• 한국의류학회지
• /
• 제26권11호
• /
• pp.1605-1614
• /
• 2002

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality. In order to implement the purpose of this study, Jeans market had been segmented by the mixture regression model, and price response function was calibrated for each segment. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables. Data were co]looted from the sample of the 23o undergraduate and graduate students in Seoul. For the data analysis, mixture regression model, conjoint analysis, and t-test were used. As a result, jeans market was divided into 5 segments. Segment 1,2,3 were allocated into the group not using the level of price as the quality indicator while segment 4,5 were done into the other group. Significant differences existed between two groups in product attributes, not in demographic variables. Mixture model and conjoint analysis were proved to be an effective set of tools in market segmentation.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권11호
• /
• pp.13-22
• /
• 2020

This research explores the appropriate rubber pricing model and the consistent empirical evidence. This model has been derived from the utility function and firm profit-maximization model of commodity goods. The finding shows that the period t - 1 affects expected commodity price and expected profit of commodity production. In fact, a change in the world price of rubber in the past period led to a change in the expected price of rubber in the short run which influenced the expected rubber profit. As a result, the past-period free on board price has an entirety effect on expected farm price of rubber given an exchange rate. In addition, the rubber pricing model indicates that the profit of local farmer on rubber plant depends solely on the world price of rubber in the short run in case of Thailand. In an empirical study, it was found that a change in the price of ribbed smoke sheet 3 in Singapore Commodity Exchange significantly and positively determined the fluctuation of rubber price at the farm gate in Thailand which was consistent with the behavior of the Thai farmers. Both prices are also cointegrated in the long run. That is, the result states that the VECM is an appropriated pricing model for forecasting the farm price in Thailand.

• 한국의류학회지
• /
• 제26권6호
• /
• pp.877-888
• /
• 2002

Price is a product attribute, which is determined by the function of the producing cost and profit. It is also identified as one of the most important components of the marketing mix. For consumers, price is an always-existing cue, definite evaluation criteria, and easily accessible information in the purchasing process. Considering the concept of the clothing-price in a comprehensive perspective encompassing economic, psychological and marketing perspectives, a theoretical model was developed. The model includes souses and dimensions of price perception and related behaviors. Souses of price perception were: the actual retail price at selling point, the internal reference price and external reference price. The dimensions of price perception included sacrifice perception, economic value perception, inference, savings perception and price as information perception. Clothing price related behaviors that flowed these dimensions were: low price consciousness, value for money consciousness, price-quality inference, price-prestige inference, sale proneness and price mavenism. An empirical study was conducted to validate the theoretical model. A questionnaire was developed and data were collected from 680 adult women living in Seoul, Korea. Confirmatory factor analysis as well as exploratory factor analysis results showed that theorized price related behaviors were successful classifications.

• Asia pacific journal of information systems
• /
• 제11권4호
• /
• pp.99-111
• /
• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• 유통과학연구
• /
• 제11권10호
• /
• pp.63-72
• /
• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• 수산경영론집
• /
• 제46권1호
• /
• pp.93-107
• /
• 2015

This study aims to analyze causalities among Hairtail prices by distribution channel using a vector autoregressive model. This study applies unit-root test for stability of data, uses Granger causality test to know interaction among Hairtail Prices by distribution channel, and employes the vector autoregressive model to estimate statistical impacts among t-2 period variables used in model. Analyzing results of this study are as follows. First, ADF, PP, and KPSS tests show that the change rate of Hairtail price by distribution channel differentiated by logarithm is stable. Second, a Granger causality test presents that the producer price of Hairtail leads the wholesale price and then the wholesale price leads the consumer price. Third, the vector autoregressive model suggests that the change rate of Hairtail producer price of t-2 period variables statistically, significantly impacts change rates of own, wholesale, and consumer prices at current period. Fourth, the impulse response analysis indicates that impulse responses of the structural shocks with a respectively distribution channel of the Hairtail prices are relatively more powerful in own distribution channel than in other distribution channels. Fifth, a forecast error variance decomposition of the Hairtail prices points out that the own price has relatively more powerful influence than other prices.

• 부동산연구
• /
• 제24권1호
• /
• pp.7-14
• /
• 2014

본 연구는 2006년 1월부터 2013년 6월까지의 서울시 아파트 개별 실거래가격에 대한 시공간 자료로 시공간자기상관의 문제를 헤도닉가격결정모형에 의한 통상최소자승법(OLS), 시간효과를 고려한 시간자기회귀모형(TAR), 공간효과를 고려한 공간자기회귀모형(SAR)과 시공간자기회귀모형(STAR)을 이용해 아파트 가격 추정결과를 비교분석하였다. 실증분석결과, STAR모형이 기존의 OLS에 비해 수정결정계수가 약 10% 증가하였으며, 추정오차는 약 18% 감소한 것으로 나타나 시공간효과를 고려했을 때 아파트 가격 추정이 기존모형에 비해 정확함을 알 수가 있었다. STAR모형 분석결과, 아파트 매매가격에 전용면적(-), 아파트연수(-), 저층더미(-), 개별난방(-), 도시가스(-), 재건축더미(+), 계단식(+), 단지규모(+)등이 영향을 주는 것으로 나타났으며 다른 분석방법론과도 대부분 같은 부호를 나타냈다. 시공간자기회귀모형을 이용해 부동산 가격을 추정시 정부 당국자는 부동산시장의 동향을 정확히 파악해 정책을 수립 집행해 정책효율을 높을 수 있고 투자자의 입장에서는 객관적인 정보를 바탕으로 합리적 투자를 할 수 있다.

• Industrial Engineering and Management Systems
• /
• 제8권1호
• /
• pp.1-13
• /
• 2009

This paper presents a coordinated planning model of price-dependent demand for a single-manufacturer and a single-retailer. The demand is assumed to be normally distributed, with its mean being price dependent. The manufacturer and retailer coordinate with each other to jointly and simultaneously determine the retail selling price and the retailer order quantity to maximize the joint expected total profit. This model is then compared to a 'returns' policy model where manufacturer buys back unsold items from the retailers. It is shown that the optimal total profit is higher for coordinated planning model than that for the returns policy model, in which the retail price is set by the retailer. A compensation or profit sharing scheme is then suggested and it is shown that the coordinated model with profit sharing yields a 'win-win' situation. Numerical results are presented to illustrate the profit patterns for both linear and nonlinear demand functions. The coordinated planning model, in addition, has a lower optimal price than for a returns policy model, which would result in higher sales, thus expanding the markets for the whole supply chain.

• Ocean and Polar Research
• /
• 제36권4호
• /
• pp.505-514
• /
• 2014

The development of deep seabed manganese nodules has been carried out with the aim of commercial development in 2023. It is important to forecast the price of the four metals (copper, nickel, cobalt, and manganese) extracted from manganese nodules because price change is a criterion for investment decision. The main purpose of the study is to forecast the price of four metals using the ARIMA model and VAR model, and calculate the MAPE to compare a goodness-of-fit between the two models. The estimated results of the two models reveal statistical significance and are in keeping with economic theory. The results of MAPE for goodness-of-fit show that the VAR model is between 0.1 and 0.2, and the ARIMA model is between 0.4 and 0.6. That is, the VAR model is better than the ARIMA model in forecasting changes in the price of metals.

• 농업과학연구
• /
• 제45권4호
• /
• pp.859-870
• /
• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.