• Health Policy and Management
• /
• v.13 no.3
• /
• pp.91-103
• /
• 2003

The purpose of this study was to provide basic reference data for stabilization scheme of health expenditure through forecasting of health expenditure. The authors analyzed the health expenditure from 1985 to 2000 that had been calculated by Korean institute for health and social affair using transfer function model as ARIMA model with input series. They used GDP as the input series for more precise forecasting. The model of error term was identified ARIMA(2,2,0) and Portmanteau statics of residuals was not significant. Forecasting health expenditure as percent of GDP at 2010 was 6.8%, under assumption of 5% GDP increase rate. Moreover that was 7.4%, under assumption of 3% GDP increase rate and that was 6.4%, under assumption of 7% GDP increase rate.

• Architectural research
• /
• v.8 no.1
• /
• pp.77-84
• /
• 2006

In spite of enormous increase in data generation, its practical usage in the construction sector has not been prevalent enough compared to those of other industries. The author would explore the obstacles against efficient data application in the arena of expenditure forecasting, and suggest a forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in the research, enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. Among 99 projects collected, the cost data from 88 projects were processed to establish a new forecasting model. The remaining 10 projects were utilized for the validation of the model. From the comprehensive study, the choice of the numbers of referring projects was investigated in detail. It is concluded that selecting similar projects at 12~19 % out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Journal of the Korea Institute of Building Construction
• /
• v.12 no.3
• /
• pp.257-265
• /
• 2012

Many construction companies are simultaneously carrying out numerous projects in the housing construction industry. It is essential to accurately forecast the cash flow of a project through optimal process management and resource input in order to manage funds rationally and enhance the competitiveness of a company. Current cash flow forecasting methods offer lower accuracy due to a large gap between the revenue and expenditure element. Expenditure elements depends on the real-time changing actual cost for work performed. This research survey was conducted on the actual state of construction management of K company to investigate the problems of cash flow forecasting. To achieve this, the work process and construction management system were integrated to improve the cost management system of K company. To accurately forecast the cash flow of a project, revenue and expenditure elements were displayed in the total cash flow forecast window. This research is expected to assist in the implementation of a system of cash flow forecasting on housing construction by excluding negative elements of revenue and expenditure.

• Korean Journal of Construction Engineering and Management
• /
• v.4 no.4 s.16
• /
• pp.114-122
• /
• 2003

Dynamic and fragmented characteristics are two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics, and do not provide considerable support This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, the choice of the numbers of referring projects was investigated. It is concluded that selecting similar projects at $5{\~}6{\%}$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2004.11a
• /
• pp.117-124
• /
• 2004

Dynamic and fragmented characteristics ale two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics and do not provide considerable support. This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting. the choice of the numbers of referring projects was investigated. it is concluded that selecting similar projects at $5\~6\;\%$ out of the whole database will produce a more precise forecasting. The new forecasting model. which suggests the predicted values based on previous projects, is more than just a forecasting methodology it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the know ledge derived from invaluable experience.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.44
• /
• pp.257-261
• /
• 1997

Patents often serve as leading indicators of technological change. This patenting activity reflected R&D (Research & Development) of new technology. The purpose of this study is to set up a forecasting model that anticipate the number of domestic patent applications and the number of patents granted relating to R&D inputs (R&D expenditure, R&D manpower) at the level of three industrial sectors in Korea : electrical-electronic, machinery, chemical etc. In this study, forecasting models were used trend extrapolation and a set of regressions. Both Theil's inequality coefficient and MAE(Mean Absolute Error) were utilized to test the precision of predicted value. The patent data and the R&D data were based on Indicators of Industrial Technology data throught 1980 to 1996. The major results obtained in this study are as follows (1) The regression model is more useful for forecasting the trends of the number of patent applications and patents granted than the trend extrapolation method. (2) The variance of Theil's inequality is smaller in patent applications than in patent granted.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.769-782
• /
• 2020

Interest in fruit and vegetables has increased due to changes in consumer consumption patterns, socioeconomic status, and family structure. This study determined the factors influencing the demand for fruit and vegetables (strawberries, paprika, tomatoes and cherry tomatoes) using a panel of Rural Development Administration household-level purchases from 2010 to 2018 and compared the ability to the prediction performance. An artificial neural network model was constructed, linking household characteristics with final food expenditure. Comparing the analysis results of the artificial neural network with the results of the panel model showed that the artificial neural network accurately predicted the pattern of the consumer panel data rather than the fixed effect model. In addition, the prediction for strawberries was found to be heavily affected by the number of families, retail places and income, while the prediction for paprika was largely affected by income, age and retail conditions. In the case of the prediction for tomatoes, they were greatly affected by age, income and place of purchase, and the prediction for cherry tomatoes was found to be affected by age, number of families and retail conditions. Therefore, a more accurate analysis of the consumer consumption pattern was possible through the artificial neural network model, which could be used as basic data for decision making.

• KDI Journal of Economic Policy
• /
• v.28 no.2
• /
• pp.1-49
• /
• 2006

Considering the fact that households' demographic characteristics affect consumption decision, it is conjectured that rapid demographic changes would lead to a substantial change in the composition of private consumption expenditure. This paper estimates the demand functions of various consumption items by applying the Quadratic Almost Ideal Demand System(QUAIDS) model to Household Income and Expenditure Survey data, and then provides a long-term forecast of the composition of household consumption expenditure for 2005-2020. The paper shows that Korea's consumption expenditure will maintain the recent years' rapid change, of which a considerable portion is due to rapid demographic changes. Results of the paper can be utilized in forecasting the change in the industrial structure of the economy, as well as in firms' investment planning.

• Health Policy and Management
• /
• v.28 no.4
• /
• pp.378-391
• /
• 2018

Background: People who were born in different years, that is, different birth cohorts, grow in varying socio-historical and dynamic contexts, which result in differences in social dispositions and physical abilities. Methods: This study used age-period-cohort analysis method to establish explanatory models on healthcare expenditure in Korea reflecting birth cohort factor using intrinsic estimator. Based on these models, we tried to investigate the effects of ageing population on future healthcare expenditure through simulation by scenarios. Results: Coefficient of cohort effect was not as high as that of age effect, but greater than that of period effect. The cohort effect can be interpreted to show 'healthy ageing' phenomenon. Healthy ageing effect shows annual average decrease of -1.74% to 1.57% in healthcare expenditure. Controlling age, period, and birth cohort effects, pure demographic effect of population ageing due to increase in life expectancy shows annual average increase of 1.61%-1.80% in healthcare expenditure. Conclusion: First, since the influence of population factor itself on healthcare expenditure increase is not as big as expected. Second, 'healthy ageing effect' suggests that there is a need of paradigm shift to prevention centered-healthcare services. Third, forecasting of health expenditure needs to reflect social change factors by considering birth cohort effect.

• The Korean Journal of Applied Statistics
• /
• v.31 no.2
• /
• pp.303-313
• /
• 2018

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.