• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.829-837
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• 2011

Since Harvey (1989), many approaches for applying unobserved components (UC) models to both univariate and multivariate time series analysis have been developed. However, practitioners still tend to use traditional methods such as exponential smoothing or ARIMA models for modeling and predicting time series data. It is well known that the UC model combines the flexibility of ARIMA models and the easy interpretability of exponential smoothing models by using unobserved components such as trend, cycle, season, and irregular components. This study reviews the UC model and compares its relative performances with those of the other models in modeling and predicting the real gross domestic products (GDP) in Korea. We conclude that the optimal model is the UC model on basis of root mean squared error.

• 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.

• 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.

• 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 Labour Economics
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• v.34 no.3
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• pp.119-139
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• 2011

Using a establishment-worker matched data, this paper estimates wage differentials between standard and non-standard workers. Unlike previous studies, we estimate a fixed-effect model for the tree-way error-components that control for both unobserved individual heterogeneities and unobserved firm heterogeneities. The estimation results show that standard workers earn 6.5~8.4% mire than non-standard workers. This wage premium is 30~40% of the wage differential estimated from the OLS model. The results implies that a large proportion of the wage differentials between standard and non standard workers can be explained by unobserved firm and individual characteristics.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• Journal of the Korean Regional Science Association
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• v.32 no.2
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• pp.45-59
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• 2016

The purpose of this study is to analyze the static and dynamic change of wage differentials of women with disabilities in the Seoul local labor market. This study attempts to explain the double discrimination mechanism for disabled women and empirically gender discrimination and disability discrimination for them by using Oaxaca-Blinder decomposition analysis. In addition, using Juhn-Murphy-Pierce decomposition analysis. we analyze the wage differentials caused by the changed characteristics of disabled women and structures of discrimination at the Seoul local labor market. Data from the Panel Survey of Employment for the Disabled and Korean Labor and Income Panel Study for two years (2008, 2012) are used. According to the result, wage differentials of disabled women caused by disability discrimination is approximately 55% of total wage discrimination, whereas 45% is caused by gender discrimination during the period. Both observed and unobserved components move in the same direction to narrow wage differentials due to the disability discrimination and gender discrimination. Also the endowments in the Seoul local labor market about the changes of observed and unobserved components contribute more to narrow gender wage differentials, while these endowments widen disability wage differentials.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• Economic Analysis
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• v.27 no.2
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• pp.1-40
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• 2021

Using an unobserved components model that features trend growth as a random walk, we find that GDP trend growth rates had gradually declined from the late 1980s to early 2010s in Korea. To uncover the underlying features of the slowdown, we use trend growth accounting. A major feature appears to be a significant decline in the growth rate of labor productivity. To be specific, the first gradual decline in trend growth, which started in 1988 and continued to 1998, is associated with a drop in TFP measured in labor-augmenting units. This finding is inconsistent with the hypothesis that the slowdown in GDP trend growth can be attributed to the 1997-1998 Korean financial crisis. Sluggish investment growth is behind the second period of the gradual slowdown, from 2002 to 2012.