• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7820-7830
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• 2015

Korean households' demand for food consumed away from home is on the steady increase. The ratio of eating-out expenditure of the household income, however, tends to decrease recently irrespective of income groups. This paper, therefore, aims to analyse the food-away-from-home expenditures of salary and wage earners' households by income decile group. The eating-out expenditure is modelled as a function of household income and then estimated using econometric methods such as regression, rolling regression, impulse response, and variance decomposition of forecast error. The regression results indicate that the higher the income decile group is, the lower the income elasticity of eating-out expenditure is, and the high income groups enjoy seasonal eating-out, the low groups do not. The coefficients of dynamic rolling regression are much smaller than those of static one, meaning that households tend to decrease the eating-out expenditure of their income. The impulse response analysis suggests that the eating-out expenditure increase of higher income groups lasts long relative to that of lower income groups. The variance decomposition, also, shows that household income plays much more important role in determining eating-out expenditure at the higher income groups than at the lower income groups.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.119-128
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• 2009

The construction R&D(Research & Development) has various performance according to research charater. But present performance evaluation form evaluates project rather than charater of research. The important reasons that evaluats the performance are to try conclusions with evaluated objects in the short run, but to forecast the performance of future construction R&D and to get the better performance. Thus this study extracts the RPI(Research Performanc Index) with output as the conter, developes the estimation method and measures the quantitative performance. Applying the ANOVA(Analysis of Variance), it is proved that the performance according to research charater is various. And applying the Correspondence Analysis, it is analised the relationship with performance and research charater. The purposes of this study are to idetify the problem of uniform performance evaluation and to improve it.

• Information Systems Review
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• v.5 no.1
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• pp.129-146
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• 2003

Increasing number of transactions and investments in technology has sparked a growing interest in technology valuation. However, it has not been easy to come up with an objective valuation of technology due to variance in technology value and specialty of technology valuation. The main objective of this paper lies in the development of a new system for technology valuation, Web-based Interactive Technology Valuation (WITV) system, which valuate the technology and analyze the technology value strategy. WITV system uses the Technology Valuation Attractiveness Model (TVAM). TVA is composed of the Intrinsic Value of Technology (IVT) and the Extrinsic Value of Technology (EVT). This paper experiment the feasibility of the TVA Model and WITV System by conducting an empirical study on small & medium sized manufacturing companies in IT industry, registered on KOSDAQ. In this study, the potential value is defined as the technology value. It is represents the expected profit appraised by the market under the competitiveness of technology and the growth of the market. TVA is measured as the index to forecast the Price-to-Book value Ratio (PBR), which is the proxy variable for the potential value of the technology. The results identify the feasibility of the TVAM through a high correlation between the TVA and the PBR.

• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.19-25
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• 2014

Demand forecasting is the basis of management activities including marketing strategy. Especially, the demand of a part is remarkably important in supply chain management (SCM). In the fields of various industries, the part demand usually has the intermittent characteristic. The intermittent characteristic implies a phenomenon that there frequently occurs zero demands. In the intermittent demands, non-zero demands have large variance and their appearances also have stochastic nature. Accordingly, in the intermittent demand forecasting, it is inappropriate to apply the traditional time series models and/or cause-effect methods such as linear regression; they cannot describe the behaviors of intermittent demand. Markov bootstrap method was developed to forecast the intermittent demand. It assumes that first-order autocorrelation and independence of lead time demands. To release the assumption of independent lead time demands, this paper proposes a modified bootstrap method. The method produces the pseudo data having the characteristics of historical data approximately. A numerical example for real data will be provided as a case study.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.153-162
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• 2012

We proposed a mixed-mode design with a landline survey and mobile survey as the solution for the problems of election opinion polls by the original telephone survey method, mostly with limited population coverage for young people not living at home and with lower efficiency in selecting valid voters. We numerically verified the applicability of the proposed dual frame survey by analyzing the preliminary opinion poll results of the Seoul mayor by-election of October 26 2011. This research achieved the result that relative standard errors were similar between a mobile RDD sample and landline RDD sample though the variance was bigger in the former. Though the combination of mobile RDD and landline RDD is not found to improve the forecast accuracy, it still is expected to have higher reliability for election polls by expanding the population coverage and compensating the weakness of each survey method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.6
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• pp.319-331
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• 2016

This study explores the signal characteristics for different averaging intervals and defines representative verticies for each observatory by criterion of percent rate and variance. The shorter averaging interval shows the higher frequency variation, though the lower percent rate. In the tidal currents, we could hardly find the differences between 60-minute and 20-minute averaging. The newly defined criterion improves reliability of HF-radar data compared with the present reference which deselects the half by percent rate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.189-203
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• 2020

Purpose - This study aims in analyzing the dynamic relationship between household loans and housing prices according to the characteristics of depository institutions after the financial crisis, identifying the recent trends between them, and making policy suggestions for stabilizing house prices. Design/methodology/approach - The monthly data used in this study are household loans, household loan interest rates, and housing prices ranging from January 2012 to May 2020, and came from ECOS of the Bank of Korea and Liiv-on of Kookmin Bank. This study used vector auto-regression, generalized impulse response function, and forecast error variance decomposition with the data so as to yield analysis results. Findings - The analysis of this study no more shows that the household loan interest rates in both deposit banks and non-bank deposit institutions had statistically significant effects on housing prices. Also, unlike the previous studies, there was statistically significant bi-directional causality between housing prices and household loans in neither deposit banks nor non-bank deposit institutions. Rather, it was found that there is a unidirectional causality from housing prices to household loans in deposit banks, which is considered that housing prices have one-sided effects on household loans due to the overheated housing market after the financial crisis. Research implications or Originality - As a result, Korea's housing market is closely related to deposit banks, and housing prices are acting as more dominant information variables than interest rates or loans under the long-term low interest rate trend. Therefore, in order to stabilize housing prices, the housing supply must be continuously made so that everyone can enjoy housing services equally. In addition, the expansion and reinforcement of the social security net should be realized systematically so as to stop households from being troubled with the housing price decline.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.39-48
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• 2024

In this paper, we introduce an extended GARCH model designed to address asymmetric leverage effects. The variance in the standard GARCH model is composed of past conditional variances and past squared residuals. However, it is not possible to model asymmetric leverage effects with squared residuals alone, so in this paper, we propose a new extended GARCH model to explain the leverage effects using the Yeo-Johnson transformation which adjusts transformation parameter to make asymmetric data more normal or symmetric. We utilize the reverse properties of Yeo-Johnson transformation to model asymmetric volatility. We investigate the characteristics of the proposed model and parameter estimation. We also explore how to derive forecasts and forecast intervals in the proposed model. We compare it with standard GARCH and other extended GARCH models that model asymmetric leverage effects through empirical data analysis.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.