• Management Science and Financial Engineering
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• v.14 no.2
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• pp.105-118
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• 2008

There are many debates on the topic of the relationship between oil prices and economic growth. Through the repeated processes of conformations and contractions on the subject, two main issues are developed; one is how to define and drive oil shocks from oil prices, and the other is how to specify an econometric model to reflect the asymmetric relations between oil prices and output growth. The study, thus, introduces the unobserved component model to pick up the oil shocks and a first-order Markov switching model to reflect the asymmetric features. We finally employ unique oil shock variables from the stochastic trend components of oil prices and adapt four lags of the mean growth Markov Switching model. The results indicate that oil shocks exert more impact to recessionary state than expansionary state and the supply-side oil shocks are more persistent and significant than the demand-side shocks.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.220-227
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• 2014

This paper introduces and applies the World Bank's methodology for constructing composite index or aggregating indicators. After recalculating the world competitiveness index of IMD using Unobserved Component Model(UCM) we compare it with the existing index and try to find some implications. We also try to construct the composite index for measuring the performance of local finance. We employ the Principal Component Analysis(PCA) for validating the appropriateness of selected indicators used in making the composite index. We found that the UCM and PCA are very useful and will be used widely in various evaluations such as regional development, local finance, local competitiveness and public enterprise, etc.

• International Area Studies Review
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• v.21 no.4
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• pp.159-172
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• 2017

In a typical trend-cycle decomposition of GDP, the trend component is usually assumed to follow a random walk process. This paper considers an ARIMA trend and assesses the validity of the ARIMA trend model. I construct univariate and bivariate unobserved-components(UC) models, allowing the ARIMA trend. Estimation results using U.S. data are favorable to the ARIMA trend models. I, also, compare the forecasting performance of the UC models. Dynamic pseudo-out-of-sample forecasting exercises are implemented with recursive estimations. I find that the bivariate model outperforms the univariate model, the smoothed estimates of trend and cycle components deliver smaller forecasting errors compared to the filtered estimates, and, most importantly, allowing for the ARIMA trend can lead to statistically significant gains in forecast accuracy, providing support for the ARIMA trend model. It is worthy of notice that trend shocks play the main source of the output fluctuation if the ARIMA trend is allowed in the UC model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.153-159
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• 2019

Job creation is the most important issue in the labor market these days, and the quality of jobs is also very important in order to resolve the mismatches that are taking place in the labor market. Kim Young-min (2014) developed the "2012 Quality of Employment Index" with twenty indicators in seven categories, including employment opportunities, to objectively assess the local labor market. This method presents the concept of the aggregate indicator, 'Quality of Work Index', and has the advantage of being easy to produce. However, it is difficult to statistically verify the adequacy of the constitutive indicators and, based on this, make them a single aggregate index through statistical techniques. Therefore, we developed an alternative '2012 Descent Work Index' and a confidence interval using Principal Component Analysis(PCA) and Unobserved Component Model(UCM) presented by Gi-Choon Kang & Myung-jig Kim (2014) and also calculated an alternative '2017 Descent Work Index' using the first half of 2017 local area labour force survey and compared its changes by region. The results of the empirical analysis show that the rank correlation coefficient between two methods of aggregating indicators, simple weight used in Young-min Kim's research, PCA method and UCM used in this study, were found to be statistically significant under 5% significance level. This implies that all methods are found to be useful. However, the PCA and UCM which determine scientific and objective weights based on data are preferred to Young-min Kim's approach. Since it provides us not only the level of aggregate indicator but also its confidence intervals, it is possible to compare ranking with the consideration of statistical significance. Therefore, it is expected that the method of constructing an aggregating indicator using UCM will be widely used in many areas in the future.

• Journal of Labour Economics
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• v.41 no.3
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• pp.95-128
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• 2018

This study tries to measure the change in the state dependence of the three labor supply choices (part-time, full-time, and the state of unemployed) in Korean married women's labor market by estimating the dynamic multinomial logit model based on MSL (maximum simulated likelihood) method. A component representing individual's unobserved characteristics has been introduced, because it is crucial to control for unobserved heterogeneity in assessing the state dependence. Estimation results show that the state dependences of the three alternatives have strengthened recently. Therefore, part-time job has become more likely to be functioning as an extra option to participate in labor market rather than a bridge(stepping stone) or shelter between unemployment and full-time job.

• Land and Housing Review
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• v.11 no.3
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• pp.33-37
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• 2020

The aim of this paper is to scrutinize the relation between Business Survey Index and Industrial Production Index in construction industry, stated in another way, the relation between CEO's expectations of future business status and real business activity in construction industry. Previous papers on this research area have been examined the relation between released BSI and released IPI. However, this paper focuses 'the relation between released BSI and the long-run component of IPI' and 'the relation between released BSI and the short-run component of IPI'. The first step is to decompose released IPI by unobserved component model. The long-run component of IPI is set up as a random walk process. And short-run component is set up as a stationary AR(1) process. The findings are as follows. First, released BSI Granger causes unidirectionally released IPI. Second, there exists one-way Granger causality from released BSI to long-run component of IPI. Third, Granger causality does not exist between released BSI and 'short-run component of IPI'. BSI increases IPI in the second or third month. These findings of this paper mean that CEO's expectations may influence industrial production in construction industry.

• KDI Journal of Economic Policy
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• v.26 no.2
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• pp.3-62
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• 2004

This paper estimates Korea's natural rate of unemployment using various estimation methods such as pure time-series methods, reduced-form methods, and structural form methods, with discussion about relative advantages and disadvantages of each estimation method. This paper also provides the confidence interval of the estimated natural rate of unemployment by the Monte Carlo integration method. Though multivariate unobserved component model exhibits better performance in many aspects than other estimation methods, awareness should be raised for a potential misspecification problem of a multivariate unobserved component model. Considering that each method has its own advantages and disadvantages, it is recommended to make an inference on the natural rate of unemployment based on common results among various methods. Korea's natural rate of unemployment was estimated to be around 3.8~4.0% on average in the period of 1979:I~1987:IV, and to decline to 2.5~2.9% in the period of 1988:I~1997:IV. During the Asian crisis, it is estimated to peak at near 4.8% and to have been on a downward trend since then.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.583-583
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• 2016

다양한 평가지표가 반영된 복합 지수(Composite Index)는 물관리 정책의 우선순위 결정 및 정책성과의 모니터링에 유용한 도구로 사용되고 있다. 각 지표별 중요도를 나타내는 가중치는 최종 지수의 산정에 영향을 미칠 수 있으며, 그 결정방법도 Data Envelopment Analysis(DEA), Benefit of doubt Approach(BOD), Unobserved Component Model(UCM), Budget Allocation Process(BAP), Analytic Hierarchy Process(AHP), Conjoint Analysis(CA) 등 다양하다. 본 연구에서는 여러 가지 가중치 결정방법 중 통계적 방법인 주성분 분석(Principal Component Analysis, PCA)을 사용하여 Park et al.(2016)이 제시한 물관리 탄력성 지수(Water Management Resilience Index, WMRI)에 대한 가중치를 산정하여 동일 가중치를 적용한 기존 결과와 비교하였다. 물관리 탄력성 지수는 자연조건상 물관리 취약성(Vulnerability), 기존 수자원 인프라의 견고성(Robustness), 물위기 적응전략의 다양성(Redundancy)의 3가지 부지수(sub-index)는 각각 13개, 11개, 7개의 지표(Indicator)로 구성되어 있으며, 117개 중권역을 다목적댐 하류 본류유역(범주 1), 용수공급 및 유량조절이 불가능한 지류(범주 2)와 가능한 지류(범주 3)로 분류하여 적용되었다. 각 부지수별로 추출된 3개, 5개, 3개의 주성분이 전체 자료의 76.4%, 71.2%, 63.2%를 설명하는 것으로 분석되었으며 부지수별 주성분의 고유벡터(Eigenvector)와 고유값(Eigenvalue)를 계산하고 각 지표의 가중치를 산정하였다. 주성분 분석에 의한 가중치와 동일 가중치를 적용하였을 경우와 비교해보면 취약성 부지수 1.9%, 견고성 부지수 1.9%, 다양성 부지수 2.1%의 차이가 나타나며 물관리 탄력성 지수는 0.4%의 차이를 보임에 따라 Park et al.이 제시한 연구결과의 적정성을 확인할 수 있었다. 주성분 분석은 객관적인 가중치 설정을 위한 통계적 접근방법의 하나로써 다양한 물관리 정책지수 산정시 활용될 수 있을 것이며, 향후 다른 가중치 산정방법을 적용함으로써 각 방법에 따른 지수 결과의 민감도 및 장단점을 분석할 수 있을 것으로 판단된다.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.