• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.329-338
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• 2019

In this study, we analyze consumer preferences based on the agricultural box scheme attributes, and make a suggestion for business revival. We estimate the marginal willingness to pay (MWTP) for box scheme attributes using a choice experiment. Attributes include the bundle method, the delivery method, and price. To select an efficient model for statistical analysis, we evaluate the conditional logit model, heteroscedastic extreme value model(HEV model), multinomial probit model, and mixed logit model under different assumptions. The results of these four models show that the bundle method, the delivery method, and price are statistically significant in explaining the probability of participation in a box scheme. The results of likelihood ratio tests show that the heteroscedastic extreme value model is the most appropriate for our survey data. The results also indicate that MWTP for a change from fixed type to selection type is KRW 7,096.6. MWTP for a change from parcel service to direct delivery and cold-chain delivery are KRW 3,497.5 and KRW 7,532.7, respectively. The results of this study may contribute to the government's local food policies.

• Journal of Korea Technology Innovation Society
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• v.6 no.3
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• pp.279-297
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• 2003

The purpose of this paper is to analyze the determinants of company R＆D investment with zero observations by using the data of R＆D Scoreboard published by Ministry of Science and Technology(2002). Conventional parametric approach to dealing with zero investments is not robust to heteroscedastic and/or non-normal error structure. Thus, this study applies symmetrically trimmed least squares(STLS) estimation as a semi-parametric approach to dealing with zero R&D investments. The result of specification test indicates the semi-parametric approach outperforms the parametric approach significantly. Moreover, the results of the study provide various implications as summarized below. The R&D investment of IT company is larger than that of non-IT company. The R&D investment has a positive relation to foreigners' investment ratio. The higher degree of financial self-reliance is, the larger the R&D investment is. Firm size variables such as sales amount and the number of workers are positively related to R&D investment. The sales elasticity of R&D investment is larger than one. However, the workers elasticity of R&D investment is smaller than one.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.89-96
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• 2009

We examined the characteristics of temperature variation in Seoul between 1961 to 2008 using the spectral heteroscedastic model. The mean function in the propsed model explains the season effect using periodic functions and the overall increase using the quadratic regression spline. The variance function also had periodic functions to explain the seasonality of variance. We found that there has been annual mean temperature increase by about $1.5^{\circ}C$ for the last 48 years. The increase of annual mean temperature was mainly caused by the increase in winter, which made the amplitude decreased.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.287-297
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• 2009

In terms of generalized autoregressive conditional heteroscedastic(GARCH) model, estimation of prediction interval based on likelihood is quite sensitive to distribution of error. Moveover, it is not an easy job to construct prediction interval for conditional variance. Recent studies show that the bootstrap method can be one of the alternatives for solving the problems. In this paper, we introduced the bootstrap approach proposed by Pascual et al. (2006). We employed it to Korean stock price data set.

• International Commerce and Information Review
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• v.10 no.1
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• pp.395-414
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• 2008

The objective of this study is to introduce the concepts and theories of conditional heteroscedastic volatility models and the news impact curves and apply them to the Korea inbound tourism market. Three volatility models were introduced and used to estimate the conditional volatility of monthly arrivals of inbound tourists into Korea and news impact curves according to the three models. Results of this study are as follows. As the proportion of American tourists occupied a large amount of Korea inbound tourism market, the markets' forecasting is very important. The news impact curves which used EGARCH model (1,1) and TGARCH model(1,1), with data on these tourists to Korea showed an asymmetry effect of volatility. It was common that bad news means that it was estimated more sensitively than good news. From these results, we will notice that American tourists who visited Korea only for tourism are affected by good news. The result suggests that the Korea government and tourism industry should pay more attention to changes in the tourism environment following bad news because conditional volatility increases more when a negative shock occurs than when a positive shock occurs.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.489-499
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• 2018

Jeonse is a unique property rental system in Korea in which a tenant pays a part of the price of a leased property as a fixed amount security deposit and gets back the entire deposit when the tenant moves out at the end of the tenancy. Jeonse deposit is very important in the Korean real estate market since it is directly related to the residential property sales price and it is a key indicator to predict future real estate market trend. Jeonse deposit data shows a skewed and heteroscedastic distribution and the commonly used mean regression model may be inappropriate for the analysis of Jeonse deposit data. In this paper, we apply a Bayesian quantile regression model to analyze Jeonse deposit data, which is non-parametric and does not require any distributional assumptions. Analysis results show that the quantile regression coefficients of most explanatory variables change dramatically for different quantiles. The regression coefficients of some variables have different signs for different quantiles, implying that even the same variable may affect the Jeonse deposit in the opposite direction depending on the amount of deposit.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.7
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• pp.1-13
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• 2002

Two global optimization methods, the SCE-UA method and the Annealing-simplex (A-S) method for calibrating a daily rainfall-runoff model, a Tank model, was compared with that of the Downhill Simplex method. The performance of the four objective functions, DRMS (daily root mean square), HMLE (heteroscedastic maximum likelihood estimator), ABSERR (mean absolute error), and NS (Nash-Sutcliffe measure), was tested and synthetic data and historical data were used. In synthetic data study. 100% success rates for all objective functions were obtained from the A-S method, and the SCE-UA method was also consistently able to obtain good estimates. The downhill simplex method was unable to escape from local optimum, the worst among the methods, and converged to the true values only when the initial guess was close to the true values. In the historical data study, the A-S method and the SCE-UA method showed consistently good results regardless of objective function. An objective function was developed with combination of DRMS and NS, which putted more weight on the low flows.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.189-200
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• 2015

Stationary bootstrapping is applied to a Lagrangian multiplier (LM) test to test market microstructure noise (MMN) in financial asset prices. A Monte-Carlo experiment shows that the bootstrapping method improves the size of the original LM test which has some size distortion for conditional heteroscedastic models. The proposed test is illustrated for real data sets like KOSPI index and Won-Dollar exchange rate.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.651-664
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• 2022

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.925-937
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• 2008

In this study, we considered the clustering analysis for stock return traded in the stock market. Most of financial time-series data, for instance, stock price and exchange rate have conditional heterogeneous variability depending on time, and, hence, are not properly applied to the autoregressive moving-average(ARMA) model with assumption of constant variance. Moreover, the variability is font and center for stock investors as well as academic researchers. So, this paper focuses on the generalized autoregressive conditional heteroscedastic(GARCH) model which is known as a solution for capturing the conditional variance(or volatility). We define the metrics for similarity of unconditional volatility and for homogeneity of model structure, and, then, evaluate the performances of the metrics. In real application, we do clustering analysis in terms of volatility and structure with stock return of the 11 Korean companies measured for the latest three years.