• International conference on construction engineering and project management
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• 2015.10a
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• pp.44-48
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• 2015

Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.

• Journal of Energy Engineering
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• v.21 no.3
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• pp.301-308
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• 2012

This paper attempts to estimate the demand function for the transport LPG and to analyze long-run and short-run price and income elasticities. In addition, the paper measures consumer surplus and economic value ensuing from the transport LPG consumption by utilizing the estimated long-run price elasticity. The price and the income data are the monthly real transport LPG price and the monthly composite index adjusted by real transport LPG price from 2003 to 2012. Unit root test, co-integration test and error correction model are to take the procedure of estimation of demand curve. The demand for transport LPG is considered to be inelastic and the long-run demand is more elasticity than that of short-run. Price elasticity of demand estimate here is -0.422, and the estimated consumer surplus and economic value in 2010/03 are 966 and 1,781 billion won, respectively.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.153-168
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• 2017

Recent studies focus on the role of investor attention and transparency in stock-related information in explaining stock return and trading volume. Moreover, recent literatures predict that firm opacity will increase the likelihood of future stock price crashes. In this paper, we investigate, using Naver Trend, the relation between portal search intensity and stock price crash. Using various alternative measures of stock price crash risk and search intensity, we demonstrate that stocks with larger volume of portal search are less likely to experience stock price crashes. These results are consistent with our hypothesis that accumulated firm opacity cause future stock price crash. Finally, our results still hold even after we control for the potential effect of endogeneity in the regression specifications.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices

• Environmental and Resource Economics Review
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• v.33 no.2
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• pp.135-157
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• 2024

In this study, we analyzed the changes in copper consumption according to copper price fluctuations and identified the domestic copper price elasticity. A total of 408 time series data from January 1989 to December 2022 were analyzed using the vector autoregressive (VAR) model with net import volume, price, and production index as variables. In addition, to identify changes in the correlation between variables over time, the dynamic relationship between variables was identified using the time-varying vector autoregressive (TV-VAR) model. As a result of the analysis, it was confirmed that the negative price elasticity for copper is -0.1835. In addition, the interquartile range was -0.3130 ~ 0.0886, with no consistent trend over time, but mainly negative elasticity. This study can be used to quantify the expected impact of various policy proposals and changes related to minerals.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.50
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• pp.309-331
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• 2011

Today, the world is considered to indispensable basic data in specific gravity of international trade is increasing in economic activity of every country with globalization, and trade connection index number analyzes an economy or part of trade that contribute to economic growth of a country along with other foreign trade statistics and evaluates along with this. Also, it is becoming one of big subject for economic policy person in charge and related economists I do how measure movement of amount, price and amount of materials in trade. But, about till now interest lack about trade index and trade index creation theoretical, it is actuality that export, import connection index number or similar research is not attained much into domestic and overseas from study tribe which is gone ahead. Moreover, study that try to judge and forecast stream of market applying trade connection index number is hard to find on study until now. And, in this research, there is the objective to figure out stream of Korean market change through trade business index creation that base on Korea Customs Administration export and the importation data and this is differences with several study, and at the same time, it is value of this study.

• Environmental and Resource Economics Review
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• v.9 no.5
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• pp.925-945
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• 2000

This paper analyses the impact of energy-saving investment on Greenhouse gas emissions using a model of energy demand in Korea. SUR method was employed to estimate the demand equation. The econometric estimates provide information about the energy price divisia index, sector income, and energy saving-investment elasticities of energy demand. Except for energy price divisia, the elasticities of each variable are statistically significant. Also, the price and substitution elasticities of each energy price are similar to the results reported by the previous studies. The energy-saving investment is statistically significant and elasticities of each sector is inelastic. Using the coefficient of energy-saving investment and carbon transmission coefficient, the amount of reduction of energy demand and the reduction of carbon emissions can be estimated. The simulation is performed with the scenario that the energy-saving investment increase by 10~50%, keeping up with Equipment Investment Plan of 30% increase in energy-saving investment by 2000. The results show that the reduction of energy demand measured as 11.2% based upon 1995's level of the energy demand, in industrial sector. Accordingly, the carbon emissions will be reduced by 11.3% based upon 1995's level of the carbon emissions in industrial sector.

• Journal of the Korea Fashion and Costume Design Association
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• v.9 no.1
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• pp.23-33
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• 2007

Clothing exports of Korea has grown rapidly till the latter half of the 1980's, contributing Korean economic development. However from the 1990's, the amount, the world market share and the international competitiveness of clothing exports have declined. Based on these phenomena, the purpose of this study was to identify the characteristics of export articles in Korean Clothing Trade focused on the 1990's. Statistical data of clothing articles(SITC 84 : Articles of apparel & clothing accessories) were used. The relative importance, trade orientation tendency and unit price of each export clothing articles were analyzed. The results of the study were as follows. On the relative importance, trade orientation tendency and unit price of each export clothing articles, outer garments or products that required complicated production process(e.g., coats, suits, ensembles, jackets, dress) had been decreased in the portion and weakened in the export orientation tendency. But one item in a set or casual wear like trousers, skirts, blouses, shirts, Jerseys, pullovers, T-shirts has been increased in the portion and risen in the unit price. These trends means that clothing exports of Korea were more focused on those category and the international competitiveness on those articles were advanced. From these results, this study can be contributed to establish the concrete clothing export articles strategies of Korean firms.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.635-651
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• 2009

Democratization and advancement of a society requires the Government's commitment to evidence-based policy. Though statistic is known as one of the best available evidence, there has been only a few case studies to tell real stories about using statistics for policy making. The object of this study is to suggest some real stories about using the Housing Purchase Price Survey for some property policies. By reviewing the origin and development of the survey, we evaluate the design and analysis strategies adopted in the survey. In addition, we describe how the Housing Purchase Price Indices have been used by the Government for some property policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7672-7676
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• 2015

This study examines the dynamic relations between housing price and trading volume in a set of apartment markets in Republic of Korea to explore the informational role of trading volume in predicting the price volatility. Using monthly index data, EGARCH model is utilized to test for volume effect. To estimate the EGARCH-based volatility, two different sets of region are applied for the monthly return. Strong evidence has been found towards housing turnover leading price volatility, this supports previous studies on financial sector(s). These findings also support that trading volume in the housing market contains information on investor sentiment which, in turn, has a valuation effect on the price.