서해 어획대상 잠재생산량 추정을 위한 자원평가모델의 비교 분석

최민제,김도훈,최지훈 한국수산해양기술학회 2019 수산해양기술연구 Vol.55 No.3

This study is aimed to compare stock assessment models depending on how the models fit to observed data. Process-error model, Observation-error model, and Bayesian state-space model for the Korean Western coast fisheries were applied for comparison. Analytical results show that there is the least error between the estimated CPUE and the observed CPUE with the Bayesian state-space model; consequently, results of the Bayesian state-space model are the most reliable. According to the Bayesian State-space model, potential yield of fishery resources in the West Sea of Korea is estimated to be 231,949 tons per year. However, the results show that the fishery resources of West Sea have been decreasing since 1967. In addition, the amounts of stock in 2013 are assessed to be only 36% of the stock biomass at MSY level. Therefore, policy efforts are needed to recover the fishery resources of West Sea of Korea. 본 연구에서는 서해 어획대상의 잠재생산량 추정에 가장 적합한 자원평가모델을 비교․분석하고, 해당 모델을 바탕으로 서해 어획대상의 자원상황을 파악하고자 하였다. 자원평가모델 선정을 위해서 과정오차모델, 관측오차모델, 그리고 Bayesian State-space 모델의 평가결과를 비교하였다. 분석 결과, Bayesian State-space 모델을 통해 추정된 연도별 단위노력당어획량이 실제 단위노력당어획량 자료를 가장 잘 반영하는 것으로 추정되었다. 따라서 분석 모델 중 Bayesian State-space 모델이 서해 어획대상의 잠재생산량 추정에 가장 적합한 것으로 평가되었다. Bayesian State-space 모델을 이용한 서해 어획대상 잠재생산량 추정 결과, 서해 어획대상의 잠재생산량은 231,949톤으로 나타났다. 그리고 서해 수산자원량의 경우 1967년 이후 지속적으로 감소한 것으로 나타났으며, 특히 2013년의 자원량은 최대지속적생산을 위한 자원량 수준의 약 36%에 불과한 것으로 추정되었다.

상태공간모형에 의한 공주지점의 T-N농도의 시계열예측

류병로 ( Ryu Byong-ro ),한양수 ( Han Yang-su ) 한국환경기술학회 2000 한국환경기술학회지 Vol.1 No.1

本 硏究는 公州地點의 水質(T-N)과 公州地点을 排水區域으로 하는 汚染排出負荷量(T-N) 公州地點의 流量, 公州地點의 直上流인 燕岐地點의 水質(T-N) 變數들간의 動的關聯性을 살펴보고 어떤 互相作用(feed back interaction)도 可能할 수 있는 模型과 이 時系列데이터를 가장 잘 適合 시키고 豫測할 수 있는 最適의 狀態空間模型을 構築하는데 그 目的이 있다. 그리고 河川水質에 影響을 미치는 汚染發生 排出負荷量(T-N)와 汚染源의 移送 및 擴散을 돕는 流量, 公州地點의 水質(T-N) 그리고, 水質因子 起作에 影響을 미치는 水質因子와 水系 上ㆍ下流間의 密接한 關係를 가지므로, 公州地点의 直上流인 燕岐地點의 T-N을 使用하여 狀態空間 模型의 構築과 水質을 豫測하였다. An optimizing method about long and short-term water quality in developed to establish State space model for the accurate hydrologic forecasting using pollution load and discharge data at a site of a river basin Input data of state space model is T-N that is classified with non-point source and point source. The non-point sources of this model are selected by populations, livestock, industries, fish-nursers, visitors and the point source are selected by pollution load according to land use. The major findings of this papers are followings: First, for the P value of VAR(P) model to establish state space theory, it used AIC value By time step and VARMA model were established that it was findings to the constituent Unit of state space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model. second, the results of comparison with computed and observed values in conformity with the rule of state space model were showed a good results and the model were exhibited to high forecasting ability of water quality third, from the simulated results by the state space model, the model are suitable to forecasting the water qualities for similar to simulated and observed results.

Spurious Mean-Reversion of Stock Prices in the State-Space Model

최원혁(Won Hyeok Choi),전덕빈(Duk Bin Jun),김동수(Dong Soo Kim),노재선(Jaesun Noh) 한국경영과학회 2011 韓國經營科學會誌 Vol.36 No.1

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.

붉은대게(Chinonoecetes japonicus) 자원평가를 위한 잉여생산량모델의 비교 분석

최지훈 ( Ji-hoon Choi ),김도훈 ( Do-hoon Kim ),오택윤 ( Taeg-yun Oh ),서영일 ( Young Il Seo ),강희중 ( Hee Joong Kang ) 한국수산과학회(구 한국수산학회) 2020 한국수산과학회지 Vol.53 No.6

This study is aimed to compare stock assessment models which are effective in assessing red snow crab Chinonoecetes japonicus resources and to select and apply an effective stock assessment model in the future. In order to select an effective stock assessment model, a process-error model, observation-error model, and a Bayesian state-space model were estimated. Analytical results show that the least error is observed between the estimated CPUE (catch per unit effort) and the observed CPUE when using the Bayesian state-space model. For the Bayesian state-space model, the 95% credible interval(CI) ranges for the maximum sustainable yield (MSY), carrying capacity (K), catchability coefficient (q), and intrinsic growth (r) are estimated to be 10,420-47,200 tons, 185,200-444,800 tons, 3.81E-06-9.02E-06, and 0.14-0.66, respectively. The results show that the Bayesian state-space model was most reliable among models.

상태공간모형을 이용한 신흥국 채권수익률 스프레드 분석

김병준 ( Byoung Joon Kim ),윤영섭 ( Young Sup Yun ) 아시아.유럽미래학회 2010 유라시아연구 Vol.7 No.1

본 연구에서는 신흥국가의 채권수익률 스프레드의 변화에 대한 동태적 과정을 분석한다. 한국가의 채권수익률은 그 나라 고유의 경제상황과 글로벌 충격요인들에 의하여 결정되며 이때 이 채권수 익률의 변동은 경제적 요인에 의해 결정되는 내재가치(fundamental value) 부분과 투자자들의 심리적 요인에 따라 변동하는 일시적 가치(transient value) 부분의 합으로 구성된다고 볼 수 있다. 본 연구에서는 신흥시장에서의 채권수익률과 미국 국채수익률을 대용치로 하는 무위험수익률과의 차이로 추정하는 신흥시장 스프레드에 대한 변동을 상태공간모형(state space model)을 사용하여 내재가치와 일시가치로 분해하여 살펴봄으로써 신흥시장 스프레드의 변동이 경제적 요인에 부합되는지 여부를 판별해 보고자 한다. 신흥시장의 채권수익률로는 JP Morgan Chase에서 제공하는 각 신흥국별 주별 EMBIGS (Emerging market global bond index spread) 자료를 사용하며 상태공간모형은 Kalman filter 기법을 사용한다. 이를 위하여 EMBIGS 자료에 대한 시계열적 자기상관관계의 검정과 단위근 검정을 사전적으로 실시하여 상태공간모형 사용상의 정당성을 확보하고 상태공간모형으로부터 도출된 수익률 스프레드 격차(Yield Spread Difference: YSD)에 대한 내재가치와 관측된 YSD를 상호 비교함으로써 신흥국의 YSD가 과잉반응으로 유발되는지를 평가해 보기로 한다. 또한 이러한 과잉반응의 강건성 검정을 위하여 별도로 T-GARCH(Threshold Generalized AutoRegressive Conditional Heteroskedasticity) 모형에서 도출되는 YSD의 변동성을 비교해 보기로 한다. 본 연구에서 채택한 신흥국가는 라틴아메리카 6개국, 유럽 4개국, 아시아 5개국 등 총 15개국을 대상으로 선정하였으며 표본기간은 주별 스프레드를 기준으로 1998년 4월~2008년 12월까지의 559주를 선택하였다. 스프레드 도출을 위한 범세계적 기준 수익률로는 미국의 10년 만기 재무성증권(treasury bond: TB) 유통 수익률을 사용하였다. 본 연구에서 행한 두 가지 추정모형에 대한 결과를 요약하면 다음과 같다. 첫째, 신흥국 스프레드의 변화는 내재가치보다는 심리적 가치인 일시가치에 의하여 주로 변화과정을 겪는 것으로 분석되었다. 이는 내재가치가 지속적 성격을 갖는다는 가정 하에 설정한 상태공간모형에 따른 것으로 분석 결과 내재가치는 장기적으로 거의 확정적(deterministic) 추세를 따르는 반면, 일시가치는 시계열의 안정성이 확보된 상태에서도 변동성의 주요인으로 나타났다. 둘째, 이러한 내재가치의 변동은 T-GARCH 모형에 의하여 분석한 수익률 변동의 비대칭적 변화에 대하여 거의 영향을 받지 않는 것으로 나타난 반면, 수익률 관측치에 대한 변동성은 비대칭적 변화에 대하여 두드러진 반응을 가져오는 것으로 분석되어 신흥시장에서의 수익률 변동이 주로 투자자들의 심리적 요인이 반영된 일시가치에 의하여 이루어지는 과잉반응의 존재를 확인하였다. 셋째, 이러한 과잉반응은 글로벌 충격요인들과 국가고유의 충격요인들을 동시에 감안한 다중회귀분석에 서도 지속적으로 유효한 것으로 나타나 신흥시장에서의 채권수익률의 변동이 과도한 것으로 분석됨으로써 과잉반응을 사전적으로 억제할 수 있는 정책적 필요성이 제기되었다. 이와 같은 결과는 내재가치에 의한 채권수익률 변동보다는 심리적 요인이 반영된 일시가치에 의한 채권 수익률 변동이 신흥시장에서의 주요한 채권시장 변동성을 설명한다는 기존의 연구결과들과 부합되는 것으로 볼 수 있다. 한편, 본 연구의 표본대상 기간에는 1998년 러시아 모라토리엄, 2002년 남미 외환위기, 2008년 전 세계적 금융위기 등 세 차례의 충격국면이 포함된 관계로 이러한 내재가치에 의한 변동이 과소 추정되었을 가능성도 제기될 수 있다. 향후로는 표본 시계열을 위기국면에 따라 재편성하거나 구조적 전환(structural break)을 탐지할 수 있는 확장된 칼만필터(extended Kalman filter) 기법에 의한 보다 정밀한 분석도 요구된다고 하겠다. 이는 추후의 연구과제로 남겨 둔다. This study analyzes the dynamic process of yield spread differences in 15 emerging countries’ bond markets by using alternately the state-space model and the threshold-GARCH (T-GARCH) model. Using the discrete Kalman filter technique, we decompose yield spread differences into fundamental and transient components in the adoption of the first order state-space model estimation. We then further decide whether spread changes coincide with both global and country-specific explanatory factors by adoption of T-GARCH estimation after extracting the fundamental values from the yield spread observations. This procedure can be taken by comparing the T-GARCH estimation results of the observed and the derived fundamental values from the state-space model. The existence of overreaction by investors’ psychological situation and also its magnitude can be checked in the emerging bond markets if the conditional volatility of observed spreads turned out to be significantly positive upon arrival of unanticipated negative shocks, whereas that of the fundamental one turned out not to be significant. In this analysis, EMBIGS (emerging bond market global index spread) from JP Morgan Chase are used as data for country yield spreads. For weekly data in use, the sample period covers the April 1998 to December 2008 period and the sample emerging countries cover 6 from Latin America, 4 from Europe and 5 from Asia. Among them, Korea and Thailand have data only up to Aril 2004 and March 2005, respectively, at which points they were excluded from the emerging market status. Major findings of this study are as follows. First, yield spread differences in emerging markets are mainly altered by their transient values rather than by their fundamental values, as the empirical results show that the fundamental values are almost deterministic in the long run and the transient values explain most of changes in spread. This is because three different crises such as the Russian moratorium in 1998, the Argentinean currency crisis in 2002 and the global financial crisis in late 2008 which all occurred during the sample period can be absorbed into the transient value process, whereas the fundamental values remained stable. This can be a meaningful evidence in that real spread changes reveal in the state of overreaction, especially in the bearish trend of the emerging bond markets during the sample period.. Second, in the procedure of T-GARCH estimation, clear evidence of overreaction in observed yield spread differences is found such that the impact on the conditional variance of the unexpected rise in observed spreads is significantly positive, whereas that for the unexpected rise in fundamental spreads does not show any significance. This evidence can be interpreted as that investors’ psychological attitude result from their on-going noise trading.. Third, in the estimation of multiple regressions in the T-GARCH model where two global factors, US T-bond rate and VIX, and two country-specific factors, domestic stock index return and US dollar based foreign exchange rate, are added as explanatory variables for the changes of spread, the same evidence of overreaction as in the above simple T-GARCH model is found. All the four estimators appear to coincide with the theoretical hypotheses in estimating the observed yield spreads. However, in the estimation of the fundamental yield spreads, most of the coefficients on the explanatory variables turn out to be insignificant. This may be partly because our model could not catch the structural break such as the October Crisis of 1987 in the US, using only a first order state-space model with a discrete Kalman filter technique. A more precise estimation technique such as the extended Kalman filter in the nonlinear estimation model need to be used to overcome this weakness, which is left for future research.

State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations

Guoxu Wang,Jie Wu,Bifan Zeng,Zhibin Xu,Wanqiang Wu,Xiaoqian Ma 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.1

A well-performed core power control to track load changes is crucial in pressurized waterreactor (PWR) nuclear power stations. It is challenging to keep the core power stable at thedesired value within acceptable error bands for the safety demands of the PWR due to thesensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC)method was applied to the control of the core power. The model for core power control wasbased on mathematical models of the reactor core, the MPC model, and quadratic programming(QP). The mathematical models of the reactor core were based on neutron dynamicmodels, thermal hydraulic models, and reactivity models. The MPC model waspresented in state-space model form, and QP was introduced for optimization solutionunder system constraints. Simulations of the proposed state-space MPC control system inPWR were designed for control performance analysis, and the simulation results manifestthe effectiveness and the good performance of the proposed control method for core powercontrol.

박용 엔진의 유한요소 모드해석을 통한 상태 공간 모델 개발

이원창(W. C. Lee),김성열(S. R. Kim),안병수(B. S. Ahn),최헌오(H. O. Choi),김재실(C. S. Kim) 한국정밀공학회 2006 한국정밀공학회 학술발표대회 논문집 Vol.2006 No.5월

This article provides a dynamic analysis model for huge marine engine that examined analytically variation effects of frequency response by fitting of transverse stays such as hydraulic type. First, vibration analysis using the three dimensional finite element models for the huge marine engine has performed in order to find out the dynamic characteristics. Second, three dimensional finite elements model for the huge marine engine was modifued so that generate forcing nodes in crosshead part and top bracing nodes in cylinder frame part. Third, a system matrix and output matrix was derived for the general siso(single input single out) state space model. Finally, developed state space model for the three dimensional finite elements model for the huge marine engine without the additional modifying process.

효과적인 자원평가모델 선정을 위한 잉여생산량모델의 비교 분석: 동해 생태계의 잠재생산량 분석을 대상으로

최민제,김도훈 한국해양과학기술원 2019 Ocean and Polar Research Vol.41 No.3

This study sought to find which model is most appropriate for estimating potential yield in the East Sea, Republic of Korea. For comparison purposes, the Process-error model, ASPIC model, Maximum entropy model, Observation-error model, and Bayesian state-space model were applied using data from catch amounts and total efforts of the whole catchable fishes in the East Sea. Results showed that the Bayesian state-space model was estimated to be the most reliable among the models. Potential yield of catchable species was estimated to be 227,858 tons per year. In addition, it was analyzed that the amount of fishery resources in 2016 was about 63% of the biomass that enables a fish stock to deliver the maximum sustainable yield.

베이지안 State-space 모델을 이용한 눈볼대 자원평가 및 관리방안

최지훈,김도훈,최민제,강희중,서영일,이재봉 한국수산해양기술학회(구 한국어업기술학회) 2019 수산해양기술연구 Vol.55 No.2

This study is aimed to take a stock assessment of blackthroat seaperch Doederleinia seaperch regarding the fishing effort of large-powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery. For the assessment, the state-space model was implemented and the standardized catch per unit effort (CPUE) of large powered Danish Seine Fishery and Southwest Sea Danish Seine Fishery which is necessary for the model was estimated with generalized linear model (GLM). The model was adequate for stock assessment because its r-square value was 0.99 and root mean square error (RMSE) value was 0.003. According to the model with 95% confidence interval, maximum sustainable yield (MSY) of Blackthroat seaperch is from 2,634 to 6,765 ton and carrying capacity (K) is between 33,180 and 62,820. Also, the catchability coefficient (q) is between 2.14E-06 and 3.95E-06 and intrinsic growth rate (r) is between 0.31 and 0.72.

A Bayesian state-space production model for Korean chub mackerel (Scomber japonicus) stock

Jung, Yuri,Seo, Young Il,Hyun, Saang-Yoon The Korean Society of Fisheries and Aquatic Scienc 2021 Fisheries and Aquatic Sciences Vol.24 No.4

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.