduction were variables such as job tenure, career, and occupation, and employment type variable has little effects, and the establishment size variable deepens inequality. The variables such as industry, age, and education did not contribute significantly to the inequality change. This is attributed to the decline in wage premiums for job tenure and management and professional jobs and the increase in wage premiums for large-scale businesses.

• Journal of Urban Science
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• v.9 no.2
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• pp.21-30
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• 2020

The purpose of this study is to present a plan to distinguish 229 local governments nationwide by taking into account various characteristics such as population, employment, housing, and industry of the region for customized urban policies in the era of metropolitan areas. The National Statistical Portal (KOSIS) collected and standardized data related to population, housing, industry, and finance by region from 2000 to 2015 for the classification of regional types necessary for customized urban policies, and this was used to classify them into regional types that considered population, employment, housing and industry. The summary of the analysis results is as follows. First, as a result of the regional type classification, 10 key employment sites (4.4%), 5 employment centers (2.2%), 38 residential centers (16.6%), 20 growth areas (8.7%), 26 industrial cities (11.4%), 35 low-fertile farming and fishing villages (15.3%) and 95 stagnant areas (41.5%). Second, the Seoul metropolitan area is the most diverse type of metropolitan area in the country, with most of its core employment sites inside Seoul, residential centers inside and outside Seoul, and growth areas in the southeastern part of the country (Busan, Ulsan, and Gyeongsangnam-do) are mixed with industrial and growth areas centered around Busan, Ulsan and surrounding areas, while the rest of the local governments are found to be low-fertile farming villages or stagnant areas. Daegu (Daegu, Gyeongbuk) is an industrial city in Daegu, and the rest of the local governments are either low-density farming and fishing villages or stagnant areas. The Honam region (Gwangju and Jeolla) was found to be a low-mill farming and fishing village or stagnant area except for Gwangju, while the Chungcheong region (Daejeon, Sejong, and Chungcheong) was seen as a growth area with areas adjacent to Daejeon, Sejong, and the Seoul metropolitan area, and some industrial cities were included. Finally, the Gangwon area was mostly classified as low-density farming and fishing villages and stagnant areas.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• The Journal of Information Systems
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• v.30 no.2
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• pp.147-163
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• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.375-389
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• 2021

Recent years have seen a rapid boom of the 4th industry and relevant policies in regions. However there are only a few studies about the impact of the 4th industry on the local economy. This study examines the agglomeration effects of the 4th industry on regional economy by using a spatial statistical models. As a result, it was found that the agglomeration of the 4th industry had a positive effect on the productivity of the local economy, while there is not good enough evidence to prove the relationship between the 4th industry and the income of the region. These findings indicate that the impact of the agglomeration of the fourth industry on the local economy is limited. In addition, the impact on the local economy was different by the type of the fourth industry, and the manufacturing industry and financial and insurance industries had a positive impact on the growth of the local economy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.146-159
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• 2021

vehicle simulator as part of an empirical analysis the driving characteristics of elderly drivers. To this end, the driving characteristics of the elderly driver from previous study review. he driving characteristics of the elderly the driving elderly driver and general driverIn summarizing these experimental results, the -test showed different driving characteristics from general drivers in all items except for one side of the lane, such as driving speed and driving operation (brake, throttle, steering operation) at a significance level of 95%. Second, when changing lanes, it was difficult for elderly driver to maintain speed and secure an appropriate distance between carslderly driver changed lanes even in inappropriate situations (short distances between cars). Third, in unexpected situation, elderly drivers needed more distance and time.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.434-446
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• 2021

Recently, there has been a growing interest in the fourth industrial revolution, expected to have a significant impact on society. However, there are only a few studies on spatial distribution and location of the fourth industry. This study tried to classify the spatial distribution of the fourth industry by using LQ, a non-geographical method, and Getis-Ord's Gi^*, a geographical method. The results of the analysis are as follows. First, there are the specialized areas of the fourth industry in the non-Seoul metropolitan area as well as the Seoul metropolitan area. Second, industrial clusters and neighborhood areas of the fourth industry were located mostly in the Seoul metropolitan area. Third, industrial clusters were concentrated on the southern part of Gyeonggi Province and Seoul, and there are no industrial clusters in the northern part of the Seoul metropolitan area and the nature conservation area. This paper shows that the cluster area of the fourth industry is concentrated in the Seoul metropolitan area. Therefore, policies for the 4th industry are needed to solve this unbalanced spatial distribution of the fourth industry.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.791-797
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• 2022

Through recent metaverse marketing case studies, success factors and activation methods were analyzed from the perspective of content, platform, network, and device of the metaverse ecosystem in each industry. The importance of contents and platform of metaverse could be found in entertainment, fashion, office space and real estate, education, advertisement and commerce industries. In order to vitalize the metaverse, firstly, it is necessary to strengthen active participation and retention by providing a stable revenue model for market participants. Secondly, the importance of attractive content to expand subscribers is a key trigger for metaverse activation. Thirdly, it is necessary to increase the convenience of using metaverse service by using a light and simple device for the user. Fourthly, a win-win cooperation strategy should be supported in the value chain of the industry through ecosystem scalability. In addition, business opportunities for market participants and additional revenue models should be continuously provided.