• East Asian Economic Review
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• v.25 no.4
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• pp.403-424
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• 2021

AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models' accuracy is high enough. Therefore, in this study, we propose a novel way to improve the performance of the existing AdaBoost algorithm by employing heterogeneous models and a stochastic twist. By employing the heterogeneous ensemble, it ensures different models that have a different initial assumption about the data are used to improve on diversity. Also, by using a stochastic algorithm with a decaying convergence rate, the model is designed to balance out the trade-off between model prediction performance and model convergence. The result showed that the stochastic algorithm with decaying convergence rate's did have a improving effect and outperformed other existing boosting techniques.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Radiation Protection and Research
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• v.20 no.1
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• pp.8-15
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• 1995

An estimate of a change in radioactivity's circumstances around the nuclear power plant is validated with the results of the radioactivity measurements are compared. In this study, to further enhance the reliability of the results obtained from the environmental radioactivity measurements and analysis around the nuclear power plants that have been carried out up to the present. In the korea standard, there is the technical analysis guide for general stable chemical element's, but there is not the technical analysis guide for the radionuclei. therefore the environmental sample collection, the pretreatment of the sample and radionuclide analysis in the sample, the result's of the environmental radioactivity measurements by each organization, etc. are different. It is not sufficient for the database to forecasting a change in radioactivity's circumstances. A comparative study of collection and pretreatment techniques for the soil sample, the results by comparison, the method of minimizing the relative error are proposed. At one side of sample collection, there are going to considered that the surroundings of sample collection like the lay of the land, the provision of the selection standard for the area and pathway of radionuclide adhesion, the coherence of sample collection, etc.. at another side of pretreatment of the sample and measurement in the case of soil sample, how to do homogeneously the soil particle size and the standard tools, i.e. kinds of meshes, must to be selected.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1213-1229
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• 2016

Cho and Shin (2016) recently demonstrated that an integrated HAR model has a forecast advantage over the HAR model of Corsi (2009). Recalling that realized volatilities of financial assets have asymmetries, we add a leverage term to the integrated HAR model, yielding the LIHAR model. Out-of-sample forecast comparisons show superiority of the LIHAR model over the HAR and IHAR models. The comparison was made for all the 20 realized volatilities in the Oxford-Man Realized Library focusing specially on the DJIA, the S&P 500, the Russell 2000, and the KOSPI. Analysis of the realized volatility data sets reveal apparent long-memory and asymmetry. The LIHAR model takes advantage of the long-memory and asymmetry and produces better forecasts than the HAR, IHAR, LHAR models.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• Economic Analysis
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• v.26 no.3
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• pp.1-47
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• 2020

This paper surveys research since the 1990s on the ability of the yield spread and its components (i.e., expectation spread and term premium components) for future economic activity, and also conducts an empirical analysis of their forecasting ability using the yield data of Korean government bonds. This paper's survey, particularly for the US, shows that the yield spread has significant predictive power for some macroeconomic variables, but since the mid-1980s, its predictive power seems to have declined, possibly due to stronger inflation targeting. Next, this paper's empirical analysis using Korean data indicates that the yield spread, and the term premium component in particular, has significant predictive power for industrial production (IP) growth, consumer price index growth, and the IP gap. An out-of-sample analysis shows that the prediction equations are unstable over time, and that in predicting IP growth, the yield spread decomposition makes a significant contribution to the prediction of IP growth.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.49-62
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• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.

• Economic Analysis
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• v.24 no.3
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• pp.1-36
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• 2018

When it comes to explaining the relationship between inventory investment and business fluctuations, the production smoothing theory and the stock-out avoidance theory take contradictory stances. Decision-making related to inventory investments of corporations is thought to be influenced by both motives, but the relative sizes or directions of their respective influences can differ depending upon the phase of the business cycle. Against this backdrop, this paper differs from existing studies in that it theoretically tests the relative significances of the production smoothing and stock-out avoidance motives in the inventory investment dynamics, while placing its analytical focus on determining the existence and patterns of the asymmetric dynamics of inventory investment over the business cycle phases. To this end this paper sets up a non-linear model that is expanded from the existing linear inventory investment model, and checks whether its predictive power is better than that of the existing model. The results of analysis confirm the nature of the asymmetric dynamics of inventory investment over the business cycle phases. A stock-out avoidance motive appears but there is no significant production smoothing motive in boom times. In downturns, in contrast, the stock-out avoidance motive is insignificant, but a quality of asymmetric dynamics in which changes in inventory cause the deepening of recessions, due to the non-convexity of production costs proposed by Ramey (1991), is detected. This paper confirms that a model considering the asymmetric dynamics of inventory investment can have better predictive power than one that does not consider it, through within-sample and out-of-sample predictions and various predictive power tests. These research results are expected to be useful for economic forecasting, through their enhancement of the understandings of the inventory investment dynamics and of the nature of its business cycle destabilization.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.323-334
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• 2017

We note that the dynamics of realized volatilities (RVs) are near the boundary between stationarity and non-stationarity because RVs have persistent long-memory and are often subject to fairly large outlying values. To forecast realized volatility, we consider a new method that adaptively use models with and without unit root according to the abnormality of observed RV: heterogeneous autoregressive (HAR) model and the Integrated HAR (IHAR) model. The resulting method is called the IHAR-O-HAR method. In an out-of-sample forecast comparison for the realized volatility datasets of the 3 major indexes of the S&P 500, the NASDAQ, and the Nikkei 225, the new IHAR-O-HAR method is shown superior to the existing HAR and IHAR method.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.875-887
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• 2021

Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.