• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Journal of Korea Water Resources Association
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• v.39 no.4 s.165
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• pp.299-311
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• 2006

In this paper it is outlined the methodology of estimating the parameters of water balance analysis method for calculating recharge, using ground water level rises in monitoring well when values of specific yield of aquifer are not available. This methodology is applied for two monitoring wells of the case study area in northern area of the Jeiu Island. A water balance of soil layer of plant rooting zone is computed on a daily basis in the following manner. Diect runoff is estimated by using SCS method. Potential evapotranspiration calculated with Penman-Monteith equation is multiplied by crop coefficients($K_c$) and water stress coefficient to compute actual evapotranspiration(AET). Daily runoff and AET is subtracted from the rainfall plus the soil water storage of the previous day. Soil water remaining above soil water retention capacity(SWRC) is assumed to be recharge. Parameters such as the SCS curve number, SWRC and Kc are estimated from a linear relationship between water level rise and recharge for rainfall events. The upper threshold value of specific yield($n_m$) at the monitoring well location is derived from the relationship between rainfall and the resulting water level rise. The specific yield($n_c$) and the coefficient of determination ($R^2$) are calculated from a linear relationship between observed water level rise and calculated recharge for the different simulations. A set of parameter values with maximum value of $R^2$ is selected among parameter values with calculated specific yield($n_c$) less than the upper threshold value of specific yield($n_m$). Results applied for two monitoring wells show that the 81% of variance of the observed water level rises are explained by calculated recharge with the estimated parameters. It is shown that the data of groundwater level is useful in estimating the parameter of water balance analysis method for calculating recharge.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.1-14
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• 2001

The enhancement of the refractive index structure parameter $C_n^2$ often occurs where vertical gradients of virtual potential temperature ${\theta}_v$ and mixing ratio q have their maximum values. The $C_n^2$ can be a very useful parameter for estimating the convective boundary layer(CBL) height. The behavior of $C_n^2$ peaks, often used to locate the height of mixed layer, was investigated in the present study. In addition, a new method to determine the CBL height objectively using both $C_n^2$ and vertical air velocity variance ${\sigma}_w$ data of UHF radar was also suggested. The present analysis showed that the $C_n^2$ peaks in the backscatter intensity profiles often occurred not only at the top of the CBL but also at the top of a residual layer or at a cloud layer. The $C_n^2$ peaks corresponding to the CBL heights were slightly lower than the CBL heights derived from rawinsonde sounding data when vertical mixing owing to weak solar heating was not significant and the height of strong vertical ${\theta}_v$ gradients were not consistent with that of strong vertical q gradients. However, the $C_n^2$ peaks corresponding to the CBL heights were in good agreement with the rawinsonde-estimated CBL hegiths when vertical mixing owing to solar heating was significant and the vertical gradient of both ${\theta}_v$ and q in the entrainment zone was very strong. The maximum backscatter intensity method, which determines the height of $C_n^2$ peak as the CBL height, correctly estimated the CBL height when the $C_n^2$ profile had single peak, but this method erroneously estimated the CBL height when there was a residual layer or a cloud layer over the top of the CBL. The new method distinguished when there the CBL height from the peak due a cloud layer or a residual layer using both $C_n^2$ and ${\sigma}_w$ data, and correctly estimated the CBL height. As for estimation of diurnal variation of the CBL height, the new method backscatter intensity method even if the vertical profile of backscatter intensity had two peaks from the CBL height and a residual layer or a cloud layer.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.

• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.83-87
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• 2012

The purpose of this study was to estimate genetic parameters for productive traits in swine. Productive traits were considered on average daily gain (ADG), body height (BH) and body length (BL). Genetic analysis was consisted of 18,668 heads for productive traits which were based on on-farm performance tested from May, 2007 to Apr, 2011. For estimating genetic parameters on productive traits, single best model was fitted after finding source of variance on fixed and random effects and estimated with a multiple trait animal model by using DF-REML (Derivative-Free Restricted Maximum Likelihood). The estimated heritabilities of Duroc, Berkshire, Landrace and Yorkshire 0.22-0.58 for the average daily gain, 0.34-0.41 for the body height and 0.4-0.52 for the body length, respectively. Phenotypic correlations of average daily gain with body height and body length for the four breeds were 0.42-0.48, 0.53-0.58, 0.34-0.46 and 0.47-0.56, respectively. Phenotypic correlations of body height with body length were 0.41, 0.57, 0.52, 0.59, respectively. The estimated genetic correlation coefficients of average daily gain with body height and body length estimated for the four breeds were 0.34-0.47, 0.70-0.75, 0.17-0.38 and 0.50-0.53, respectively. The estimated genetic correlation coefficients of body height with body length were 0.57, 0.69, 0.61 and 0.71, respectively.

• Current Research on Agriculture and Life Sciences
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• v.9
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• pp.37-49
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• 1991

This study was conducted to estimate the effects of year, age of dam at calving, farm and lactation period on milk yield with the data of 4,008 cows' records which were collected at 32 farms by Korea Animal Improvement Association from 1985 to 1989. The results obtained in this study are summarized as follows: 1. The average performance of the dairy cattle in the study were $5,959.23{\pm}2,113.03kg$ in actual milk yield, $49.19{\pm}22.77$ months in age of dam at calving, $27.11{\pm}5.13$ months in age at first calving and $255.11{\pm}79.68$ days in lactation period. 2. The percentages of variance component for different sources were 29.39% for the residuals, 1.91% for years, 4.86% for age at calving, 8.89% for farms and 54.94% for lactation period. 3. The overall mean of least-square estimate on the milk yield was 6,229.31kg. In the effects of year, the least-square means of milk yield were estimated 6,000.76kg in 1985-1987, 6,028.11kg in 1988 and 6,659.07kg in 1989. 4. The least-square means of calving age on the milk yield were estimated 5,456.01kg in less than 24 months, 6,565.48kg in 61-66 months which were the highest least-square means. This effects were gradually increased until the 61-66months and gradually decreased after the 61-66months, with highly significant differences among different months of age at calving(p<0.01). 5. In the effects of farm, the least-square means of milk yield were estimated 4,959.50 kg in the lowest farm and 7,497.07kg in the highest farm. Among the milk yield of each farm the effects showed highly significant difference(p<0.01). 6. The least-square means of milk yield in the effects of lactation period were gradually increased with the lapse of the lactation period. Among the lactation period the effects showed highly significant difference(p<0.01).

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.25 no.2
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• pp.9-25
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• 2020

In 2010 and 2015, total 77 surface sediment samples were collected to assess the anthropogenic effects of trace metals in surface sediments of Garolim Bay, and the physical characteristics (particle size and specific surface area) and geochemical components (major (Al, Ca, Fe, K, Ba) and trace metals (Mn, Cs, Cr, Co, Ni, Cu, Zn, Pb), organic carbon and calcium carbonate) were analyzed. Mean grain size of Garolim Bay surface sediments ranged from 0.51-5.58 Ø (mean 3.98 Ø) and increased from the inlet of bay to the inner bay, and from the waterway to the land. Most of the metal concentrations except for some elements showed the similar distribution to those of mean grain size and specific surface area. As the particle size decreased and the specific surface area increased, the metal concentration increased. In order to estimate the factors controlling the concentration of trace metals, factor analysis was performed, and three factors were extracted (92.7% of the total variation). Factor 1 accounted for 71.3% of the total variation, which was a grain size factor. Factor 2 accounted for 14.2% of the total variation, Factor 3 accounted for 7.2% of the total variance. Enrichment factor was calculated using the particle size corrected background concentration. Metals with a enrichment factor of 1.5 or higher and the number of samples were 4 for Cr (St. 1, 16, 27, 39) and 1 for Pb (St. 39), but there were little differences in the concentrations of 1M HCl leached metals for these metals. The percentage of 1M HCl leached fraction to total metal concentration decreased in the order of Pb~Co>Cu>Zn~Mn>Ni>Cr. Comparing this value with contaminated and clean sediments in other coastal areas, the percentages for each metal were similar regardless of the trace metal levels in all regions. This fact might be resulted from the reaction between the 1M HCl solution and the different sediment constituents, indicating that there is a limit to apply this percentage of leached metal to the estimation of the contamination extent.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.15 no.2
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• pp.124-134
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• 2005

To investigate the exposure effect of polynuclear aromatic hydrocarbons (PAHs), we measured airborne total PAHs as an external dose, urinary 1-hydroxypyrene (1-OHP) as an internal dose of PAHs exposure, and analyzed the relationship between urinary 1-OHP concentration and PAHs exposure. The study population contained 44 workers in steel-pipe coating and paint manufacture industries. The airborne PAHs was obtained during survey day, and urine were sampled at the end of shift. Personal information on age, body weight, height, eniployment duration, smoking habit, and alcohol consumption was obtained by a structured questionnaire. Airborne PAHs were analyzed by the gas chromatograph with mass selective detector. Urinary 1-OHP levels were analyzed by the high performance liquid chromatograph with ultraviolet wavelength detector. For statistical estimation, t-test, ${\chi}^2$-test, analysis of variance, correlation analysis, arid regression analysis were executed by SPSS/PC (Windows version 10). The mean of environmental total PAHs was $87.8{\pm}7.81{\mu}g/m^3$. The mean concentration ($526.5{\pm}2.85{\mu}g/m^3$) of workers in steel-pipe coating industries using coal tar enamel was the higher than that ($17.5{\pm}3.36{\mu}g/m^3$) of workers in paint manufacture industries using coal tar paint. The mean of urinary 1-OHP concentration ($51.63{\pm}3.144{\mu}\;mol/mol$ creatinine) of workers in steel-pipe coating industries was the higher than that ($2.33{\pm}4.709{\mu}\;mol/mol$ creatinine) of workers in paint manufacture industries. The mean of urinary 1-OHP concentration of smokers was the higher than that of non-smokers. There was significant correlation between the urinary concentration of 1-OHP and the environmental concentration of PAHs (r=O.S48, p<0.001), pyrene(r=0.859, p<0.001), and urinary cotinine (r=0.324, p<0.05). The regression equation between the urinary concentration of 1-OHP in ${\mu}g/g$ creatinine($C_{1-OHP}$) and airborne concentration of PAHs (or pyrene) in ${\mu}g/m^3$ ($C_{PAHs}$ or Cpyrene) is: Log ($C_{1-OHP}$)=-0.650+0.889×Log($C_{PAHs}$), where $R^2=0.694$ and n=38 for p<0.001.Log ($C_{1-OHP}$)=1.087+0.707${\times}$Log(Cpyrene), where $R^2=0.713$ and n=38 for p<0.001. From the results of stepwise multiple regression analysis about 1-OHP, significant independents were total PAHs and urinary cotinine (adjusted $R^2=0.743$, p<0.001). In this study, there were significant correlation between the urinary concentration of 1-OHP and the airborne concentration of PAHs. The urinary 1-OHP was effective index as a biomarker of airborne PAHs in workplace. But it was influenced by non-occupational PAHs source, smoking.

• Journal of Distribution Research
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• v.17 no.3
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• pp.59-92
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• 2012

With accumulated research evidence, there is little doubt that leadership behavior is related to a wide variety of positive individual and organizational outcomes. Indeed, leadership behavior has been empirically linked to increased employee satisfaction, organizational commitment, extra effort, turnover intention, organizational citizenship behavior, and overall employee performance. Although leadership behavior has been linked to a number of positive organizational outcomes, research regarding the antecedents of such behavior is limited. Especially there is little research dealing with the antecedents of inter-organizational leadership behavior. This study interests in inter-organizational ethical leadership among marketing channel members. In both the mass media and the academic association, there has been a surge in interest in the ethical and unethical behavior of leaders. Although the corporate scandals in recent years may explain much of the mass media and popular focus, academics' interest has been limited by evidence that ethical leadership behavior is associated with both positive and negative inter-organizational processes and performances. This study tried to contribute to this body of knowledge by examining antecedents of ethical leadership. Ethical leadership is defined "the demonstration of normatively appropriate conduct through personal actions and interpersonal relationships, and the promotion of such conduct to followers through two-way communication, reinforcement, and decision-making." Ethical leaders not only inform individuals of the behefits of ethical behavior and the cost of inappropriate behavior, such leaders also set clear standards and use rewards and fair and balanced punishment to hold followers accountable for their ethical conduct. Despite the assume importance and prominence of ethical leadership among organizations, there are still many questions relating to its antecedents and consequences. One is whether the likelihood of an leading organization being perceived as an ethical leader among other following organizations in marketing channels can be predicted using its characteristics and inter-organizational relationship maintenance skills. Identifying trait and skill antecedents will aid in the development of strategies for selecting and developing ethical leaders and determining the best means to reinforce ethical behaviors. The purpose of this study is to investigate the effects of three categorized variables on ethical leadership of channel leader. To be concrete, this study develops a model of the antecedents of three conceptually distinct forms of channel leader characteristics, such as organizational traits, inter-organizational relationship maintenance strategies, and supplier management strategies, and tests the hypothesized differential effects on ethical leadership of marketing channel leaders. The reason why this study deals with discount store channel is that there is very strong inter-dependence between a discount store and its suppliers. Their strong inter-dependence makes their relationship as the relationship between a leader and suppliers and creates an atmosphere that leadership occur without difficulty. The research model is as follows. For the purpose of empirical testing, 295 respondents of suppliers of discount store channel in Korea were surveyed. The procedures included scale reliability, and discriminant and convergent validity were used to validate measures. Also, the reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than .70. This study conducted confirmatory factor analyses to assess the validity of our measurements. All items loaded significantly on their respective constructs(with the lowest t-value being 15.2), providing support for convergent validity. We then examined composite reliability and average variance extracted(AVE). The composite reliability of each construct was greater than .70. The AVE of each construct was greater than .50. This study tested research model using Partial Least Square(PLS). The estimation of the structural equation model revealed an acceptable fit of the model to the data($r^2$=.851). Thus, This study concluded that the model fit was considered acceptable. The results of PLS are as follows. The results indicated that conscientiousness, openness, conflict management, social networks, training, fair reward had positive effects on ethical leadership of channel leaders. On the other hand, emotional insecure had negative effect and agreeableness, assurance, and inter-organizational communication had no significant effect on supply chain leadership.