• Environmental and Resource Economics Review
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• v.33 no.2
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• pp.135-157
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• 2024

In this study, we analyzed the changes in copper consumption according to copper price fluctuations and identified the domestic copper price elasticity. A total of 408 time series data from January 1989 to December 2022 were analyzed using the vector autoregressive (VAR) model with net import volume, price, and production index as variables. In addition, to identify changes in the correlation between variables over time, the dynamic relationship between variables was identified using the time-varying vector autoregressive (TV-VAR) model. As a result of the analysis, it was confirmed that the negative price elasticity for copper is -0.1835. In addition, the interquartile range was -0.3130 ~ 0.0886, with no consistent trend over time, but mainly negative elasticity. This study can be used to quantify the expected impact of various policy proposals and changes related to minerals.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.419-425
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• 2010

Kernel machine learning is gaining a lot of popularities in analyzing large or high dimensional nonlinear data. We use this technique to estimate a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we show that GARCH models can be estimated using kernel machine learning and that kernel machine has a higher predicting ability than ML methods and support vector machine, when estimating volatility of financial time series data with fat tail.

• Korea Trade Review
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• v.42 no.2
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• pp.145-174
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• 2017

Recently, due to the worsening global economic recession, Korea which is a small, export-oriented economy has decreased exports and the domestic economy also continues to stagnate. Therefore, for continued growth of our economy through export growth, we need to analyze the validity of export support system such as export insurance and prepare ways to expand exports. This study is to investigate the effects of Export Insurance on the exports of SMEs as well as LEs. For this purpose, this study conducted Time Series Analysis using data such as export, export insurance acquisition, export price index, exchange rate, and coincident composite index(CCI). First, as a result of the Granger Causality Test, the exports of LEs has found to have a causal relationship with the CCI, and CCI is to have a causal relationship with the short-term export insurance record. Second, the results of VAR analysis show that the export insurance acquisition result and the export price index have a positive effect on the exports of LEs, while the short - term export insurance has a negative effect on the exports of LEs. Third, as a result of variance decomposition, the export of LEs has much more influenced for mid to long term by the short-term export insurance acquisition compared to SMEs. Fourth, short-term export insurance has a positive effect on exports of SMEs. In order to activate short-term export insurance against SMEs, it is necessary to expand support for SMEs by local governments. This study aims to suggest policy implications for establishing effective export insurance policy by analyzing the effects of export insurance on the export of SMEs as well as LEs. It is necessary to carry out a time series analysis on the export results according to the insurance acquisition results by industry to measure the export support effect of export insurance more precisely.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.293-301
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• 2001

This paper reviews three methods to compute a pointwise confidence interval of neuro-fuzzy models and compares their estimation perfonnanee through simulations. The eOITl.putation methods under consideration include stacked generalization using cross-validation, predictive error bar in regressive models, and local reliability measure for the networks employing a local representation scheme. These methods implemented on the neuro-fuzzy models are applied to the problems of simple function approximation and chaotic time series prediction. The results of reliability estimation are compared both quantitatively and qualitatively.

• The Korean Journal of Financial Management
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• v.16 no.2
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• pp.91-118
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• 1999

본(本) 연구(硏究)는 기업(企業)의 소유구조(所有構造)와 기업가치(企業價値) 사이의 관계를 분석하여 횡단면(橫斷面) 요인(要因)과 시계열(時系列) 요인(要因)을 결합하는 이론적(理論的) 모형(模型)을 제시하며, 또한 제시된 모형(模型)을 한국증권시장(韓國證券市場)의 자료(資料)를 이용하여 실증적(實證的)으로 분석하여 절충가설이 우리나라 기업에 적용될 수 있는지를 검토하였다. 본(本) 연구(硏究)에서는 기업가치(企業價値)를 나타내는 대용변수(代用變數)로 시장가치 대 장부가치비율을, 독립변수(獨立變數)로는 대주주 1인 지분율을 그리고 통제변수(統制變數)로는 광고비 정도, 기업규모, 재무레버리지, 30대 재벌 비재벌의 가변수(假變數)를 사용하였다. 실증분석 결과 본(本) 연구(硏究)에서 제시된 모형이 상당한 의미를 가지나, 모형(模型)의 설명력(說明力)은 비교적 낮아 본(本) 연구(硏究)에서 제시된 설명변수(說明變數)들이 자본구조(資本構造)의 변동(變動)을 8%정도 설명하고 있다. 또한 본(本) 연구(硏究)에서 제시된 독립변수 대주주 1인의 지분율과 이를 제곱한 것은 통계적으로 의미가 있는 변수가 되고 있다. 이는 대주주 1인의 지분율과 기업가치(企業價値) 사이에는 곡선관계(曲線關係)가 존재한다는 절충가설(折衷假說)을 지지하는 강력한 증거이다. 그리고 본(本) 연구(硏究)에서 제시된 통제변수(統制變數)인 광고비 정도, 기업규모, 재무레버리지 그리고 30대 재벌 비재벌 가변수(假變數) 등은 기업가치(機業價値)를 결정하는 통계적인 의미를 갖는 변수로 밝혀졌으며 회귀계수(回歸係數)의 부호도 기대하였던 바와 일치하고 있다.

• International Area Studies Review
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• v.20 no.1
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• pp.167-181
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• 2016

In Korea, the transportation & telecommunication service industry is composed of public sector entities, such as public transportation and the postal service, and private sector entities. The public sector may be regulated in terms of pricing or is guaranteed a normal profit. Markup can be a subject for regulation. This paper is to set up the markup equation, estimate the industrial markup, and analyze the markup determinants by estimating the factor price elasticities of markup in the Transportation & Telecommunication industry. The factor price elasticities of markup were estimated to be -0.07 in wage rate, -0.45 in import price index, and -0.13 in interest rates. We suggest that import price and interest rate are major factors to be considered first of all in regulating the transportation & telecommunication industry.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.279-294
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• 2023

It is well known to generate synthetic data sets by the sequential regression multiple imputation (SRMI) method. The R-package synthpop are widely used for generating synthetic data by the SRMI approaches. In this paper, I suggest generating synthetic data based on the probabilistic principal component analysis (PPCA) method. Two simple data sets are used for a simulation study to compare the SRMI and PPCA approaches. Simulation results demonstrate that pairwise coefficients in synthetic data sets by PPCA can be closer to original ones than by SRMI. Furthermore, for the various data types that PPCA applications are well established, such as time series data, the PPCA approach can be extended to generate synthetic data sets.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.187-187
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• 2021

최근 기후 변화로 인한 한반도의 극한 홍수 및 가뭄의 발생 빈도가 급증하고 있다. 이와 같은 이상 기후의 발생으로 연간 강수량의 편차가 더 커지면서 장래 물 부족 문제가 더욱 심화될 전망이다. 따라서 본 연구에서는 한강 유역의 수문 모형 구축을 통해 미래 물수지 변화를 정량적으로 예측함으로써 기후 변화가 한강 유역의 수문순환요소와 수자원에 미치는 영향을 분석하고 평가하였다. 레이더 강우 시계열 자료의 활용을 위하여 격자 단위로 분석이 가능한 분포형 수문 모형 VIC (Variable Infiltration Capacity)을 사용하였고 모형 구축에는 레이더 강우와 종관기상관측소의 강우 자료에 조건부 합성 기법(Conditional Merging, CM)을 적용하여 보정된 강우 자료를 사용하였다. 제공되는 VIC의 토양, 식생 변수는 공간 해상도가 너무 낮고 유역 내에서도 공간적인 변동성이 크지 않아 침투에 관련된 매개변수를 토지 피복과 연관 지어 격자마다 적정한 매개변수를 회귀 분석하여 새로 산정하였다. 먼저 보정 기간 6년(2009-2014)에 대해 보정을 하고 이후 검증 기간 6년(2014-2019)에 대해 검증을 진행하였다. 보정, 검증 기간에 대한 NSE 값은 각각 0.968, 0.569로 양호한 결과를 보여주었다. 구축된 수문 모형에 기후 변화를 고려하기 위해 GCM 기반의 CMIP6 미래기상자료를 입력해서 미래의 물 수지와 수문순환요소의 변화를 모의하였고 모의 결과, 미래 기간에는 강수의 편차가 더욱 심화되면서 가뭄이 더 빈번하게 발생하는 경향을 보였다.

• Korean Journal of Construction Engineering and Management
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• v.14 no.6
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• pp.70-77
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• 2013

Construction industry is broadly agreed as one of the most important sectors of any economy around the world. In this paper, time series data of Korean construction industry and Korean economy are examined. The Bon's proposition will be inspected for Korean context using both cross-sectional and longitudinal analyses. The analysis of the longer than four decades national account statistic of the Korean macro economy verify Bon's proposition of an inverted U-shaped relationships. The verified U-shaped relationships for Korean context exist not only in terms of the construction share in total GDP but also in terms of total construction volume as an economy develops from LDC to NIC and then to AIC eventually with time. The results of the thesis show that the contribution towards the macro economy has varied across different stages of development.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.