Estimation of Seasonal Cointegration under Conditional Heteroskedasticity

Seong, Byeongchan The Korean Statistical Society 2015 Communications for statistical applications and me Vol.22 No.6

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

Cointegration Analysis with Mixed-Frequency Data of Quarterly GDP and Monthly Coincident Indicators

Seong, Byeongchan 한국통계학회 2012 응용통계연구 Vol.25 No.6

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

A Comment to “Dickey-Fuller Test for an Extended MA Model”

Byeongchan Seong 계명대학교 자연과학연구소 2019 Quantitative Bio-Science Vol.38 No.1

Characterization of lithium metal oxide layer removal reactions with laser cleaning technology

Byeongchan Kim(김병찬),Jaegeum shin(신재근),Mincheoul Seong(성민철),Myeong Ho Park(박명호),Haeun Kim(김하은),Dongkyung Lee(이동경) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11

Grouping stocks using dynamic linear models

Sihyeon, Kim,Byeongchan, Seong The Korean Statistical Society 2022 Communications for statistical applications and me Vol.29 No.6

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

구조적 오차수정모형을 이용한 한국노동시장 자료분석

성병찬,정효상,Seong, Byeongchan,Jung, Hyosang 한국통계학회 2013 응용통계연구 Vol.26 No.6

본 논문에서는, 구조적 오차수정모형을 한국의 노동시장 자료에 적용함으로써, 실업률에 미치는 구조적 충격의 영향을 분석한다. 이를 위하여 기술력, 노동수요, 노동공급, 임금 부문에서의 충격을 정의하였으며, 이를 각각 노동생산성, 취업자 수, 실업률, 실질임금과 연결하였다. 그 결과로서, 노동수요 및 노동공급 충격이 각각 장기적 및 단기적으로 실업률에 유의한 영향을 미치는 것으로 나타났다. We use a structural vector error correction model of the labor market to investigate the effect of shocks to Korean unemployment. We associate technology, labor demand, labor supply, and wage-setting shocks with equations for productivity, employment, unemployment, and real wages, respectively. Subsequently, labor demand and supply shocks have significant long-run and contemporaneous effects on unemployment, respectively.

Forecasting with a combined model of ETS and ARIMA

Jiu Oh,Byeongchan Seong 한국통계학회 2024 Communications for statistical applications and me Vol.31 No.1

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the dierenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

국면전환 GARCH 모형을 이용한 코스피 변동성 분석

허진영,성병찬,Huh, Jinyoung,Seong, Byeongchan 한국통계학회 2015 응용통계연구 Vol.28 No.3

변동성(volatility)은 투자위험을 의미하며 자산의 가격결정이나 포트폴리오 관리 및 투자전략에서 아주 중요한 역할을 한다. 이러한 변동성을 모형화하기 위한 조건부 이분산 모형으로서 전통적인 GARCH(generalized autoregressive conditional heteroskedastic) 모형 및 확장된 형태들이 널리 사용되어지고 있으나, 금융위기와 재정위기와 같은 구조적 변화를 변동성 예측에 반영할 수 없다는 단점을 가지고 있다. 본 논문에서는 이를 극복하기 위한 모형으로서 국면전환 GARCH(Markov regime switching GARCH) 모형을 소개하고, 한국의 일별 KOSPI 수익률에 적용하여 변동성 분석 및 예측을 실시하고, 기존의 GARCH 모형들과 비교하여 그 성능을 평가한다. 그 결과 표본 내(in-sample)의 변동성 적합도 측면에서 국면전환 GARCH 모형이 가장 우수한 성능을 보였으며, 표본 외(out-of-sample) 예측력 측면에서는 국면전환 GARCH 모형이 단기적 예측에서 좋지 않은 성능을 보였으나 장기적 예측에서 우수함을 보였다. Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

모수 절약 주기적 자기회귀 모형에 관한 연구

이지호,성병찬,Lee, Jiho,Seong, Byeongchan 한국통계학회 2016 응용통계연구 Vol.29 No.1

본 논문에서는 주기적 자기회귀(periodic autoregressive) 모형에서 모수의 수를 줄이기 위한 모수 절약 주기적 자기회귀 모형을 연구하였다. 제안된 모수 절약 모형은 실증분석에서 실업률을 이용하여 기존의 계절 시계열 모형과 비교를 통하여 그 성능을 평가하였다. 모수 절약 구조를 부여하기 위하여 계절성에서 공통된 패턴을 찾아내는 방법을 사용하였으며 기존 주기적 자기회귀 모형과의 통계적 차이 유무는 LR 검정을 통해 확인하였다. 그 결과, 중장기적으로 주기적 자기회귀 모형이 기존의 계절시계열 모형보다 우수한 예측성능을 보였으며, 특히 모수 절약 주기적 자기 회귀 모형의 사용은 기존의 주기적 자기회귀 모형보다 우수한 예측성능을 나타내는 것을 확인하였다. This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.

코로나-19에 따른 서울시 생활인구 변화와 동별 반응 차이 분석

진주혜,성병찬,Jin, Juhae,Seong, Byeongchan 한국통계학회 2020 응용통계연구 Vol.33 No.6

New infectious diseases have broken out repeatedly across the world over the last 20 years; COVID-19 is causing drastic changes and damage to daily lives. Furthermore, as there is no denying that new epidemics will appear in the future, there is a continuous need to develop measures aimed towards responding to economic damage. Against this backdrop, the living population is an important indicator that shows changes in citizens' life patterns. This study analyzes time-based and socio-environmental characteristics by detecting and classifying changes in everyday life caused by COVID-19 from the perspective of the floating population. k-shape Clustering is used to classify living population data of each of the 424 dong's in Seoul measured by the hour; then by applying intervention analysis and One-way ANOVA, each cluster's characteristics and aspects of change in the living population occurring in the aftermath of COVID-19 are scrutinized. In conclusion, this study confirms each cluster's obvious characteristics in changes of population flows before and after the confirmation of coronavirus patients and distinguishes groups that reacted sensitively to the intervention times on the basis of COVID-related incidents from those that did not. 최근 20년간 세계적으로 새로운 전염병이 반복해서 등장해왔으며 코로나-19에 들어서는 일상에까지 큰 변화와 피해를 주고 있다. 이에 더해 앞으로도 새로운 전염병의 등장을 간과할 수 없게 되면서 경제 타격에 대응하기 위한 정책 발굴이 지속적으로 요구되고 있다. 이러한 상황에서 생활인구는 시민들의 생활 패턴 변화를 드러내는 중요한 지표이다. 본 논문에서는 코로나-19에 의한 일상의 변화를 유동인구 관점에서 감지 및 분류하여 시간적 및 사회환경적 특징을 분석한다. 시간 단위로 측정된 서울시 424개 행정동별 생활인구 데이터를 분류하기 위해 k-shape clustering을 사용하였고, 이후에는 각 군집에 개입분석, One-way ANOVA 등을 적용하여 코로나-19 진행 여파에 따른 군집별 특성 및 생활인구 변화 양상을 자세히 살펴보았다. 결론적으로 국내 코로나 환자 발생 전후의 인구 유출입 변동에 있어 각 군집별로 뚜렷한 특징을 확인하였으며, 코로나-19 관련 사건을 바탕으로 지정한 개입 시점에 대해서도 민감하게 반응하는 군집과 그렇지 않은 군집을 구분할 수 있었다.