• KDI Journal of Economic Policy
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• v.34 no.4
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• pp.1-26
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• 2012

Greenhouse gas emission policy in Korea and elsewhere is based on emissions projections, a key element of which is the projected path of structural change from high productivity growth to low productivity growth economic sectors given sector specific labor productivity growth, emissions abatement across sectors and population growth. Thus, it is important to model the source of the structural change to forecast emissions correctly. Using data for the Korean economy, this study constructs and quantitatively evaluates a model of structural change and green growth to generate policy implications for Korea and the international greenhouse gas debate.

• Journal of Korean Society for Quality Management
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• v.13 no.1
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• pp.20-25
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• 1985

There are several approaches for dealing with the structural change in regression model, but by introducing a concept of Spline, the structural change can be expressed more clearly. This makes it possible not only to know the location where the structural change happens and the total number, but also to derive posterior distribution from anterior-posterior distribution when the probability of the judgement anterior for entire combination was given to each model, by which, the model that has the highest posterior probability is the method which realizes the structural change. The purpose of this study is to find a peculiarity of the posterior probability on the occasion of anterior information acquired and of not acquired with Baysian approach.

• East Asian mathematical journal
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• v.35 no.3
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• pp.305-318
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• 2019

Over the past decades, the Lee-Carter model [1] has attracted much attention from various demography-related fields in order to project the future mortality rates. In the Lee-Carter model, the speed of mortality improvement is stochastically modeled by the so-called mortality index and is used to forecast the future mortality rates based on the time series analysis. However, the modeling is applied to long time series and thus an important structural change might exist, leading to potentially large long-term forecasting errors. Therefore, in this paper, we are interested in detecting the structural change of the Lee-Carter model and investigating the actuarial implications. For the purpose, we employ the tests proposed by Coelho and Nunes [2] and analyze the mortality data for six countries including Korea since 1970. Also, we calculate life expectancies and whole life insurance premiums by taking into account the structural change found in the Korean male mortality rates. Our empirical result shows that more caution needs to be paid to the Lee-Carter modeling and its actuarial applications.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• Korean Management Science Review
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• v.18 no.1
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• pp.119-133
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• 2001

It is important to find the equilibrium level of real interest rate for it affects real and financial sector of economy. However, it is difficult to find the equilibrium level because like the most macroeconomic model the real interest model has parameter instability problem caused by structural change and it is supported by various theories and definitions. Hence, in order to cover these problems structural change detection model of real interest rate is developed to combine the real interest rate equilibrium model and the procedure to detect structural change points. 3 equations are established to find various effects of other interest-related macroeconomic variables and from each equation, structural changes are found. Those structural change points are consistent with common expectation. Oil Crisis (December, 1987), the starting point of Economic Stabilization Policy (January, 1982), the starting point of capital liberalization (January, 1988), the starting and finishing points of Interest deregulation (January, 1992 and December, 1994), Foreign Exchange Crisis (December, 1977) are detected as important points. From the equation of fisher and real effects, real interest rate level is estimated as 4.09% (October, 1988) and dependent on the underlying model, it is estimated as 0%∼13.56% (October, 1988), so it varies so much. It is expected that this result is connected to the large scale simultaneous equations to detect the parameter instability in real time, so induces the flexible economic policies.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.103-110
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• 1996

In this paper, behaviour of underground structural systems due to excavation and change of groundwater level is analyzed using finite elements. Equilibrium equations based on the effective pressure theory and transient flow equations considering the groundwater level are derived. Integration equations are derived using Galerkin's approximation and time dependent analysis is employed to compute groundwater level change and pore pressures. This computed pore pressures are employed in equilibrium equations and then finally displacements and stresses are computed. The developed program is applied to analyze the behaviour of ground excavation below the groundwater level. The program is also applied to multi-step excavation at the same model. The results show that the displacements of the ground surface are much influenced by the change of the groundwater level. Therefore, it is concluded that the change of the groundwater level should be considered in order to analyze the behaviour of the underground structural systems accurately

• Geomechanics and Engineering
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• v.22 no.4
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• pp.291-303
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• 2020

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• Journal of Technology Innovation
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• v.7 no.1
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• pp.14-35
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• 1999

This paper presents the relationship between the pace of structural change and the magnitude of employment growth in the manufacturing sector in OECD countries. To measure the pace of structural change, the compositional change index in value-added in manufacturing sector is introduced. For mid to long-term there seems to be a positive relationship between the pace of structural change and the magnitude of employment growth. In those countries with higher value of the compositional index, the employment growth in manufacturing sector was generally higher. To analyse the characteristics of structural change in manufacturing sector, this paper classifies manufacturing industries into groups: one based on technology, one on orientation, one on wages and one on skills. The international comparison of manufacturing sector's employment patterns based on above four classifications are presented. International comparison suggests that Korean manufacturing sector move into jobs with more skills and knowledge The structural change of SMEs and large firms are compared based on above four classification methods. It is shown that SMEs' employment in low value sectors, that is low-technology, labor-intensive, tow-wage, and unskilled sectors, have risen faster than SMEs' employment in high-technology, science-based, high-wage and skilled sectors. Large firms' employment have been mainly increased in high value sectors. However, the employment growth of both large and small firms have been concentrated on production worker-intensively-using sectors, i.e. unskilled sectors. This widened the wage differential of production workers by firm sizes and concurrently led to severe shortage of production workers for SMEs, which has little ability to pay high wage to production workers because they usually belong to low-wage sectors. Korea need to push SMEs forward to high value sectors. The premise of that is, however, to pull large firms out of production worker-intensively-using sectors.